A Model for Developing Clinical Analytics Capacity: Closing the Loops on Outcomes to Optimize Quality.

PubMed

Eggert, Corinne; Moselle, Kenneth; Protti, Denis; Sanders, Dale

2017-01-01

Closed Loop Analytics© is receiving growing interest in healthcare as a term referring to information technology, local data and clinical analytics working together to generate evidence for improvement. The Closed Loop Analytics model consists of three loops corresponding to the decision-making levels of an organization and the associated data within each loop - Patients, Protocols, and Populations. The authors propose that each of these levels should utilize the same ecosystem of electronic health record (EHR) and enterprise data warehouse (EDW) enabled data, in a closed-loop fashion, with that data being repackaged and delivered to suit the analytic and decision support needs of each level, in support of better outcomes.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions.

PubMed

White, B J; Amrine, D E; Larson, R L

2018-04-14

Big data are frequently used in many facets of business and agronomy to enhance knowledge needed to improve operational decisions. Livestock operations collect data of sufficient quantity to perform predictive analytics. Predictive analytics can be defined as a methodology and suite of data evaluation techniques to generate a prediction for specific target outcomes. The objective of this manuscript is to describe the process of using big data and the predictive analytic framework to create tools to drive decisions in livestock production, health, and welfare. The predictive analytic process involves selecting a target variable, managing the data, partitioning the data, then creating algorithms, refining algorithms, and finally comparing accuracy of the created classifiers. The partitioning of the datasets allows model building and refining to occur prior to testing the predictive accuracy of the model with naive data to evaluate overall accuracy. Many different classification algorithms are available for predictive use and testing multiple algorithms can lead to optimal results. Application of a systematic process for predictive analytics using data that is currently collected or that could be collected on livestock operations will facilitate precision animal management through enhanced livestock operational decisions.

Predictive analytics and child protection: constraints and opportunities.

PubMed

Russell, Jesse

2015-08-01

This paper considers how predictive analytics might inform, assist, and improve decision making in child protection. Predictive analytics represents recent increases in data quantity and data diversity, along with advances in computing technology. While the use of data and statistical modeling is not new to child protection decision making, its use in child protection is experiencing growth, and efforts to leverage predictive analytics for better decision-making in child protection are increasing. Past experiences, constraints and opportunities are reviewed. For predictive analytics to make the most impact on child protection practice and outcomes, it must embrace established criteria of validity, equity, reliability, and usefulness. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Purpose of Analytical Models from the Perspective of a Data Provider.

ERIC Educational Resources Information Center

Sheehan, Bernard S.

The purpose of analytical models is to reduce complex institutional management problems and situations to simpler proportions and compressed time frames so that human skills of decision makers can be brought to bear most effectively. Also, modeling cultivates the art of management by forcing explicit and analytical consideration of important…

Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori.

PubMed

Pasta, D J; Taylor, J L; Henning, J M

1999-01-01

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.

Improving Adolescent Judgment and Decision Making

PubMed Central

Dansereau, Donald F.; Knight, Danica K.; Flynn, Patrick M.

2013-01-01

Human judgment and decision making (JDM) has substantial room for improvement, especially among adolescents. Increased technological and social complexity “ups the ante” for developing impactful JDM interventions and aids. Current explanatory advances in this field emphasize dual processing models that incorporate both experiential and analytic processing systems. According to these models, judgment and decisions based on the experiential system are rapid and stem from automatic reference to previously stored episodes. Those based on the analytic system are viewed as slower and consciously developed. These models also hypothesize that metacognitive (self-monitoring) activities embedded in the analytic system influence how and when the two systems are used. What is not included in these models is the development of an intersection between the two systems. Because such an intersection is strongly suggested by memory and educational research as the basis of wisdom/expertise, the present paper describes an Integrated Judgment and Decision-Making Model (IJDM) that incorporates this component. Wisdom/expertise is hypothesized to contain a collection of schematic structures that can emerge from the accumulation of similar episodes or repeated analytic practice. As will be argued, in comparisons to dual system models, the addition of this component provides a broader basis for selecting and designing interventions to improve adolescent JDM. Its development also has implications for generally enhancing cognitive interventions by adopting principles from athletic training to create automated, expert behaviors. PMID:24391350

Decision analytic models for Alzheimer's disease: state of the art and future directions.

PubMed

Cohen, Joshua T; Neumann, Peter J

2008-05-01

Decision analytic policy models for Alzheimer's disease (AD) enable researchers and policy makers to investigate questions about the costs and benefits of a wide range of existing and potential screening, testing, and treatment strategies. Such models permit analysts to compare existing alternatives, explore hypothetical scenarios, and test the strength of underlying assumptions in an explicit, quantitative, and systematic way. Decision analytic models can best be viewed as complementing clinical trials both by filling knowledge gaps not readily addressed by empirical research and by extrapolating beyond the surrogate markers recorded in a trial. We identified and critiqued 13 distinct AD decision analytic policy models published since 1997. Although existing models provide useful insights, they also have a variety of limitations. (1) They generally characterize disease progression in terms of cognitive function and do not account for other distinguishing features, such as behavioral symptoms, functional performance, and the emotional well-being of AD patients and caregivers. (2) Many describe disease progression in terms of a limited number of discrete states, thus constraining the level of detail that can be used to characterize both changes in patient status and the relationships between disease progression and other factors, such as residential status, that influence outcomes of interest. (3) They have focused almost exclusively on evaluating drug treatments, thus neglecting other disease management strategies and combinations of pharmacologic and nonpharmacologic interventions. Future AD models should facilitate more realistic and compelling evaluations of various interventions to address the disease. An improved model will allow decision makers to better characterize the disease, to better assess the costs and benefits of a wide range of potential interventions, and to better evaluate the incremental costs and benefits of specific interventions used in conjunction with other disease management strategies.

Lending Officers' Decisions to Recommend Innovative Agricultural Technology.

ERIC Educational Resources Information Center

McIntosh, Wm. Alex; Zey-Ferrell, Mary

1986-01-01

Path analysis examines an analytical model of decision making by lending officers of 211 Texas banks when recommending agricultural technology to farmer-clients. Model analyzes effects of loan officers' ascribed/achieved personal characteristics and perceptions of organizational constraints during three stages of decision process: using…

The Rational Adolescent: Strategic Information Processing during Decision Making Revealed by Eye Tracking.

PubMed

Kwak, Youngbin; Payne, John W; Cohen, Andrew L; Huettel, Scott A

2015-01-01

Adolescence is often viewed as a time of irrational, risky decision-making - despite adolescents' competence in other cognitive domains. In this study, we examined the strategies used by adolescents (N=30) and young adults (N=47) to resolve complex, multi-outcome economic gambles. Compared to adults, adolescents were more likely to make conservative, loss-minimizing choices consistent with economic models. Eye-tracking data showed that prior to decisions, adolescents acquired more information in a more thorough manner; that is, they engaged in a more analytic processing strategy indicative of trade-offs between decision variables. In contrast, young adults' decisions were more consistent with heuristics that simplified the decision problem, at the expense of analytic precision. Collectively, these results demonstrate a counter-intuitive developmental transition in economic decision making: adolescents' decisions are more consistent with rational-choice models, while young adults more readily engage task-appropriate heuristics.

The Rational Adolescent: Strategic Information Processing during Decision Making Revealed by Eye Tracking

PubMed Central

Kwak, Youngbin; Payne, John W.; Cohen, Andrew L.; Huettel, Scott A.

2015-01-01

Adolescence is often viewed as a time of irrational, risky decision-making – despite adolescents' competence in other cognitive domains. In this study, we examined the strategies used by adolescents (N=30) and young adults (N=47) to resolve complex, multi-outcome economic gambles. Compared to adults, adolescents were more likely to make conservative, loss-minimizing choices consistent with economic models. Eye-tracking data showed that prior to decisions, adolescents acquired more information in a more thorough manner; that is, they engaged in a more analytic processing strategy indicative of trade-offs between decision variables. In contrast, young adults' decisions were more consistent with heuristics that simplified the decision problem, at the expense of analytic precision. Collectively, these results demonstrate a counter-intuitive developmental transition in economic decision making: adolescents' decisions are more consistent with rational-choice models, while young adults more readily engage task-appropriate heuristics. PMID:26388664

Estimating the Health Effects of Greenhouse Gas Mitigation Strategies: Addressing Parametric, Model, and Valuation Challenges

PubMed Central

Hess, Jeremy J.; Ebi, Kristie L.; Markandya, Anil; Balbus, John M.; Wilkinson, Paul; Haines, Andy; Chalabi, Zaid

2014-01-01

Background: Policy decisions regarding climate change mitigation are increasingly incorporating the beneficial and adverse health impacts of greenhouse gas emission reduction strategies. Studies of such co-benefits and co-harms involve modeling approaches requiring a range of analytic decisions that affect the model output. Objective: Our objective was to assess analytic decisions regarding model framework, structure, choice of parameters, and handling of uncertainty when modeling health co-benefits, and to make recommendations for improvements that could increase policy uptake. Methods: We describe the assumptions and analytic decisions underlying models of mitigation co-benefits, examining their effects on modeling outputs, and consider tools for quantifying uncertainty. Discussion: There is considerable variation in approaches to valuation metrics, discounting methods, uncertainty characterization and propagation, and assessment of low-probability/high-impact events. There is also variable inclusion of adverse impacts of mitigation policies, and limited extension of modeling domains to include implementation considerations. Going forward, co-benefits modeling efforts should be carried out in collaboration with policy makers; these efforts should include the full range of positive and negative impacts and critical uncertainties, as well as a range of discount rates, and should explicitly characterize uncertainty. We make recommendations to improve the rigor and consistency of modeling of health co-benefits. Conclusion: Modeling health co-benefits requires systematic consideration of the suitability of model assumptions, of what should be included and excluded from the model framework, and how uncertainty should be treated. Increased attention to these and other analytic decisions has the potential to increase the policy relevance and application of co-benefits modeling studies, potentially helping policy makers to maximize mitigation potential while simultaneously improving health. Citation: Remais JV, Hess JJ, Ebi KL, Markandya A, Balbus JM, Wilkinson P, Haines A, Chalabi Z. 2014. Estimating the health effects of greenhouse gas mitigation strategies: addressing parametric, model, and valuation challenges. Environ Health Perspect 122:447–455; http://dx.doi.org/10.1289/ehp.1306744 PMID:24583270

IBM's Health Analytics and Clinical Decision Support.

PubMed

Kohn, M S; Sun, J; Knoop, S; Shabo, A; Carmeli, B; Sow, D; Syed-Mahmood, T; Rapp, W

2014-08-15

This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.

Beyond Decision Making: Cultural Ideology as Heuristic Paradigmatic Models.

ERIC Educational Resources Information Center

Whitley, L. Darrell

A paradigmatic model of cultural ideology provides a context for understanding the relationship between decision-making and personal and cultural rationality. Cultural rules or heuristics exist which indicate that many decisions can be made on the basis of established strategy rather than continual analytical calculations. When an optimal solution…

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling

PubMed Central

Williams, Claire; Lewsey, James D.; Mackay, Daniel F.; Briggs, Andrew H.

2016-01-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results. PMID:27698003

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.

PubMed

Williams, Claire; Lewsey, James D; Mackay, Daniel F; Briggs, Andrew H

2017-05-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.

Accounting for Uncertainty in Decision Analytic Models Using Rank Preserving Structural Failure Time Modeling: Application to Parametric Survival Models.

PubMed

Bennett, Iain; Paracha, Noman; Abrams, Keith; Ray, Joshua

2018-01-01

Rank Preserving Structural Failure Time models are one of the most commonly used statistical methods to adjust for treatment switching in oncology clinical trials. The method is often applied in a decision analytic model without appropriately accounting for additional uncertainty when determining the allocation of health care resources. The aim of the study is to describe novel approaches to adequately account for uncertainty when using a Rank Preserving Structural Failure Time model in a decision analytic model. Using two examples, we tested and compared the performance of the novel Test-based method with the resampling bootstrap method and with the conventional approach of no adjustment. In the first example, we simulated life expectancy using a simple decision analytic model based on a hypothetical oncology trial with treatment switching. In the second example, we applied the adjustment method on published data when no individual patient data were available. Mean estimates of overall and incremental life expectancy were similar across methods. However, the bootstrapped and test-based estimates consistently produced greater estimates of uncertainty compared with the estimate without any adjustment applied. Similar results were observed when using the test based approach on a published data showing that failing to adjust for uncertainty led to smaller confidence intervals. Both the bootstrapping and test-based approaches provide a solution to appropriately incorporate uncertainty, with the benefit that the latter can implemented by researchers in the absence of individual patient data. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

An analytical framework to assist decision makers in the use of forest ecosystem model predictions

USDA-ARS?s Scientific Manuscript database

The predictions of most terrestrial ecosystem models originate from deterministic simulations. Relatively few uncertainty evaluation exercises in model outputs are performed by either model developers or users. This issue has important consequences for decision makers who rely on models to develop n...

Analytic hierarchy process (AHP) as a tool in asset allocation

NASA Astrophysics Data System (ADS)

Zainol Abidin, Siti Nazifah; Mohd Jaffar, Maheran

2013-04-01

Allocation capital investment into different assets is the best way to balance the risk and reward. This can prevent from losing big amount of money. Thus, the aim of this paper is to help investors in making wise investment decision in asset allocation. This paper proposes modifying and adapting Analytic Hierarchy Process (AHP) model. The AHP model is widely used in various fields of study that are related in decision making. The results of the case studies show that the proposed model can categorize stocks and determine the portion of capital investment. Hence, it can assist investors in decision making process and reduce the risk of loss in stock market investment.

IT vendor selection model by using structural equation model & analytical hierarchy process

NASA Astrophysics Data System (ADS)

Maitra, Sarit; Dominic, P. D. D.

2012-11-01

Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.

Strategic analytics: towards fully embedding evidence in healthcare decision-making.

PubMed

Garay, Jason; Cartagena, Rosario; Esensoy, Ali Vahit; Handa, Kiren; Kane, Eli; Kaw, Neal; Sadat, Somayeh

2015-01-01

Cancer Care Ontario (CCO) has implemented multiple information technology solutions and collected health-system data to support its programs. There is now an opportunity to leverage these data and perform advanced end-to-end analytics that inform decisions around improving health-system performance. In 2014, CCO engaged in an extensive assessment of its current data capacity and capability, with the intent to drive increased use of data for evidence-based decision-making. The breadth and volume of data at CCO uniquely places the organization to contribute to not only system-wide operational reporting, but more advanced modelling of current and future state system management and planning. In 2012, CCO established a strategic analytics practice to assist the agency's programs contextualize and inform key business decisions and to provide support through innovative predictive analytics solutions. This paper describes the organizational structure, services and supporting operations that have enabled progress to date, and discusses the next steps towards the vision of embedding evidence fully into healthcare decision-making. Copyright © 2014 Longwoods Publishing.

Verification of Decision-Analytic Models for Health Economic Evaluations: An Overview.

PubMed

Dasbach, Erik J; Elbasha, Elamin H

2017-07-01

Decision-analytic models for cost-effectiveness analysis are developed in a variety of software packages where the accuracy of the computer code is seldom verified. Although modeling guidelines recommend using state-of-the-art quality assurance and control methods for software engineering to verify models, the fields of pharmacoeconomics and health technology assessment (HTA) have yet to establish and adopt guidance on how to verify health and economic models. The objective of this paper is to introduce to our field the variety of methods the software engineering field uses to verify that software performs as expected. We identify how many of these methods can be incorporated in the development process of decision-analytic models in order to reduce errors and increase transparency. Given the breadth of methods used in software engineering, we recommend a more in-depth initiative to be undertaken (e.g., by an ISPOR-SMDM Task Force) to define the best practices for model verification in our field and to accelerate adoption. Establishing a general guidance for verifying models will benefit the pharmacoeconomics and HTA communities by increasing accuracy of computer programming, transparency, accessibility, sharing, understandability, and trust of models.

IBM’s Health Analytics and Clinical Decision Support

PubMed Central

Sun, J.; Knoop, S.; Shabo, A.; Carmeli, B.; Sow, D.; Syed-Mahmood, T.; Rapp, W.

2014-01-01

Summary Objectives This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Methods Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. Results There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Conclusion Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation. PMID:25123736

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.

PubMed

Tulabandhula, Theja; Rudin, Cynthia

2014-06-01

Our goal is to design a prediction and decision system for real-time use during a professional car race. In designing a knowledge discovery process for racing, we faced several challenges that were overcome only when domain knowledge of racing was carefully infused within statistical modeling techniques. In this article, we describe how we leveraged expert knowledge of the domain to produce a real-time decision system for tire changes within a race. Our forecasts have the potential to impact how racing teams can optimize strategy by making tire-change decisions to benefit their rank position. Our work significantly expands previous research on sports analytics, as it is the only work on analytical methods for within-race prediction and decision making for professional car racing.

Current recommendations on the estimation of transition probabilities in Markov cohort models for use in health care decision-making: a targeted literature review.

PubMed

Olariu, Elena; Cadwell, Kevin K; Hancock, Elizabeth; Trueman, David; Chevrou-Severac, Helene

2017-01-01

Although Markov cohort models represent one of the most common forms of decision-analytic models used in health care decision-making, correct implementation of such models requires reliable estimation of transition probabilities. This study sought to identify consensus statements or guidelines that detail how such transition probability matrices should be estimated. A literature review was performed to identify relevant publications in the following databases: Medline, Embase, the Cochrane Library, and PubMed. Electronic searches were supplemented by manual-searches of health technology assessment (HTA) websites in Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and the UK. One reviewer assessed studies for eligibility. Of the 1,931 citations identified in the electronic searches, no studies met the inclusion criteria for full-text review, and no guidelines on transition probabilities in Markov models were identified. Manual-searching of the websites of HTA agencies identified ten guidelines on economic evaluations (Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and UK). All identified guidelines provided general guidance on how to develop economic models, but none provided guidance on the calculation of transition probabilities. One relevant publication was identified following review of the reference lists of HTA agency guidelines: the International Society for Pharmacoeconomics and Outcomes Research taskforce guidance. This provided limited guidance on the use of rates and probabilities. There is limited formal guidance available on the estimation of transition probabilities for use in decision-analytic models. Given the increasing importance of cost-effectiveness analysis in the decision-making processes of HTA bodies and other medical decision-makers, there is a need for additional guidance to inform a more consistent approach to decision-analytic modeling. Further research should be done to develop more detailed guidelines on the estimation of transition probabilities.

Patient or physician preferences for decision analysis: the prenatal genetic testing decision.

PubMed

Heckerling, P S; Verp, M S; Albert, N

1999-01-01

The choice between amniocentesis and chorionic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by decision-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was significantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; Cl, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; Cl, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085+/-0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospective preference-assessment studies will be necessary to confirm this association.

University Macro Analytic Simulation Model.

ERIC Educational Resources Information Center

Baron, Robert; Gulko, Warren

The University Macro Analytic Simulation System (UMASS) has been designed as a forecasting tool to help university administrators budgeting decisions. Alternative budgeting strategies can be tested on a computer model and then an operational alternative can be selected on the basis of the most desirable projected outcome. UMASS uses readily…

Accelerated bridge construction (ABC) decision making and economic modeling tool.

DOT National Transportation Integrated Search

2011-12-01

In this FHWA-sponsored pool funded study, a set of decision making tools, based on the Analytic Hierarchy Process (AHP) was developed. This tool set is prepared for transportation specialists and decision-makers to determine if ABC is more effective ...

Decision analysis for conservation breeding: Maximizing production for reintroduction of whooping cranes

USGS Publications Warehouse

Smith, Des H.V.; Converse, Sarah J.; Gibson, Keith; Moehrenschlager, Axel; Link, William A.; Olsen, Glenn H.; Maguire, Kelly

2011-01-01

Captive breeding is key to management of severely endangered species, but maximizing captive production can be challenging because of poor knowledge of species breeding biology and the complexity of evaluating different management options. In the face of uncertainty and complexity, decision-analytic approaches can be used to identify optimal management options for maximizing captive production. Building decision-analytic models requires iterations of model conception, data analysis, model building and evaluation, identification of remaining uncertainty, further research and monitoring to reduce uncertainty, and integration of new data into the model. We initiated such a process to maximize captive production of the whooping crane (Grus americana), the world's most endangered crane, which is managed through captive breeding and reintroduction. We collected 15 years of captive breeding data from 3 institutions and used Bayesian analysis and model selection to identify predictors of whooping crane hatching success. The strongest predictor, and that with clear management relevance, was incubation environment. The incubation period of whooping crane eggs is split across two environments: crane nests and artificial incubators. Although artificial incubators are useful for allowing breeding pairs to produce multiple clutches, our results indicate that crane incubation is most effective at promoting hatching success. Hatching probability increased the longer an egg spent in a crane nest, from 40% hatching probability for eggs receiving 1 day of crane incubation to 95% for those receiving 30 days (time incubated in each environment varied independently of total incubation period). Because birds will lay fewer eggs when they are incubating longer, a tradeoff exists between the number of clutches produced and egg hatching probability. We developed a decision-analytic model that estimated 16 to be the optimal number of days of crane incubation needed to maximize the number of offspring produced. These results show that using decision-analytic tools to account for uncertainty in captive breeding can improve the rate at which such programs contribute to wildlife reintroductions. 

A decision support system using analytical hierarchy process (AHP) for the optimal environmental reclamation of an open-pit mine

NASA Astrophysics Data System (ADS)

Bascetin, A.

2007-04-01

The selection of an optimal reclamation method is one of the most important factors in open-pit design and production planning. It also affects economic considerations in open-pit design as a function of plan location and depth. Furthermore, the selection is a complex multi-person, multi-criteria decision problem. The group decision-making process can be improved by applying a systematic and logical approach to assess the priorities based on the inputs of several specialists from different functional areas within the mine company. The analytical hierarchy process (AHP) can be very useful in involving several decision makers with different conflicting objectives to arrive at a consensus decision. In this paper, the selection of an optimal reclamation method using an AHP-based model was evaluated for coal production in an open-pit coal mine located at Seyitomer region in Turkey. The use of the proposed model indicates that it can be applied to improve the group decision making in selecting a reclamation method that satisfies optimal specifications. Also, it is found that the decision process is systematic and using the proposed model can reduce the time taken to select a optimal method.

Decision-Oriented Health Technology Assessment: One Step Forward in Supporting the Decision-Making Process in Hospitals.

PubMed

Ritrovato, Matteo; Faggiano, Francesco C; Tedesco, Giorgia; Derrico, Pietro

2015-06-01

This article outlines the Decision-Oriented Health Technology Assessment: a new implementation of the European network for Health Technology Assessment Core Model, integrating the multicriteria decision-making analysis by using the analytic hierarchy process to introduce a standardized methodological approach as a valued and shared tool to support health care decision making within a hospital. Following the Core Model as guidance (European network for Health Technology Assessment. HTA core model for medical and surgical interventions. Available from: http://www.eunethta.eu/outputs/hta-core-model-medical-and-surgical-interventions-10r. [Accessed May 27, 2014]), it is possible to apply the analytic hierarchy process to break down a problem into its constituent parts and identify priorities (i.e., assigning a weight to each part) in a hierarchical structure. Thus, it quantitatively compares the importance of multiple criteria in assessing health technologies and how the alternative technologies perform in satisfying these criteria. The verbal ratings are translated into a quantitative form by using the Saaty scale (Saaty TL. Decision making with the analytic hierarchy process. Int J Serv Sci 2008;1:83-98). An eigenvectors analysis is used for deriving the weights' systems (i.e., local and global weights' system) that reflect the importance assigned to the criteria and the priorities related to the performance of the alternative technologies. Compared with the Core Model, this methodological approach supplies a more timely as well as contextualized evidence for a specific technology, making it possible to obtain data that are more relevant and easier to interpret, and therefore more useful for decision makers to make investment choices with greater awareness. We reached the conclusion that although there may be scope for improvement, this implementation is a step forward toward the goal of building a "solid bridge" between the scientific evidence and the final decision maker's choice. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Early warning systems for the management of chronic heart failure: a systematic literature review of cost-effectiveness models.

PubMed

Albuquerque De Almeida, Fernando; Al, Maiwenn; Koymans, Ron; Caliskan, Kadir; Kerstens, Ankie; Severens, Johan L

2018-04-01

Describing the general and methodological characteristics of decision-analytical models used in the economic evaluation of early warning systems for the management of chronic heart failure patients and performing a quality assessment of their methodological characteristics is expected to provide concise and useful insight to inform the future development of decision-analytical models in the field of heart failure management. Areas covered: The literature on decision-analytical models for the economic evaluation of early warning systems for the management of chronic heart failure patients was systematically reviewed. Nine electronic databases were searched through the combination of synonyms for heart failure and sensitive filters for cost-effectiveness and early warning systems. Expert commentary: The retrieved models show some variability with regards to their general study characteristics. Overall, they display satisfactory methodological quality, even though some points could be improved, namely on the consideration and discussion of any competing theories regarding model structure and disease progression, identification of key parameters and the use of expert opinion, and uncertainty analyses. A comprehensive definition of early warning systems and further research under this label should be pursued. To improve the transparency of economic evaluation publications, authors should make available detailed technical information regarding the published models.

Decision analysis to complete diagnostic research by closing the gap between test characteristics and cost-effectiveness.

PubMed

Schaafsma, Joanna D; van der Graaf, Yolanda; Rinkel, Gabriel J E; Buskens, Erik

2009-12-01

The lack of a standard methodology in diagnostic research impedes adequate evaluation before implementation of constantly developing diagnostic techniques. We discuss the methodology of diagnostic research and underscore the relevance of decision analysis in the process of evaluation of diagnostic tests. Overview and conceptual discussion. Diagnostic research requires a stepwise approach comprising assessment of test characteristics followed by evaluation of added value, clinical outcome, and cost-effectiveness. These multiple goals are generally incompatible with a randomized design. Decision-analytic models provide an important alternative through integration of the best available evidence. Thus, critical assessment of clinical value and efficient use of resources can be achieved. Decision-analytic models should be considered part of the standard methodology in diagnostic research. They can serve as a valid alternative to diagnostic randomized clinical trials (RCTs).

Development of a robust space power system decision model

NASA Astrophysics Data System (ADS)

Chew, Gilbert; Pelaccio, Dennis G.; Jacobs, Mark; Stancati, Michael; Cataldo, Robert

2001-02-01

NASA continues to evaluate power systems to support human exploration of the Moon and Mars. The system(s) would address all power needs of surface bases and on-board power for space transfer vehicles. Prior studies have examined both solar and nuclear-based alternatives with respect to individual issues such as sizing or cost. What has not been addressed is a comprehensive look at the risks and benefits of the options that could serve as the analytical framework to support a system choice that best serves the needs of the exploration program. This paper describes the SAIC developed Space Power System Decision Model, which uses a formal Two-step Analytical Hierarchy Process (TAHP) methodology that is used in the decision-making process to clearly distinguish candidate power systems in terms of benefits, safety, and risk. TAHP is a decision making process based on the Analytical Hierarchy Process, which employs a hierarchic approach of structuring decision factors by weights, and relatively ranks system design options on a consistent basis. This decision process also includes a level of data gathering and organization that produces a consistent, well-documented assessment, from which the capability of each power system option to meet top-level goals can be prioritized. The model defined on this effort focuses on the comparative assessment candidate power system options for Mars surface application(s). This paper describes the principles of this approach, the assessment criteria and weighting procedures, and the tools to capture and assess the expert knowledge associated with space power system evaluation. .

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-01-01

Background Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. Methods In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Results Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Conclusion Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided. PMID:19036144

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.

PubMed

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-11-26

Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.

A Dual-Process Approach to Health Risk Decision Making: The Prototype Willingness Model

ERIC Educational Resources Information Center

Gerrard, Meg; Gibbons, Frederick X.; Houlihan, Amy E.; Stock, Michelle L.; Pomery, Elizabeth A.

2008-01-01

Although dual-process models in cognitive, personality, and social psychology have stimulated a large body of research about analytic and heuristic modes of decision making, these models have seldom been applied to the study of adolescent risk behaviors. In addition, the developmental course of these two kinds of information processing, and their…

Decision analysis to address extreme weather : extreme weather effects on ridership and modeling the decision to invest in canopy coverage.

DOT National Transportation Integrated Search

2016-09-01

This project applies a decision analytic methodology that takes considerations of extreme weather events to quantify and assess canopy investment options. The project collected data for two cases studies in two different transit agencies: Chicago Tra...

A model to inform management actions as a response to chytridiomycosis-associated decline

USGS Publications Warehouse

Converse, Sarah J.; Bailey, Larissa L.; Mosher, Brittany A.; Funk, W. Chris; Gerber, Brian D.; Muths, Erin L.

2017-01-01

Decision-analytic models provide forecasts of how systems of interest will respond to management. These models can be parameterized using empirical data, but sometimes require information elicited from experts. When evaluating the effects of disease in species translocation programs, expert judgment is likely to play a role because complete empirical information will rarely be available. We illustrate development of a decision-analytic model built to inform decision-making regarding translocations and other management actions for the boreal toad (Anaxyrus boreas boreas), a species with declines linked to chytridiomycosis caused by Batrachochytrium dendrobatidis (Bd). Using the model, we explored the management implications of major uncertainties in this system, including whether there is a genetic basis for resistance to pathogenic infection by Bd, how translocation can best be implemented, and the effectiveness of efforts to reduce the spread of Bd. Our modeling exercise suggested that while selection for resistance to pathogenic infectionDecision-analytic models provide forecasts of how systems of interest will respond to management. These models can be parameterized using empirical data, but sometimes require information elicited from experts. When evaluating the effects of disease in species translocation programs, expert judgment is likely to play a role because complete empirical information will rarely be available. We illustrate development of a decision-analytic model built to inform decision-making regarding translocations and other management actions for the boreal toad (Anaxyrus boreas boreas), a species with declines linked to chytridiomycosis caused by Batrachochytrium dendrobatidis (Bd). Using the model, we explored the management implications of major uncertainties in this system, including whether there is a genetic basis for resistance to pathogenic infection by Bd, how translocation can best be implemented, and the effectiveness of efforts to reduce the spread of Bd. Our modeling exercise suggested that while selection for resistance to pathogenic infection by Bd could increase numbers of sites occupied by toads, and translocations could increase the rate of toad recovery, efforts to reduce the spread of Bd may have little effect. We emphasize the need to continue developing and parameterizing models necessary to assess management actions for combating chytridiomycosis-associated declines. by Bd could increase numbers of sites occupied by toads, and translocations could increase the rate of toad recovery, efforts to reduce the spread of Bd may have little effect. We emphasize the need to continue developing and parameterizing models necessary to assess management actions for combating chytridiomycosis-associated declines.

Health versus money. Value judgments in the perspective of decision analysis.

PubMed

Thompson, M S

1983-01-01

An important, but largely uninvestigated, value trade-off balances marginal nonhealth consumption against marginal medical care. Benefit-cost analysts have traditionally, if not fully satisfactorily, dealt with this issue by valuing health gains by their effects on productivity. Cost-effectiveness analysts compare monetary and health effects and leave their relative valuations to decision makers. A decision-analytic model using the satisfaction or utility gained from nonhealth consumption and the level of health enables one to calculate willingness to pay--a theoretically superior way of assigning monetary values to effects for benefit-cost analysis-and to determine minimally acceptable cost-effectiveness ratios. Examples show how a decision-analytic model of utility can differentiate medical actions so essential that failure to take them would be considered negligent from actions so expensive as to be unjustifiable, and can help to determine optimal legal arrangements for compensation for medical malpractice.

A Data Analytical Framework for Improving Real-Time, Decision Support Systems in Healthcare

ERIC Educational Resources Information Center

Yahav, Inbal

2010-01-01

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to…

Diagnosing Alzheimer's disease: a systematic review of economic evaluations.

PubMed

Handels, Ron L H; Wolfs, Claire A G; Aalten, Pauline; Joore, Manuela A; Verhey, Frans R J; Severens, Johan L

2014-03-01

The objective of this study is to systematically review the literature on economic evaluations of interventions for the early diagnosis of Alzheimer's disease (AD) and related disorders and to describe their general and methodological characteristics. We focused on the diagnostic aspects of the decision models to assess the applicability of existing decision models for the evaluation of the recently revised diagnostic research criteria for AD. PubMed and the National Institute for Health Research Economic Evaluation database were searched for English-language publications related to economic evaluations on diagnostic technologies. Trial-based economic evaluations were assessed using the Consensus on Health Economic Criteria list. Modeling studies were assessed using the framework for quality assessment of decision-analytic models. The search retrieved 2109 items, from which eight decision-analytic modeling studies and one trial-based economic evaluation met all eligibility criteria. Diversity among the study objective and characteristics was considerable and, despite considerable methodological quality, several flaws were indicated. Recommendations were focused on diagnostic aspects and the applicability of existing models for the evaluation of recently revised diagnostic research criteria for AD. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Decision support systems in health economics.

PubMed

Quaglini, S; Dazzi, L; Stefanelli, M; Barosi, G; Marchetti, M

1999-08-01

This article describes a system addressed to different health care professionals for building, using, and sharing decision support systems for resource allocation. The system deals with selected areas, namely the choice of diagnostic tests, the therapy planning, and the instrumentation purchase. Decision support is based on decision-analytic models, incorporating an explicit knowledge representation of both the medical domain knowledge and the economic evaluation theory. Application models are built on top of meta-models, that are used as guidelines for making explicit both the cost and effectiveness components. This approach improves the transparency and soundness of the collaborative decision-making process and facilitates the result interpretation.

Structuring modeling and simulation analysis for evacuation planning and operations.

DOT National Transportation Integrated Search

2009-06-01

This document is intended to provide guidance to decision-makers at agencies and jurisdictions considering the role of analytical tools in evacuation planning and operations. It is often unclear what kind of analytical approach may be of most value, ...

Models based on value and probability in health improve shared decision making.

PubMed

Ortendahl, Monica

2008-10-01

Diagnostic reasoning and treatment decisions are a key competence of doctors. A model based on values and probability provides a conceptual framework for clinical judgments and decisions, and also facilitates the integration of clinical and biomedical knowledge into a diagnostic decision. Both value and probability are usually estimated values in clinical decision making. Therefore, model assumptions and parameter estimates should be continually assessed against data, and models should be revised accordingly. Introducing parameter estimates for both value and probability, which usually pertain in clinical work, gives the model labelled subjective expected utility. Estimated values and probabilities are involved sequentially for every step in the decision-making process. Introducing decision-analytic modelling gives a more complete picture of variables that influence the decisions carried out by the doctor and the patient. A model revised for perceived values and probabilities by both the doctor and the patient could be used as a tool for engaging in a mutual and shared decision-making process in clinical work.

Bridging the Gap between Human Judgment and Automated Reasoning in Predictive Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; Riensche, Roderick M.; Unwin, Stephen D.

2010-06-07

Events occur daily that impact the health, security and sustainable growth of our society. If we are to address the challenges that emerge from these events, anticipatory reasoning has to become an everyday activity. Strong advances have been made in using integrated modeling for analysis and decision making. However, a wider impact of predictive analytics is currently hindered by the lack of systematic methods for integrating predictive inferences from computer models with human judgment. In this paper, we present a predictive analytics approach that supports anticipatory analysis and decision-making through a concerted reasoning effort that interleaves human judgment and automatedmore » inferences. We describe a systematic methodology for integrating modeling algorithms within a serious gaming environment in which role-playing by human agents provides updates to model nodes and the ensuing model outcomes in turn influence the behavior of the human players. The approach ensures a strong functional partnership between human players and computer models while maintaining a high degree of independence and greatly facilitating the connection between model and game structures.« less

Issues in Developing a Normative Descriptive Model for Dyadic Decision Making

NASA Technical Reports Server (NTRS)

Serfaty, D.; Kleinman, D. L.

1984-01-01

Most research in modelling human information processing and decision making has been devoted to the case of the single human operator. In the present effort, concepts from the fields of organizational behavior, engineering psychology, team theory and mathematical modelling are merged in an attempt to consider first the case of two cooperating decisionmakers (the Dyad) in a multi-task environment. Rooted in the well-known Dynamic Decision Model (DDM), the normative descriptive approach brings basic cognitive and psychophysical characteristics inherent to human behavior into a team theoretic analytic framework. An experimental paradigm, involving teams in dynamic decision making tasks, is designed to produce the data with which to build the theoretical model.

Collaborative human-machine analysis to disambiguate entities in unstructured text and structured datasets

NASA Astrophysics Data System (ADS)

Davenport, Jack H.

2016-05-01

Intelligence analysts demand rapid information fusion capabilities to develop and maintain accurate situational awareness and understanding of dynamic enemy threats in asymmetric military operations. The ability to extract relationships between people, groups, and locations from a variety of text datasets is critical to proactive decision making. The derived network of entities must be automatically created and presented to analysts to assist in decision making. DECISIVE ANALYTICS Corporation (DAC) provides capabilities to automatically extract entities, relationships between entities, semantic concepts about entities, and network models of entities from text and multi-source datasets. DAC's Natural Language Processing (NLP) Entity Analytics model entities as complex systems of attributes and interrelationships which are extracted from unstructured text via NLP algorithms. The extracted entities are automatically disambiguated via machine learning algorithms, and resolution recommendations are presented to the analyst for validation; the analyst's expertise is leveraged in this hybrid human/computer collaborative model. Military capability is enhanced by these NLP Entity Analytics because analysts can now create/update an entity profile with intelligence automatically extracted from unstructured text, thereby fusing entity knowledge from structured and unstructured data sources. Operational and sustainment costs are reduced since analysts do not have to manually tag and resolve entities.

Comparative-effectiveness research to aid population decision making by relating clinical outcomes and quality-adjusted life years.

PubMed

Campbell, Jonathan D; Zerzan, Judy; Garrison, Louis P; Libby, Anne M

2013-04-01

Comparative-effectiveness research (CER) at the population level is missing standardized approaches to quantify and weigh interventions in terms of their clinical risks, benefits, and uncertainty. We proposed an adapted CER framework for population decision making, provided example displays of the outputs, and discussed the implications for population decision makers. Building on decision-analytical modeling but excluding cost, we proposed a 2-step approach to CER that explicitly compared interventions in terms of clinical risks and benefits and linked this evidence to the quality-adjusted life year (QALY). The first step was a traditional intervention-specific evidence synthesis of risks and benefits. The second step was a decision-analytical model to simulate intervention-specific progression of disease over an appropriate time. The output was the ability to compare and quantitatively link clinical outcomes with QALYs. The outputs from these CER models include clinical risks, benefits, and QALYs over flexible and relevant time horizons. This approach yields an explicit, structured, and consistent quantitative framework to weigh all relevant clinical measures. Population decision makers can use this modeling framework and QALYs to aid in their judgment of the individual and collective risks and benefits of the alternatives over time. Future research should study effective communication of these domains for stakeholders. Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.

Demographics of reintroduced populations: estimation, modeling, and decision analysis

USGS Publications Warehouse

Converse, Sarah J.; Moore, Clinton T.; Armstrong, Doug P.

2013-01-01

Reintroduction can be necessary for recovering populations of threatened species. However, the success of reintroduction efforts has been poorer than many biologists and managers would hope. To increase the benefits gained from reintroduction, management decision making should be couched within formal decision-analytic frameworks. Decision analysis is a structured process for informing decision making that recognizes that all decisions have a set of components—objectives, alternative management actions, predictive models, and optimization methods—that can be decomposed, analyzed, and recomposed to facilitate optimal, transparent decisions. Because the outcome of interest in reintroduction efforts is typically population viability or related metrics, models used in decision analysis efforts for reintroductions will need to include population models. In this special section of the Journal of Wildlife Management, we highlight examples of the construction and use of models for informing management decisions in reintroduced populations. In this introductory contribution, we review concepts in decision analysis, population modeling for analysis of decisions in reintroduction settings, and future directions. Increased use of formal decision analysis, including adaptive management, has great potential to inform reintroduction efforts. Adopting these practices will require close collaboration among managers, decision analysts, population modelers, and field biologists.

Analytical tool for measuring emissions impact of acceleration and deceleration lanes : final report.

DOT National Transportation Integrated Search

2001-04-01

Air quality has become one of the important factors to be considered in making transportation improvement : decisions. Thus, tools are expected to help such decision-makings. On the other hand, MOBILE5 model, which : has been widely used in evaluatin...

Evaluation of simplified stream-aquifer depletion models for water rights administration

USGS Publications Warehouse

Sophocleous, Marios; Koussis, Antonis; Martin, J.L.; Perkins, S.P.

1995-01-01

We assess the predictive accuracy of Glover's (1974) stream-aquifer analytical solutions, which are commonly used in administering water rights, and evaluate the impact of the assumed idealizations on administrative and management decisions. To achieve these objectives, we evaluate the predictive capabilities of the Glover stream-aquifer depletion model against the MODFLOW numerical standard, which, unlike the analytical model, can handle increasing hydrogeologic complexity. We rank-order and quantify the relative importance of the various assumptions on which the analytical model is based, the three most important being: (1) streambed clogging as quantified by streambed-aquifer hydraulic conductivity contrast; (2) degree of stream partial penetration; and (3) aquifer heterogeneity. These three factors relate directly to the multidimensional nature of the aquifer flow conditions. From these considerations, future efforts to reduce the uncertainty in stream depletion-related administrative decisions should primarily address these three factors in characterizing the stream-aquifer process. We also investigate the impact of progressively coarser model grid size on numerically estimating stream leakage and conclude that grid size effects are relatively minor. Therefore, when modeling is required, coarser model grids could be used thus minimizing the input data requirements.

Non-thermal transitions in a model inspired by moral decisions

NASA Astrophysics Data System (ADS)

Alamino, Roberto C.

2016-08-01

This work introduces a model in which agents of a network act upon one another according to three different kinds of moral decisions. These decisions are based on an increasing level of sophistication in the empathy capacity of the agent, a hierarchy which we name Piaget’s ladder. The decision strategy of the agents is non-rational, in the sense they are arbitrarily fixed, and the model presents quenched disorder given by the distribution of its defining parameters. An analytical solution for this model is obtained in the large system limit as well as a leading order correction for finite-size systems which shows that typical realisations of the model develop a phase structure with both continuous and discontinuous non-thermal transitions.

Decision Support Model for Introduction of Gamification Solution Using AHP

PubMed Central

2014-01-01

Gamification means the use of various elements of game design in nongame contexts including workplace collaboration, marketing, education, military, and medical services. Gamification is effective for both improving workplace productivity and motivating employees. However, introduction of gamification is not easy because the planning and implementation processes of gamification are very complicated and it needs interdisciplinary knowledge such as information systems, organization behavior, and human psychology. Providing a systematic decision making method for gamification process is the purpose of this paper. This paper suggests the decision criteria for selection of gamification platform to support a systematic decision making process for managements. The criteria are derived from previous works on gamification, introduction of information systems, and analytic hierarchy process. The weights of decision criteria are calculated through a survey by the professionals on game, information systems, and business administration. The analytic hierarchy process is used to derive the weights. The decision criteria and weights provided in this paper could support the managements to make a systematic decision for selection of gamification platform. PMID:24892075

Decision support model for introduction of gamification solution using AHP.

PubMed

Kim, Sangkyun

2014-01-01

Gamification means the use of various elements of game design in nongame contexts including workplace collaboration, marketing, education, military, and medical services. Gamification is effective for both improving workplace productivity and motivating employees. However, introduction of gamification is not easy because the planning and implementation processes of gamification are very complicated and it needs interdisciplinary knowledge such as information systems, organization behavior, and human psychology. Providing a systematic decision making method for gamification process is the purpose of this paper. This paper suggests the decision criteria for selection of gamification platform to support a systematic decision making process for managements. The criteria are derived from previous works on gamification, introduction of information systems, and analytic hierarchy process. The weights of decision criteria are calculated through a survey by the professionals on game, information systems, and business administration. The analytic hierarchy process is used to derive the weights. The decision criteria and weights provided in this paper could support the managements to make a systematic decision for selection of gamification platform.

Measurement and Modelling: Sequential Use of Analytical Techniques in a Study of Risk-Taking in Decision-Making by School Principals

ERIC Educational Resources Information Center

Trimmer, Karen

2016-01-01

This paper investigates reasoned risk-taking in decision-making by school principals using a methodology that combines sequential use of psychometric and traditional measurement techniques. Risk-taking is defined as when decisions are made that are not compliant with the regulatory framework, the primary governance mechanism for public schools in…

CorRECTreatment: A Web-based Decision Support Tool for Rectal Cancer Treatment that Uses the Analytic Hierarchy Process and Decision Tree

PubMed Central

Karakülah, G.; Dicle, O.; Sökmen, S.; Çelikoğlu, C.C.

2015-01-01

Summary Background The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making. Objective The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. Methods The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. Results In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. Conclusions The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options. PMID:25848413

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree.

PubMed

Suner, A; Karakülah, G; Dicle, O; Sökmen, S; Çelikoğlu, C C

2015-01-01

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making. The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

GIS-Based Suitability Model for Assessment of Forest Biomass Energy Potential in a Region of Portugal

NASA Astrophysics Data System (ADS)

Quinta-Nova, Luis; Fernandez, Paulo; Pedro, Nuno

2017-12-01

This work focuses on developed a decision support system based on multicriteria spatial analysis to assess the potential for generation of biomass residues from forestry sources in a region of Portugal (Beira Baixa). A set of environmental, economic and social criteria was defined, evaluated and weighted in the context of Saaty’s analytic hierarchies. The best alternatives were obtained after applying Analytic Hierarchy Process (AHP). The model was applied to the central region of Portugal where forest and agriculture are the most representative land uses. Finally, sensitivity analysis of the set of factors and their associated weights was performed to test the robustness of the model. The proposed evaluation model provides a valuable reference for decision makers in establishing a standardized means of selecting the optimal location for new biomass plants.

Failure detection system design methodology. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chow, E. Y.

1980-01-01

The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules.

Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

PubMed

Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

2013-01-01

Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.

Assessing and reducing hydrogeologic model uncertainty

USDA-ARS?s Scientific Manuscript database

NRC is sponsoring research that couples model abstraction techniques with model uncertainty assessment methods. Insights and information from this program will be useful in decision making by NRC staff, licensees and stakeholders in their assessment of subsurface radionuclide transport. All analytic...

Robust Sensitivity Analysis for Multi-Attribute Deterministic Hierarchical Value Models

DTIC Science & Technology

2002-03-01

such as weighted sum method, weighted 5 product method, and the Analytic Hierarchy Process ( AHP ). This research focuses on only weighted sum...different groups. They can be termed as deterministic, stochastic, or fuzzy multi-objective decision methods if they are classified according to the...weighted product model (WPM), and analytic hierarchy process ( AHP ). His method attempts to identify the most important criteria weight and the most

The potential value of Clostridium difficile vaccine: an economic computer simulation model.

PubMed

Lee, Bruce Y; Popovich, Michael J; Tian, Ye; Bailey, Rachel R; Ufberg, Paul J; Wiringa, Ann E; Muder, Robert R

2010-07-19

Efforts are currently underway to develop a vaccine against Clostridium difficile infection (CDI). We developed two decision analytic Monte Carlo computer simulation models: (1) an Initial Prevention Model depicting the decision whether to administer C. difficile vaccine to patients at-risk for CDI and (2) a Recurrence Prevention Model depicting the decision whether to administer C. difficile vaccine to prevent CDI recurrence. Our results suggest that a C. difficile vaccine could be cost-effective over a wide range of C. difficile risk, vaccine costs, and vaccine efficacies especially, when being used post-CDI treatment to prevent recurrent disease. (c) 2010 Elsevier Ltd. All rights reserved.

The Potential Value of Clostridium difficile Vaccine: An Economic Computer Simulation Model

PubMed Central

Lee, Bruce Y.; Popovich, Michael J.; Tian, Ye; Bailey, Rachel R.; Ufberg, Paul J.; Wiringa, Ann E.; Muder, Robert R.

2010-01-01

Efforts are currently underway to develop a vaccine against Clostridium difficile infection (CDI). We developed two decision analytic Monte Carlo computer simulation models: (1) an Initial Prevention Model depicting the decision whether to administer C. difficile vaccine to patients at-risk for CDI and (2) a Recurrence Prevention Model depicting the decision whether to administer C. difficile vaccine to prevent CDI recurrence. Our results suggest that a C. difficile vaccine could be cost-effective over a wide range of C. difficile risk, vaccine costs, and vaccine efficacies especially when being used post-CDI treatment to prevent recurrent disease. PMID:20541582

Modelling a suitable location for Urban Solid Waste Management using AHP method and GIS -A geospatial approach and MCDM Model

NASA Astrophysics Data System (ADS)

Iqbal, M.; Islam, A.; Hossain, A.; Mustaque, S.

2016-12-01

Multi-Criteria Decision Making(MCDM) is advanced analytical method to evaluate appropriate result or decision from multiple criterion environment. Present time in advanced research, MCDM technique is progressive analytical process to evaluate a logical decision from various conflict. In addition, Present day Geospatial approach (e.g. Remote sensing and GIS) also another advanced technical approach in a research to collect, process and analyze various spatial data at a time. GIS and Remote sensing together with the MCDM technique could be the best platform to solve a complex decision making process. These two latest process combined very effectively used in site selection for solid waste management in urban policy. The most popular MCDM technique is Weighted Linear Method (WLC) where Analytical Hierarchy Process (AHP) is another popular and consistent techniques used in worldwide as dependable decision making. Consequently, the main objective of this study is improving a AHP model as MCDM technique with Geographic Information System (GIS) to select a suitable landfill site for urban solid waste management. Here AHP technique used as a MCDM tool to select the best suitable landfill location for urban solid waste management. To protect the urban environment in a sustainable way municipal waste needs an appropriate landfill site considering environmental, geological, social and technical aspect of the region. A MCDM model generate from five class related which related to environmental, geological, social and technical using AHP method and input the result set in GIS for final model location for urban solid waste management. The final suitable location comes out that 12.2% of the area corresponds to 22.89 km2 considering the total study area. In this study, Keraniganj sub-district of Dhaka district in Bangladesh is consider as study area which is densely populated city currently undergoes an unmanaged waste management system especially the suitable landfill sites for waste dumping site.

An Analysis of Machine- and Human-Analytics in Classification.

PubMed

Tam, Gary K L; Kothari, Vivek; Chen, Min

2017-01-01

In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.

Modeling Choice Under Uncertainty in Military Systems Analysis

DTIC Science & Technology

1991-11-01

operators rather than fuzzy operators. This is suggested for further research. 4.3 ANALYTIC HIERARCHICAL PROCESS ( AHP ) In AHP , objectives, functions and...14 4.1 IMPRECISELY SPECIFIED MULTIPLE A’ITRIBUTE UTILITY THEORY... 14 4.2 FUZZY DECISION ANALYSIS...14 4.3 ANALYTIC HIERARCHICAL PROCESS ( AHP ) ................................... 14 4.4 SUBJECTIVE TRANSFER FUNCTION APPROACH

Optimizing an Immersion ESL Curriculum Using Analytic Hierarchy Process

ERIC Educational Resources Information Center

Tang, Hui-Wen Vivian

2011-01-01

The main purpose of this study is to fill a substantial knowledge gap regarding reaching a uniform group decision in English curriculum design and planning. A comprehensive content-based course criterion model extracted from existing literature and expert opinions was developed. Analytical hierarchy process (AHP) was used to identify the relative…

Modeling Biodegradation and Reactive Transport: Analytical and Numerical Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Y; Glascoe, L

The computational modeling of the biodegradation of contaminated groundwater systems accounting for biochemical reactions coupled to contaminant transport is a valuable tool for both the field engineer/planner with limited computational resources and the expert computational researcher less constrained by time and computer power. There exists several analytical and numerical computer models that have been and are being developed to cover the practical needs put forth by users to fulfill this spectrum of computational demands. Generally, analytical models provide rapid and convenient screening tools running on very limited computational power, while numerical models can provide more detailed information with consequent requirementsmore » of greater computational time and effort. While these analytical and numerical computer models can provide accurate and adequate information to produce defensible remediation strategies, decisions based on inadequate modeling output or on over-analysis can have costly and risky consequences. In this chapter we consider both analytical and numerical modeling approaches to biodegradation and reactive transport. Both approaches are discussed and analyzed in terms of achieving bioremediation goals, recognizing that there is always a tradeoff between computational cost and the resolution of simulated systems.« less

Integrating Water Quality and River Rehabilitation Management - A Decision-Analytical Perspective

NASA Astrophysics Data System (ADS)

Reichert, P.; Langhans, S.; Lienert, J.; Schuwirth, N.

2009-04-01

Integrative river management involves difficult decisions about alternative measures to improve their ecological state. For this reason, it seems useful to apply knowledge from the decision sciences to support river management. We discuss how decision-analytical elements can be employed for designing an integrated river management procedure. An important aspect of this procedure is to clearly separate scientific predictions of the consequences of alternatives from objectives to be achieved by river management. The key elements of the suggested procedure are (i) the quantitative elicitation of the objectives from different stakeholder groups, (ii) the compilation of the current scientific knowledge about the consequences of the effects resulting from suggested measures in the form of a probabilistic mathematical model, and (iii) the use of these predictions and valuations to prioritize alternatives, to uncover conflicting objectives, to support the design of better alternatives, and to improve the transparency of communication about the chosen management strategy. The development of this procedure led to insights regarding necessary steps to be taken for rational decision-making in river management, to guidelines about the use of decision-analytical techniques for performing these steps, but also to new insights about the application of decision-analytical techniques in general. In particular, the consideration of the spatial distribution of the effects of measures and the potential added value of connected rehabilitated river reaches leads to favoring measures that have a positive effect beyond a single river reach. As these effects only propagate within the river network, this results in a river basin oriented management concept as a consequence of a rational decision support procedure, rather than as an a priori management paradigm. There are also limitations to the support that can be expected from the decision-analytical perspective. It will not provide the societal values that are driving prioritization in river management, it will only support their elicitation and rational use. This is particularly important for the assessment of micro-pollutants because of severe limitations in scientific knowledge of their effects on river ecosystems. This makes the influence of pollution by micro-pollutants on prioritization of measures strongly dependent on the weight of the precautionary principle relative to other societal objectives of river management.

[Evaluating the maturity of IT-supported clinical imaging and diagnosis using the Digital Imaging Adoption Model : Are your clinical imaging processes ready for the digital era?

PubMed

Studzinski, J

2017-06-01

The Digital Imaging Adoption Model (DIAM) has been jointly developed by HIMSS Analytics and the European Society of Radiology (ESR). It helps evaluate the maturity of IT-supported processes in medical imaging, particularly in radiology. This eight-stage maturity model drives your organisational, strategic and tactical alignment towards imaging-IT planning. The key audience for the model comprises hospitals with imaging centers, as well as external imaging centers that collaborate with hospitals. The assessment focuses on different dimensions relevant to digital imaging, such as software infrastructure and usage, workflow security, clinical documentation and decision support, data exchange and analytical capabilities. With its standardised approach, it enables regional, national and international benchmarking. All DIAM participants receive a structured report that can be used as a basis for presenting, e.g. budget planning and investment decisions at management level.

An Empirical Study on the Preference of Supermarkets with Analytic Hierarchy Process Model

NASA Astrophysics Data System (ADS)

Weng Siew, Lam; Singh, Ranjeet; Singh, Bishan; Weng Hoe, Lam; Kah Fai, Liew

2018-04-01

Large-scale retailers are very important to the consumers in this fast-paced world. Selection of desirable market to purchase products and services becomes major concern among consumers in their daily life due to vast choices available. Therefore, the objective of this paper is to determine the most preferred supermarket among AEON, Jaya Grocer, Tesco, Giant and Econsave by the undergraduate students in Malaysia with Analytic Hierarchy Process (AHP) model. Besides that, this study also aims to determine the priority of decision criteria in the selection of supermarkets among the undergraduatestudents with AHP model. The decision criteria employed in this study are product quality, competitive price, cleanliness, product variety, location, good price labelling, fast checkout and employee courtesy. The results of this study show that AEON is the most preferred supermarket followed by Jaya Grocer, Tesco, Econsave and Giant among the students based on AHP model. Product quality, cleanliness and competitive price are ranked as the top three influential factors in this study. This study is significant because it helps to determine the most preferred supermarket as well as the most influential decision criteria in the preference of supermarkets among the undergraduate students with AHP model.

Antecedents and Consequences of Retirement Planning and Decision-Making: A Meta-Analysis and Model

ERIC Educational Resources Information Center

Topa, Gabriela; Moriano, Juan Antonio; Depolo, Marco; Alcover, Carlos-Maria; Morales, J. Francisco

2009-01-01

In this study, meta-analytic procedures were used to examine the relationships between retirement planning, retirement decision and their antecedent and consequences. Our review of the literature generated 341 independent samples obtained from 99 primary studies with 188,222 participants. A small effect size (ES) for antecedents of retirement…

Performance Assessment in Serious Games: Compensating for the Effects of Randomness

ERIC Educational Resources Information Center

Westera, Wim

2016-01-01

This paper is about performance assessment in serious games. We conceive serious gaming as a process of player-lead decision taking. Starting from combinatorics and item-response theory we provide an analytical model that makes explicit to what extent observed player performances (decisions) are blurred by chance processes (guessing behaviors). We…

A dashboard-based system for supporting diabetes care.

PubMed

Dagliati, Arianna; Sacchi, Lucia; Tibollo, Valentina; Cogni, Giulia; Teliti, Marsida; Martinez-Millana, Antonio; Traver, Vicente; Segagni, Daniele; Posada, Jorge; Ottaviano, Manuel; Fico, Giuseppe; Arredondo, Maria Teresa; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

2018-05-01

To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system's capability of identifying and understanding the characteristics of patient subgroups treated at the center. Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

Dynamic remapping of parallel computations with varying resource demands

NASA Technical Reports Server (NTRS)

Nicol, D. M.; Saltz, J. H.

1986-01-01

A large class of computational problems is characterized by frequent synchronization, and computational requirements which change as a function of time. When such a problem must be solved on a message passing multiprocessor machine, the combination of these characteristics lead to system performance which decreases in time. Performance can be improved with periodic redistribution of computational load; however, redistribution can exact a sometimes large delay cost. We study the issue of deciding when to invoke a global load remapping mechanism. Such a decision policy must effectively weigh the costs of remapping against the performance benefits. We treat this problem by constructing two analytic models which exhibit stochastically decreasing performance. One model is quite tractable; we are able to describe the optimal remapping algorithm, and the optimal decision policy governing when to invoke that algorithm. However, computational complexity prohibits the use of the optimal remapping decision policy. We then study the performance of a general remapping policy on both analytic models. This policy attempts to minimize a statistic W(n) which measures the system degradation (including the cost of remapping) per computation step over a period of n steps. We show that as a function of time, the expected value of W(n) has at most one minimum, and that when this minimum exists it defines the optimal fixed-interval remapping policy. Our decision policy appeals to this result by remapping when it estimates that W(n) is minimized. Our performance data suggests that this policy effectively finds the natural frequency of remapping. We also use the analytic models to express the relationship between performance and remapping cost, number of processors, and the computation's stochastic activity.

Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management

PubMed Central

Li, Yan; Thomas, Manoj; Osei-Bryson, Kweku-Muata; Levy, Jason

2016-01-01

With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. We build a DM3 ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. A framework to assist decision making in the problem formulation process is developed. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The HAPs case highlights not only the role of complexity in problem formulation but also the need for integrating data from multiple sources and the importance of employing appropriate KDDA modeling techniques. Challenges and opportunities for KDDA are summarized with an emphasis on environmental risk management and HAPs. PMID:27983713

Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management.

PubMed

Li, Yan; Thomas, Manoj; Osei-Bryson, Kweku-Muata; Levy, Jason

2016-12-15

With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. We build a DM³ ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. A framework to assist decision making in the problem formulation process is developed. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The HAPs case highlights not only the role of complexity in problem formulation but also the need for integrating data from multiple sources and the importance of employing appropriate KDDA modeling techniques. Challenges and opportunities for KDDA are summarized with an emphasis on environmental risk management and HAPs.

A preference aggregation model and application in AHP-group decision making

NASA Astrophysics Data System (ADS)

Yang, Taiyi; Yang, De; Chao, Xiangrui

2018-04-01

Group decision making process integrate individual preferences to obtain the group preference by applying aggregation rules and preference relations. The two most useful approaches, the aggregation of individual judgements and the aggregation of individual priorities, traditionally are employed in the Analytic Hierarchy Process to deal with group decision making problems. In both cases, it is assumed that the group preference is approximate weighted mathematical expectation of individual judgements and individual priorities. We propose new preference aggregation methods using optimization models in order to obtain group preference which is close to all individual priorities. Some illustrative examples are finally examined to demonstrate proposed models for application.

Midwives׳ clinical reasoning during second stage labour: Report on an interpretive study.

PubMed

Jefford, Elaine; Fahy, Kathleen

2015-05-01

clinical reasoning was once thought to be the exclusive domain of medicine - setting it apart from 'non-scientific' occupations like midwifery. Poor assessment, clinical reasoning and decision-making skills are well known contributors to adverse outcomes in maternity care. Midwifery decision-making models share a common deficit: they are insufficiently detailed to guide reasoning processes for midwives in practice. For these reasons we wanted to explore if midwives actively engaged in clinical reasoning processes within their clinical practice and if so to what extent. The study was conducted using post structural, feminist methodology. to what extent do midwives engage in clinical reasoning processes when making decisions in the second stage labour? twenty-six practising midwives were interviewed. Feminist interpretive analysis was conducted by two researchers guided by the steps of a model of clinical reasoning process. Six narratives were excluded from analysis because they did not sufficiently address the research question. The midwives narratives were prepared via data reduction. A theoretically informed analysis and interpretation was conducted. using a feminist, interpretive approach we created a model of midwifery clinical reasoning grounded in the literature and consistent with the data. Thirteen of the 20 participant narratives demonstrate analytical clinical reasoning abilities but only nine completed the process and implemented the decision. Seven midwives used non-analytical decision-making without adequately checking against assessment data. over half of the participants demonstrated the ability to use clinical reasoning skills. Less than half of the midwives demonstrated clinical reasoning as their way of making decisions. The new model of Midwifery Clinical Reasoning includes 'intuition' as a valued way of knowing. Using intuition, however, should not replace clinical reasoning which promotes through decision-making can be made transparent and be consensually validated. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combined monitoring, decision and control model for the human operator in a command and control desk

NASA Technical Reports Server (NTRS)

Muralidharan, R.; Baron, S.

1978-01-01

A report is given on the ongoing efforts to mode the human operator in the context of the task during the enroute/return phases in the ground based control of multiple flights of remotely piloted vehicles (RPV). The approach employed here uses models that have their analytical bases in control theory and in statistical estimation and decision theory. In particular, it draws heavily on the modes and the concepts of the optimal control model (OCM) of the human operator. The OCM is being extended into a combined monitoring, decision, and control model (DEMON) of the human operator by infusing decision theoretic notions that make it suitable for application to problems in which human control actions are infrequent and in which monitoring and decision-making are the operator's main activities. Some results obtained with a specialized version of DEMON for the RPV control problem are included.

Reports of the AAAI 2009 Spring Symposia: Technosocial Predictive Analytics.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.

2009-10-01

The Technosocial Predictive Analytics AAAI symposium was held at Stanford University, Stanford, CA, March 23-25, 2009. The goal of this symposium was to explore new methods for anticipatory analytical thinking that provide decision advantage through the integration of human and physical models. Special attention was also placed on how to leverage supporting disciplines to (a) facilitate the achievement of knowledge inputs, (b) improve the user experience, and (c) foster social intelligence through collaborative/competitive work.

Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment.

PubMed

McMeekin, Peter; Flynn, Darren; Ford, Gary A; Rodgers, Helen; Gray, Jo; Thomson, Richard G

2015-11-11

Individualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke. A decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data. The model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA. We have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent.

Approach of Decision Making Based on the Analytic Hierarchy Process for Urban Landscape Management

NASA Astrophysics Data System (ADS)

Srdjevic, Zorica; Lakicevic, Milena; Srdjevic, Bojan

2013-03-01

This paper proposes a two-stage group decision making approach to urban landscape management and planning supported by the analytic hierarchy process. The proposed approach combines an application of the consensus convergence model and the weighted geometric mean method. The application of the proposed approach is shown on a real urban landscape planning problem with a park-forest in Belgrade, Serbia. Decision makers were policy makers, i.e., representatives of several key national and municipal institutions, and experts coming from different scientific fields. As a result, the most suitable management plan from the set of plans is recognized. It includes both native vegetation renewal in degraded areas of park-forest and continued maintenance of its dominant tourism function. Decision makers included in this research consider the approach to be transparent and useful for addressing landscape management tasks. The central idea of this paper can be understood in a broader sense and easily applied to other decision making problems in various scientific fields.

Approach of decision making based on the analytic hierarchy process for urban landscape management.

PubMed

Srdjevic, Zorica; Lakicevic, Milena; Srdjevic, Bojan

2013-03-01

This paper proposes a two-stage group decision making approach to urban landscape management and planning supported by the analytic hierarchy process. The proposed approach combines an application of the consensus convergence model and the weighted geometric mean method. The application of the proposed approach is shown on a real urban landscape planning problem with a park-forest in Belgrade, Serbia. Decision makers were policy makers, i.e., representatives of several key national and municipal institutions, and experts coming from different scientific fields. As a result, the most suitable management plan from the set of plans is recognized. It includes both native vegetation renewal in degraded areas of park-forest and continued maintenance of its dominant tourism function. Decision makers included in this research consider the approach to be transparent and useful for addressing landscape management tasks. The central idea of this paper can be understood in a broader sense and easily applied to other decision making problems in various scientific fields.

Making the right decisions about new technologies: a perspective on criteria and preferences in hospitals.

PubMed

Gurtner, Sebastian

2014-01-01

Decision makers in hospitals are regularly faced with choices about the adoption of new technologies. Wrong decisions lead to a waste of resources and can have serious effects on the patients' and hospital's well-being. The goal of this research was to contribute to the understanding of decision making in hospitals. This study produced insights regarding relevant decision criteria and explored their specific relevance. An initial empirical survey was used to collect the relevant criteria for technological decision making in hospitals. In total, 220 experts in the field of health technology assessment from 34 countries participated in the survey. As a second step, the abovementioned criteria were used to form the basis of an analytic hierarchy process model. A group of 115 physicians, medical technical assistants, and other staff, all of whom worked in the field of radiooncology, prioritized the criteria. An analysis of variance was performed to explore differences among groups in terms of institutional and personal categorization variables. The first part of the research revealed seven key criteria for technological decision making in hospitals. The analytic hierarchy process model revealed that organizational impact was the most important criterion, followed by budget impact. The analysis of variance indicated that there were differences in the perceptions of the importance of the identified criteria. This exploration of the criteria for technological decision making in hospitals will help decision makers consider all of the relevant aspects, leading to more structured and rational decisions. For the optimal resource allocation, all of the relevant stakeholder perspectives and local issues must be considered appropriately.

The Design and Development of an Intelligent Planning Aid

DTIC Science & Technology

1986-07-01

reasons why widening the scope of TACPLAK’s applicability make sense. First# plan execution and monitoring (and the re-planning that then occurs) are...Orsssnu, contracting officer’s representative I», KKY voees o Decision Making Tactical Planning Taxonomy Problem Solving ii M ifrntitr *r MM* I...planning aid. It documents the development of a decision- making , planning, and decision-aiding analytical framework comprising a set of models, s generic

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.

Manufacturing data analytics using a virtual factory representation.

PubMed

Jain, Sanjay; Shao, Guodong; Shin, Seung-Jun

2017-01-01

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

An Information Theoretic Approach for Measuring Data Discovery and Utilization During Analytical and Decision Making Processes

DTIC Science & Technology

2015-07-31

and make the expected decision outcomes. The scenario is based around a scripted storyboard where an organized crime network is operating in a city to...interdicted by law enforcement to disrupt the network. The scenario storyboard was used to develop a probabilistic vehicle traffic model in order to

Towards a Context-Aware Proactive Decision Support Framework

DTIC Science & Technology

2013-11-15

initiative that has developed text analytic technology that crosses the semantic gap into the area of event recognition and representation. The...recognizing operational context, and techniques for recognizing context shift. Additional research areas include: • Adequately capturing users...Universal Interaction Context Ontology [12] might serve as a foundation • Instantiating formal models of decision making based on information seeking

Online Learning Era: Exploring the Most Decisive Determinants of MOOCs in Taiwanese Higher Education

ERIC Educational Resources Information Center

Hsieh, Ming-Yuan

2016-01-01

Because the development of Taiwanese Massive Open Online Course (MOOCs) websites is at this moment full of vitality, this research employs a series of analytical cross-measurements of Quality Function Deployment method of House of Quality (QFD-HOQ) model and Multiple Criteria Decision Making (MCDM) methodology to cross-evaluate the weighted…

The Value of Decision Analytical Modeling in Surgical Research: An Example of Laparoscopic Versus Open Distal Pancreatectomy.

PubMed

Tax, Casper; Govaert, Paulien H M; Stommel, Martijn W J; Besselink, Marc G H; Gooszen, Hein G; Rovers, Maroeska M

2017-11-02

To illustrate how decision modeling may identify relevant uncertainty and can preclude or identify areas of future research in surgery. To optimize use of research resources, a tool is needed that assists in identifying relevant uncertainties and the added value of reducing these uncertainties. The clinical pathway for laparoscopic distal pancreatectomy (LDP) versus open (ODP) for nonmalignant lesions was modeled in a decision tree. Cost-effectiveness based on complications, hospital stay, costs, quality of life, and survival was analyzed. The effect of existing uncertainty on the cost-effectiveness was addressed, as well as the expected value of eliminating uncertainties. Based on 29 nonrandomized studies (3.701 patients) the model shows that LDP is more cost-effective compared with ODP. Scenarios in which LDP does not outperform ODP for cost-effectiveness seem unrealistic, e.g., a 30-day mortality rate of 1.79 times higher after LDP as compared with ODP, conversion in 62.2%, surgically repair of incisional hernias in 21% after LDP, or an average 2.3 days longer hospital stay after LDP than after ODP. Taking all uncertainty into account, LDP remained more cost-effective. Minimizing these uncertainties did not change the outcome. The results show how decision analytical modeling can help to identify relevant uncertainty and guide decisions for future research in surgery. Based on the current available evidence, a randomized clinical trial on complications, hospital stay, costs, quality of life, and survival is highly unlikely to change the conclusion that LDP is more cost-effective than ODP.

How Qualitative Methods Can be Used to Inform Model Development.

PubMed

Husbands, Samantha; Jowett, Susan; Barton, Pelham; Coast, Joanna

2017-06-01

Decision-analytic models play a key role in informing healthcare resource allocation decisions. However, there are ongoing concerns with the credibility of models. Modelling methods guidance can encourage good practice within model development, but its value is dependent on its ability to address the areas that modellers find most challenging. Further, it is important that modelling methods and related guidance are continually updated in light of any new approaches that could potentially enhance model credibility. The objective of this article was to highlight the ways in which qualitative methods have been used and recommended to inform decision-analytic model development and enhance modelling practices. With reference to the literature, the article discusses two key ways in which qualitative methods can be, and have been, applied. The first approach involves using qualitative methods to understand and inform general and future processes of model development, and the second, using qualitative techniques to directly inform the development of individual models. The literature suggests that qualitative methods can improve the validity and credibility of modelling processes by providing a means to understand existing modelling approaches that identifies where problems are occurring and further guidance is needed. It can also be applied within model development to facilitate the input of experts to structural development. We recommend that current and future model development would benefit from the greater integration of qualitative methods, specifically by studying 'real' modelling processes, and by developing recommendations around how qualitative methods can be adopted within everyday modelling practice.

Analytical Support Capabilities of Turkish General Staff Scientific Decision Support Centre (SDSC) to Defence Transformation

DTIC Science & Technology

2005-04-01

RTO-MP-SAS-055 4 - 1 UNCLASSIFIED/UNLIMITED UNCLASSIFIED/UNLIMITED Analytical Support Capabilities of Turkish General Staff Scientific...the end failed to achieve anything commensurate with the effort. The analytical support capabilities of Turkish Scientific Decision Support Center to...percent of the İpekkan, Z.; Özkil, A. (2005) Analytical Support Capabilities of Turkish General Staff Scientific Decision Support Centre (SDSC) to

Optimizing an immersion ESL curriculum using analytic hierarchy process.

PubMed

Tang, Hui-Wen Vivian

2011-11-01

The main purpose of this study is to fill a substantial knowledge gap regarding reaching a uniform group decision in English curriculum design and planning. A comprehensive content-based course criterion model extracted from existing literature and expert opinions was developed. Analytical hierarchy process (AHP) was used to identify the relative importance of course criteria for the purpose of tailoring an optimal one-week immersion English as a second language (ESL) curriculum for elementary school students in a suburban county of Taiwan. The hierarchy model and AHP analysis utilized in the present study will be useful for resolving several important multi-criteria decision-making issues in planning and evaluating ESL programs. This study also offers valuable insights and provides a basis for further research in customizing ESL curriculum models for different student populations with distinct learning needs, goals, and socioeconomic backgrounds. Copyright © 2011 Elsevier Ltd. All rights reserved.

Hierarchical analytical and simulation modelling of human-machine systems with interference

NASA Astrophysics Data System (ADS)

Braginsky, M. Ya; Tarakanov, D. V.; Tsapko, S. G.; Tsapko, I. V.; Baglaeva, E. A.

2017-01-01

The article considers the principles of building the analytical and simulation model of the human operator and the industrial control system hardware and software. E-networks as the extension of Petri nets are used as the mathematical apparatus. This approach allows simulating complex parallel distributed processes in human-machine systems. The structural and hierarchical approach is used as the building method for the mathematical model of the human operator. The upper level of the human operator is represented by the logical dynamic model of decision making based on E-networks. The lower level reflects psychophysiological characteristics of the human-operator.

An uncertainty analysis of wildfire modeling [Chapter 13

Treesearch

Karin Riley; Matthew Thompson

2017-01-01

Before fire models can be understood, evaluated, and effectively applied to support decision making, model-based uncertainties must be analyzed. In this chapter, we identify and classify sources of uncertainty using an established analytical framework, and summarize results graphically in an uncertainty matrix. Our analysis facilitates characterization of the...

Modelling a flows in supply chain with analytical models: Case of a chemical industry

NASA Astrophysics Data System (ADS)

Benhida, Khalid; Azougagh, Yassine; Elfezazi, Said

2016-02-01

This study is interested on the modelling of the logistics flows in a supply chain composed on a production sites and a logistics platform. The contribution of this research is to develop an analytical model (integrated linear programming model), based on a case study of a real company operating in the phosphate field, considering a various constraints in this supply chain to resolve the planning problems for a better decision-making. The objectives of this model is to determine and define the optimal quantities of different products to route, to and from the various entities in the supply chain studied.

Integration of fuzzy analytic hierarchy process and probabilistic dynamic programming in formulating an optimal fleet management model

NASA Astrophysics Data System (ADS)

Teoh, Lay Eng; Khoo, Hooi Ling

2013-09-01

This study deals with two major aspects of airlines, i.e. supply and demand management. The aspect of supply focuses on the mathematical formulation of an optimal fleet management model to maximize operational profit of the airlines while the aspect of demand focuses on the incorporation of mode choice modeling as parts of the developed model. The proposed methodology is outlined in two-stage, i.e. Fuzzy Analytic Hierarchy Process is first adopted to capture mode choice modeling in order to quantify the probability of probable phenomena (for aircraft acquisition/leasing decision). Then, an optimization model is developed as a probabilistic dynamic programming model to determine the optimal number and types of aircraft to be acquired and/or leased in order to meet stochastic demand during the planning horizon. The findings of an illustrative case study show that the proposed methodology is viable. The results demonstrate that the incorporation of mode choice modeling could affect the operational profit and fleet management decision of the airlines at varying degrees.

The evaluation and enhancement of quality, environmental protection and seaport safety by using FAHP

NASA Astrophysics Data System (ADS)

Tadic, Danijela; Aleksic, Aleksandar; Popovic, Pavle; Arsovski, Slavko; Castelli, Ana; Joksimovic, Danijela; Stefanovic, Miladin

2017-02-01

The evaluation and enhancement of business processes in any organization in an uncertain environment presents one of the main requirements of ISO 9000:2008 and has a key effect on competitive advantage and long-term sustainability. The aim of this paper can be defined as the identification and discussion of some of the most important business processes of seaports and the performances of business processes and their key performance indicators (KPIs). The complexity and importance of the treated problem call for analytic methods rather than intuitive decisions. The existing decision variables of the considered problem are described by linguistic expressions which are modelled by triangular fuzzy numbers (TFNs). In this paper, the modified fuzzy extended analytic hierarchy process (FAHP) is proposed. The assessment of the relative importance of each pair of performances and their key performance indicators are stated as a fuzzy group decision-making problem. By using the modified fuzzy extended analytic hierarchy process, the fuzzy rank of business processes of a seaport is obtained. The model is tested through an illustrative example with real-life data, where the obtained data suggest measures which should enhance business strategy and improve key performance indicators. The future improvement is based on benchmark and knowledge sharing.

Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection

NASA Astrophysics Data System (ADS)

Harwati

2017-06-01

Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.

Decision Making in Adults with ADHD

ERIC Educational Resources Information Center

Montyla, Timo; Still, Johanna; Gullberg, Stina; Del Missier, Fabio

2012-01-01

Objectives: This study examined decision-making competence in ADHD by using multiple decision tasks with varying demands on analytic versus affective processes. Methods: Adults with ADHD and healthy controls completed two tasks of analytic decision making, as measured by the Adult Decision-Making Competence (A-DMC) battery, and two affective…

Extending BPM Environments of Your Choice with Performance Related Decision Support

NASA Astrophysics Data System (ADS)

Fritzsche, Mathias; Picht, Michael; Gilani, Wasif; Spence, Ivor; Brown, John; Kilpatrick, Peter

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Judicial and legislative viewpoints on physician misestimation of patient dysutilities: a problem for decision analysts.

PubMed

Mazur, D J

1990-01-01

Appellate courts, state legislatures, and ethicists have recently (post-1972) been interested-through the evolving court doctrine of informed consent-in patient-physician joint decision making. Yet these professional groups' approaches differ markedly from that of decision analysis, failing to include an explicit role for patients' rational processing of information in informed consent. In addition, these groups charge that decision analysts are misestimating patient dysutilities. This paper examines three issues: 1) in what sense(s), if any, is decision-analytic work in individualized medical decision making misestimating patient dysutilities, 2) if this misestimation is real, whether it is an example of the normative-descriptive tensions that exist in medical decision making, and 3) in what ways do the relationships between decision-analytic and judicial decision making change when informed consent is viewed in terms of contract law as opposed to tort law. This paper argues that a key link dividing these professional groups is the differing weights given to the "value of information" by decision-analytic vs. non-decision-analytic frameworks.

Financial Forecasting and Stochastic Modeling: Predicting the Impact of Business Decisions.

PubMed

Rubin, Geoffrey D; Patel, Bhavik N

2017-05-01

In health care organizations, effective investment of precious resources is critical to assure that the organization delivers high-quality and sustainable patient care within a supportive environment for patients, their families, and the health care providers. This holds true for organizations independent of size, from small practices to large health systems. For radiologists whose role is to oversee the delivery of imaging services and the interpretation, communication, and curation of imaging-informed information, business decisions influence where and how they practice, the tools available for image acquisition and interpretation, and ultimately their professional satisfaction. With so much at stake, physicians must understand and embrace the methods necessary to develop and interpret robust financial analyses so they effectively participate in and better understand decision making. This review discusses the financial drivers upon which health care organizations base investment decisions and the central role that stochastic financial modeling should play in support of strategically aligned capital investments. Given a health care industry that has been slow to embrace advanced financial analytics, a fundamental message of this review is that the skills and analytical tools are readily attainable and well worth the effort to implement in the interest of informed decision making. © RSNA, 2017 Online supplemental material is available for this article.

Pitting intuitive and analytical thinking against each other: the case of transitivity.

PubMed

Rusou, Zohar; Zakay, Dan; Usher, Marius

2013-06-01

Identifying which thinking mode, intuitive or analytical, yields better decisions has been a major subject of inquiry by decision-making researchers. Yet studies show contradictory results. One possibility is that the ambiguity is due to the variability in experimental conditions across studies. Our hypothesis is that decision quality depends critically on the level of compatibility between the thinking mode employed in the decision and the nature of the decision-making task. In two experiments, we pitted intuition and analytical thinking against each other on tasks that were either mainly intuitive or mainly analytical. Thinking modes, as well as task characteristics, were manipulated in a factorial design, with choice transitivity as the dependent measure. Results showed higher choice consistency (transitivity) when thinking mode and the characteristics of the decision task were compatible.

Empirical Evaluation of a Decision-Analytic Aid.

DTIC Science & Technology

1980-05-01

scenarios may be attributable to the use of the Baye- sian revision model by the latter group . In the A scenarios, as well as in the NA scenarios, aided...inten- tions and to make a decision by recommending one of four prespecified courses of action. The use of the aiding package significantly increased...courses of action. The use of the aiding package significantly in- I creased the number of correct decisions under the attack version of the scenarios

Role of scientific data in health decisions.

PubMed Central

Samuels, S W

1979-01-01

The distinction between reality and models or methodological assumptions is necessary for an understanding of the use of data--economic, technical or biological--in decision-making. The traditional modes of analysis used in decisions are discussed historically and analytically. Utilitarian-based concepts such as cost-benefit analysis and cannibalistic concepts such as "acceptable risk" are rejected on logical and moral grounds. Historical reality suggests the concept of socially necessary risk determined through the dialectic process in democracy. PMID:120251

A Comparison of Four Software Programs for Implementing Decision Analytic Cost-Effectiveness Models.

PubMed

Hollman, Chase; Paulden, Mike; Pechlivanoglou, Petros; McCabe, Christopher

2017-08-01

The volume and technical complexity of both academic and commercial research using decision analytic modelling has increased rapidly over the last two decades. The range of software programs used for their implementation has also increased, but it remains true that a small number of programs account for the vast majority of cost-effectiveness modelling work. We report a comparison of four software programs: TreeAge Pro, Microsoft Excel, R and MATLAB. Our focus is on software commonly used for building Markov models and decision trees to conduct cohort simulations, given their predominance in the published literature around cost-effectiveness modelling. Our comparison uses three qualitative criteria as proposed by Eddy et al.: "transparency and validation", "learning curve" and "capability". In addition, we introduce the quantitative criterion of processing speed. We also consider the cost of each program to academic users and commercial users. We rank the programs based on each of these criteria. We find that, whilst Microsoft Excel and TreeAge Pro are good programs for educational purposes and for producing the types of analyses typically required by health technology assessment agencies, the efficiency and transparency advantages of programming languages such as MATLAB and R become increasingly valuable when more complex analyses are required.

Big Data Architectures for Operationalized Seismic and Subsurface Monitoring and Decision Support Workflows

NASA Astrophysics Data System (ADS)

Irving, D. H.; Rasheed, M.; Hillman, C.; O'Doherty, N.

2012-12-01

Oilfield management is moving to a more operational footing with near-realtime seismic and sensor monitoring governing drilling, fluid injection and hydrocarbon extraction workflows within safety, productivity and profitability constraints. To date, the geoscientific analytical architectures employed are configured for large volumes of data, computational power or analytical latency and compromises in system design must be made to achieve all three aspects. These challenges are encapsulated by the phrase 'Big Data' which has been employed for over a decade in the IT industry to describe the challenges presented by data sets that are too large, volatile and diverse for existing computational architectures and paradigms. We present a data-centric architecture developed to support a geoscientific and geotechnical workflow whereby: ●scientific insight is continuously applied to fresh data ●insights and derived information are incorporated into engineering and operational decisions ●data governance and provenance are routine within a broader data management framework Strategic decision support systems in large infrastructure projects such as oilfields are typically relational data environments; data modelling is pervasive across analytical functions. However, subsurface data and models are typically non-relational (i.e. file-based) in the form of large volumes of seismic imaging data or rapid streams of sensor feeds and are analysed and interpreted using niche applications. The key architectural challenge is to move data and insight from a non-relational to a relational, or structured, data environment for faster and more integrated analytics. We describe how a blend of MapReduce and relational database technologies can be applied in geoscientific decision support, and the strengths and weaknesses of each in such an analytical ecosystem. In addition we discuss hybrid technologies that use aspects of both and translational technologies for moving data and analytics across these platforms. Moving to a data-centric architecture requires data management methodologies to be overhauled by default and we show how end-to-end data provenancing and dependency management is implicit in such an environment and how it benefits system administration as well as the user community. Whilst the architectural experiences are drawn from the oil industry, we believe that they are more broadly applicable in academic and government settings where large volumes of data are added to incrementally and require revisiting with low analytical latency and we suggest application to earthquake monitoring and remote sensing networks.

Assessing the Rigor of HS Curriculum in Admissions Decisions: A Functional Method, Plus Practical Advising for Prospective Students and High School Counselors

ERIC Educational Resources Information Center

Micceri, Theodore; Brigman, Leellen; Spatig, Robert

2009-01-01

An extensive, internally cross-validated analytical study using nested (within academic disciplines) Multilevel Modeling (MLM) on 4,560 students identified functional criteria for defining high school curriculum rigor and further determined which measures could best be used to help guide decision making for marginal applicants. The key outcome…

Equilibrium relations and bipolar cognitive mapping for online analytical processing with applications in international relations and strategic decision support.

PubMed

Zhang, Wen-Ran

2003-01-01

Bipolar logic, bipolar sets, and equilibrium relations are proposed for bipolar cognitive mapping and visualization in online analytical processing (OLAP) and online analytical mining (OLAM). As cognitive models, cognitive maps (CMs) hold great potential for clustering and visualization. Due to the lack of a formal mathematical basis, however, CM-based OLAP and OLAM have not gained popularity. Compared with existing approaches, bipolar cognitive mapping has a number of advantages. First, bipolar CMs are formal logical models as well as cognitive models. Second, equilibrium relations (with polarized reflexivity, symmetry, and transitivity), as bipolar generalizations and fusions of equivalence relations, provide a theoretical basis for bipolar visualization and coordination. Third, an equilibrium relation or CM induces bipolar partitions that distinguish disjoint coalition subsets not involved in any conflict, disjoint coalition subsets involved in a conflict, disjoint conflict subsets, and disjoint harmony subsets. Finally, equilibrium energy analysis leads to harmony and stability measures for strategic decision and multiagent coordination. Thus, this work bridges a gap for CM-based clustering and visualization in OLAP and OLAM. Basic ideas are illustrated with example CMs in international relations.

[Basic research on digital logistic management of hospital].

PubMed

Cao, Hui

2010-05-01

This paper analyzes and explores the possibilities of digital information-based management realized by equipment department, general services department, supply room and other material flow departments in different hospitals in order to optimize the procedures of information-based asset management. There are various analytical methods of medical supplies business models, providing analytical data for correct decisions made by departments and leaders of hospital and the governing authorities.

Interacting neural networks.

PubMed

Metzler, R; Kinzel, W; Kanter, I

2000-08-01

Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

Interacting neural networks

NASA Astrophysics Data System (ADS)

Metzler, R.; Kinzel, W.; Kanter, I.

2000-08-01

Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

E-HOSPITAL - A Digital Workbench for Hospital Operations and Services Planning Using Information Technology and Algebraic Languages.

PubMed

Gartner, Daniel; Padman, Rema

2017-01-01

In this paper, we describe the development of a unified framework and a digital workbench for the strategic, tactical and operational hospital management plan driven by information technology and analytics. The workbench can be used not only by multiple stakeholders in the healthcare delivery setting, but also for pedagogical purposes on topics such as healthcare analytics, services management, and information systems. This tool combines the three classical hierarchical decision-making levels in one integrated environment. At each level, several decision problems can be chosen. Extensions of mathematical models from the literature are presented and incorporated into the digital platform. In a case study using real-world data, we demonstrate how we used the workbench to inform strategic capacity planning decisions in a multi-hospital, multi-stakeholder setting in the United Kingdom.

Deciding about fast and slow decisions.

PubMed

Croskerry, Pat; Petrie, David A; Reilly, James B; Tait, Gordon

2014-02-01

Two reports in this issue address the important topic of clinical decision making. Dual process theory has emerged as the dominant model for understanding the complex processes that underlie human decision making. This theory distinguishes between the reflexive, autonomous processes that characterize intuitive decision making and the deliberate reasoning of an analytical approach. In this commentary, the authors address the polarization of viewpoints that has developed around the relative merits of the two systems. Although intuitive processes are typically fast and analytical processes slow, speed alone does not distinguish them. In any event, the majority of decisions in clinical medicine are not dependent on very short response times. What does appear relevant to diagnostic ease and accuracy is the degree to which the symptoms of the disease being diagnosed are characteristic ones. There are also concerns around some methodological issues related to research design in this area of enquiry. Reductionist approaches that attempt to isolate dependent variables may create such artificial experimental conditions that both external and ecological validity are sacrificed. Clinical decision making is a complex process with many independent (and interdependent) variables that need to be separated out in a discrete fashion and then reflected on in real time to preserve the fidelity of clinical practice. With these caveats in mind, the authors believe that research in this area should promote a better understanding of clinical practice and teaching by focusing less on the deficiencies of intuitive and analytical systems and more on their adaptive strengths.

Analytic innovations for air quality modeling

EPA Science Inventory

The presentation provides an overview of ongoing research activities at the U.S. EPA, focusing on improving long-term emission projections and the development of decision support systems for coordinated environmental, climate and energy planning.

Modelling Complexity: Making Sense of Leadership Issues in 14-19 Education

ERIC Educational Resources Information Center

Briggs, Ann R. J.

2008-01-01

Modelling of statistical data is a well established analytical strategy. Statistical data can be modelled to represent, and thereby predict, the forces acting upon a structure or system. For the rapidly changing systems in the world of education, modelling enables the researcher to understand, to predict and to enable decisions to be based upon…

Sensitivity Analysis in Sequential Decision Models.

PubMed

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

Decision Support Model for Municipal Solid Waste Management at Department of Defense Installations.

DTIC Science & Technology

1995-12-01

Huang uses "Grey Dynamic Programming for Waste Management Planning Under Uncertainty." Fuzzy Dynamic Programming (FDP) is usually designed to...and Composting Programs. Washington: Island Press, 1991. Junio, D.F. Development of an Analytical Hierarchy Process ( AHP ) Model for Siting of

From accuracy to patient outcome and cost-effectiveness evaluations of diagnostic tests and biomarkers: an exemplary modelling study

PubMed Central

2013-01-01

Background Proper evaluation of new diagnostic tests is required to reduce overutilization and to limit potential negative health effects and costs related to testing. A decision analytic modelling approach may be worthwhile when a diagnostic randomized controlled trial is not feasible. We demonstrate this by assessing the cost-effectiveness of modified transesophageal echocardiography (TEE) compared with manual palpation for the detection of atherosclerosis in the ascending aorta. Methods Based on a previous diagnostic accuracy study, actual Dutch reimbursement data, and evidence from literature we developed a Markov decision analytic model. Cost-effectiveness of modified TEE was assessed for a life time horizon and a health care perspective. Prevalence rates of atherosclerosis were age-dependent and low as well as high rates were applied. Probabilistic sensitivity analysis was applied. Results The model synthesized all available evidence on the risk of stroke in cardiac surgery patients. The modified TEE strategy consistently resulted in more adapted surgical procedures and, hence, a lower risk of stroke and a slightly higher number of life-years. With 10% prevalence of atherosclerosis the incremental cost-effectiveness ratio was €4,651 and €481 per quality-adjusted life year in 55-year-old men and women, respectively. In all patients aged 65 years or older the modified TEE strategy was cost saving and resulted in additional health benefits. Conclusions Decision analytic modelling to assess the cost-effectiveness of a new diagnostic test based on characteristics, costs and effects of the test itself and of the subsequent treatment options is both feasible and valuable. Our case study on modified TEE suggests that it may reduce the risk of stroke in cardiac surgery patients older than 55 years at acceptable cost-effectiveness levels. PMID:23368927

Big data and high-performance analytics in structural health monitoring for bridge management

NASA Astrophysics Data System (ADS)

Alampalli, Sharada; Alampalli, Sandeep; Ettouney, Mohammed

2016-04-01

Structural Health Monitoring (SHM) can be a vital tool for effective bridge management. Combining large data sets from multiple sources to create a data-driven decision-making framework is crucial for the success of SHM. This paper presents a big data analytics framework that combines multiple data sets correlated with functional relatedness to convert data into actionable information that empowers risk-based decision-making. The integrated data environment incorporates near real-time streams of semi-structured data from remote sensors, historical visual inspection data, and observations from structural analysis models to monitor, assess, and manage risks associated with the aging bridge inventories. Accelerated processing of dataset is made possible by four technologies: cloud computing, relational database processing, support from NOSQL database, and in-memory analytics. The framework is being validated on a railroad corridor that can be subjected to multiple hazards. The framework enables to compute reliability indices for critical bridge components and individual bridge spans. In addition, framework includes a risk-based decision-making process that enumerate costs and consequences of poor bridge performance at span- and network-levels when rail networks are exposed to natural hazard events such as floods and earthquakes. Big data and high-performance analytics enable insights to assist bridge owners to address problems faster.

Hemispheric Activation Differences in Novice and Expert Clinicians during Clinical Decision Making

ERIC Educational Resources Information Center

Hruska, Pam; Hecker, Kent G.; Coderre, Sylvain; McLaughlin, Kevin; Cortese, Filomeno; Doig, Christopher; Beran, Tanya; Wright, Bruce; Krigolson, Olav

2016-01-01

Clinical decision making requires knowledge, experience and analytical/non-analytical types of decision processes. As clinicians progress from novice to expert, research indicates decision-making becomes less reliant on foundational biomedical knowledge and more on previous experience. In this study, we investigated how knowledge and experience…

Decision models for capital investment and financing decisions in hospitals.

PubMed Central

Vraciu, R A

1980-01-01

The literature on capital investment and financing decisions for hospitals has suggested several approaches to analyzing sets of options. In this paper, I present a taxonomy of the different approaches; analyze and compare the different elements of the taxonomy; and illustrate and discuss the information that can be gained by using each approach. I view these different analytic methods as complementary rather than competing methods of providing information to decision makers, and argue that the complex nature of hospital objectives demands the use of more than one approach. Failure to do this may lead to biased evaluations and poor decision making. PMID:6768699

Predicting Team Performance through Human Behavioral Sensing and Quantitative Workflow Instrumentation

DTIC Science & Technology

2016-07-27

make risk-informed decisions during serious games . Statistical models of intra- game performance were developed to determine whether behaviors in...specific facets of the gameplay workflow were predictive of analytical performance and games outcomes. A study of over seventy instrumented teams revealed...more accurate game decisions. 2 Keywords: Humatics · Serious Games · Human-System Interaction · Instrumentation · Teamwork · Communication Analysis

An Analytic Model for DoD Investment & Divestment Decisions (Briefing Charts)

DTIC Science & Technology

2015-05-01

cost drives Strategic; Performance mixed; cost drives Invest Insurance Risk Mitigation √ “Making Trade-Offs in Corporate Portfolio Decisions...Effects (SE) + Insurance Intended externalities, unintended consequences Are SE measureable? Do they add/subtract so NPV is worthwhile? Deadweight...Sustainable Cost Effective Cost is supportable, LPO outsourced, or is income Advantageous NPV? Y Y Y Y either N N N N Secondary Effects+ Insurance

Development of a Suite of Analytical Tools for Energy and Water Infrastructure Knowledge Discovery

NASA Astrophysics Data System (ADS)

Morton, A.; Piburn, J.; Stewart, R.; Chandola, V.

2017-12-01

Energy and water generation and delivery systems are inherently interconnected. With demand for energy growing, the energy sector is experiencing increasing competition for water. With increasing population and changing environmental, socioeconomic, and demographic scenarios, new technology and investment decisions must be made for optimized and sustainable energy-water resource management. This also requires novel scientific insights into the complex interdependencies of energy-water infrastructures across multiple space and time scales. To address this need, we've developed a suite of analytical tools to support an integrated data driven modeling, analysis, and visualization capability for understanding, designing, and developing efficient local and regional practices related to the energy-water nexus. This work reviews the analytical capabilities available along with a series of case studies designed to demonstrate the potential of these tools for illuminating energy-water nexus solutions and supporting strategic (federal) policy decisions.

Warfighter decision making performance analysis as an investment priority driver

NASA Astrophysics Data System (ADS)

Thornley, David J.; Dean, David F.; Kirk, James C.

2010-04-01

Estimating the relative value of alternative tactics, techniques and procedures (TTP) and information systems requires measures of the costs and benefits of each, and methods for combining and comparing those measures. The NATO Code of Best Practice for Command and Control Assessment explains that decision making quality would ideally be best assessed on outcomes. Lessons learned in practice can be assessed statistically to support this, but experimentation with alternate measures in live conflict is undesirable. To this end, the development of practical experimentation to parameterize effective constructive simulation and analytic modelling for system utility prediction is desirable. The Land Battlespace Systems Department of Dstl has modeled human development of situational awareness to support constructive simulation by empirically discovering how evidence is weighed according to circumstance, personality, training and briefing. The human decision maker (DM) provides the backbone of the information processing activity associated with military engagements because of inherent uncertainty associated with combat operations. To develop methods for representing the process in order to assess equipment and non-technological interventions such as training and TTPs we are developing componentized or modularized timed analytic stochastic model components and instruments as part of a framework to support quantitative assessment of intelligence production and consumption methods in a human decision maker-centric mission space. In this paper, we formulate an abstraction of the human intelligence fusion process from the Defence Science and Technology Laboratory's (Dstl's) INCIDER model to include in our framework, and synthesize relevant cost and benefit characteristics.

The Funding of Virtual Universities

ERIC Educational Resources Information Center

Poulin, Russell; Michelau, Demaree K.

2009-01-01

This article reviews categorization models and the outcomes of a virtual university funding survey. Although categorization of types of funding mechanisms is a necessary analytical tool, it often hides the many and varied political decisions that created them. In commenting on the implications of the type of funding model, political forces behind…

Sample Size and Power Estimates for a Confirmatory Factor Analytic Model in Exercise and Sport: A Monte Carlo Approach

ERIC Educational Resources Information Center

Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying

2011-01-01

Monte Carlo methods can be used in data analytic situations (e.g., validity studies) to make decisions about sample size and to estimate power. The purpose of using Monte Carlo methods in a validity study is to improve the methodological approach within a study where the primary focus is on construct validity issues and not on advancing…

Developing an Analytical Framework: Incorporating Ecosystem Services into Decision Making - Proceedings of a Workshop

USGS Publications Warehouse

Hogan, Dianna; Arthaud, Greg; Pattison, Malka; Sayre, Roger G.; Shapiro, Carl

2010-01-01

The analytical framework for understanding ecosystem services in conservation, resource management, and development decisions is multidisciplinary, encompassing a combination of the natural and social sciences. This report summarizes a workshop on 'Developing an Analytical Framework: Incorporating Ecosystem Services into Decision Making,' which focused on the analytical process and on identifying research priorities for assessing ecosystem services, their production and use, their spatial and temporal characteristics, their relationship with natural systems, and their interdependencies. Attendees discussed research directions and solutions to key challenges in developing the analytical framework. The discussion was divided into two sessions: (1) the measurement framework: quantities and values, and (2) the spatial framework: mapping and spatial relationships. This workshop was the second of three preconference workshops associated with ACES 2008 (A Conference on Ecosystem Services): Using Science for Decision Making in Dynamic Systems. These three workshops were designed to explore the ACES 2008 theme on decision making and how the concept of ecosystem services can be more effectively incorporated into conservation, restoration, resource management, and development decisions. Preconference workshop 1, 'Developing a Vision: Incorporating Ecosystem Services into Decision Making,' was held on April 15, 2008, in Cambridge, MA. In preconference workshop 1, participants addressed what would have to happen to make ecosystem services be used more routinely and effectively in conservation, restoration, resource management, and development decisions, and they identified some key challenges in developing the analytical framework. Preconference workshop 3, 'Developing an Institutional Framework: Incorporating Ecosystem Services into Decision Making,' was held on October 30, 2008, in Albuquerque, NM; participants examined the relationship between the institutional framework and the use of ecosystem services in decision making.

A MDMP for All Seasons: Modifying the MDMP for Success

DTIC Science & Technology

2004-05-26

4 Rational Decision - Making Theory ............................................................................. 5 Limited Rationality ... making instead of using the MDMP, which is an analytical decision - making process. Limited rationality and analytical decision - making will be discussed...limited rationality decision - making theories. FM 5.0 defines fundamentals of planning, such as commander’s involvement and developing creative plans

INTEGRATING DATA ANALYTICS AND SIMULATION METHODS TO SUPPORT MANUFACTURING DECISION MAKING

PubMed Central

Kibira, Deogratias; Hatim, Qais; Kumara, Soundar; Shao, Guodong

2017-01-01

Modern manufacturing systems are installed with smart devices such as sensors that monitor system performance and collect data to manage uncertainties in their operations. However, multiple parameters and variables affect system performance, making it impossible for a human to make informed decisions without systematic methodologies and tools. Further, the large volume and variety of streaming data collected is beyond simulation analysis alone. Simulation models are run with well-prepared data. Novel approaches, combining different methods, are needed to use this data for making guided decisions. This paper proposes a methodology whereby parameters that most affect system performance are extracted from the data using data analytics methods. These parameters are used to develop scenarios for simulation inputs; system optimizations are performed on simulation data outputs. A case study of a machine shop demonstrates the proposed methodology. This paper also reviews candidate standards for data collection, simulation, and systems interfaces. PMID:28690363

Prescriptive models to support decision making in genetics.

PubMed

Pauker, S G; Pauker, S P

1987-01-01

Formal prescriptive models can help patients and clinicians better understand the risks and uncertainties they face and better formulate well-reasoned decisions. Using Bayes rule, the clinician can interpret pedigrees, historical data, physical findings and laboratory data, providing individualized probabilities of various diagnoses and outcomes of pregnancy. With the advent of screening programs for genetic disease, it becomes increasingly important to consider the prior probabilities of disease when interpreting an abnormal screening test result. Decision trees provide a convenient formalism for structuring diagnostic, therapeutic and reproductive decisions; such trees can also enhance communication between clinicians and patients. Utility theory provides a mechanism for patients to understand the choices they face and to communicate their attitudes about potential reproductive outcomes in a manner which encourages the integration of those attitudes into appropriate decisions. Using a decision tree, the relevant probabilities and the patients' utilities, physicians can estimate the relative worth of various medical and reproductive options by calculating the expected utility of each. By performing relevant sensitivity analyses, clinicians and patients can understand the impact of various soft data, including the patients' attitudes toward various health outcomes, on the decision making process. Formal clinical decision analytic models can provide deeper understanding and improved decision making in clinical genetics.

Facilities Stewardship: Measuring the Return on Physical Assets.

ERIC Educational Resources Information Center

Kadamus, David A.

2001-01-01

Asserts that colleges and universities should apply the same analytical rigor to physical assets as they do financial assets. Presents a management tool, the Return on Physical Assets model, to help guide physical asset allocation decisions. (EV)

Mathematical modeling and pharmaceutical pricing: analyses used to inform in-licensing and developmental go/No-Go decisions.

PubMed

Vernon, John A; Hughen, W Keener; Johnson, Scott J

2005-05-01

In the face of significant real healthcare cost inflation, pressured budgets, and ongoing launches of myriad technology of uncertain value, payers have formalized new valuation techniques that represent a barrier to entry for drugs. Cost-effectiveness analysis predominates among these methods, which involves differencing a new technological intervention's marginal costs and benefits with a comparator's, and comparing the resulting ratio to a payer's willingness-to-pay threshold. In this paper we describe how firms are able to model the feasible range of future product prices when making in-licensing and developmental Go/No-Go decisions by considering payers' use of the cost-effectiveness method. We illustrate this analytic method with a simple deterministic example and then incorporate stochastic assumptions using both analytic and simulation methods. Using this strategic approach, firms may reduce product development and in-licensing risk.

An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory.

PubMed

Pelaccia, Thierry; Tardif, Jacques; Triby, Emmanuel; Charlin, Bernard

2011-03-14

Clinical reasoning plays a major role in the ability of doctors to make diagnoses and decisions. It is considered as the physician's most critical competence, and has been widely studied by physicians, educationalists, psychologists and sociologists. Since the 1970s, many theories about clinical reasoning in medicine have been put forward. This paper aims at exploring a comprehensive approach: the "dual-process theory", a model developed by cognitive psychologists over the last few years. After 40 years of sometimes contradictory studies on clinical reasoning, the dual-process theory gives us many answers on how doctors think while making diagnoses and decisions. It highlights the importance of physicians' intuition and the high level of interaction between analytical and non-analytical processes. However, it has not received much attention in the medical education literature. The implications of dual-process models of reasoning in terms of medical education will be discussed.

Evaluation of markers and risk prediction models: Overview of relationships between NRI and decision-analytic measures

PubMed Central

Calster, Ben Van; Vickers, Andrew J; Pencina, Michael J; Baker, Stuart G; Timmerman, Dirk; Steyerberg, Ewout W

2014-01-01

BACKGROUND For the evaluation and comparison of markers and risk prediction models, various novel measures have recently been introduced as alternatives to the commonly used difference in the area under the ROC curve (ΔAUC). The Net Reclassification Improvement (NRI) is increasingly popular to compare predictions with one or more risk thresholds, but decision-analytic approaches have also been proposed. OBJECTIVE We aimed to identify the mathematical relationships between novel performance measures for the situation that a single risk threshold T is used to classify patients as having the outcome or not. METHODS We considered the NRI and three utility-based measures that take misclassification costs into account: difference in Net Benefit (ΔNB), difference in Relative Utility (ΔRU), and weighted NRI (wNRI). We illustrate the behavior of these measures in 1938 women suspect of ovarian cancer (prevalence 28%). RESULTS The three utility-based measures appear transformations of each other, and hence always lead to consistent conclusions. On the other hand, conclusions may differ when using the standard NRI, depending on the adopted risk threshold T, prevalence P and the obtained differences in sensitivity and specificity of the two models that are compared. In the case study, adding the CA-125 tumor marker to a baseline set of covariates yielded a negative NRI yet a positive value for the utility-based measures. CONCLUSIONS The decision-analytic measures are each appropriate to indicate the clinical usefulness of an added marker or compare prediction models, since these measures each reflect misclassification costs. This is of practical importance as these measures may thus adjust conclusions based on purely statistical measures. A range of risk thresholds should be considered in applying these measures. PMID:23313931

A Systematic Review of Health Economics Simulation Models of Chronic Obstructive Pulmonary Disease.

PubMed

Zafari, Zafar; Bryan, Stirling; Sin, Don D; Conte, Tania; Khakban, Rahman; Sadatsafavi, Mohsen

2017-01-01

Many decision-analytic models with varying structures have been developed to inform resource allocation in chronic obstructive pulmonary disease (COPD). To review COPD models for their adherence to the best practice modeling recommendations and their assumptions regarding important aspects of the natural history of COPD. A systematic search of English articles reporting on the development or application of a decision-analytic model in COPD was performed in MEDLINE, Embase, and citations within reviewed articles. Studies were summarized and evaluated on the basis of their adherence to the Consolidated Health Economic Evaluation Reporting Standards. They were also evaluated for the underlying assumptions about disease progression, heterogeneity, comorbidity, and treatment effects. Forty-nine models of COPD were included. Decision trees and Markov models were the most popular techniques (43 studies). Quality of reporting and adherence to the guidelines were generally high, especially in more recent publications. Disease progression was modeled through clinical staging in most studies. Although most studies (n = 43) had incorporated some aspects of COPD heterogeneity, only 8 reported the results across subgroups. Only 2 evaluations explicitly considered the impact of comorbidities. Treatment effect had been mostly modeled (20) as both reduction in exacerbation rate and improvement in lung function. Many COPD models have been developed, generally with similar structural elements. COPD is highly heterogeneous, and comorbid conditions play an important role in its burden. These important aspects, however, have not been adequately addressed in most of the published models. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Cost-Effectiveness of HBV and HCV Screening Strategies – A Systematic Review of Existing Modelling Techniques

PubMed Central

Geue, Claudia; Wu, Olivia; Xin, Yiqiao; Heggie, Robert; Hutchinson, Sharon; Martin, Natasha K.; Fenwick, Elisabeth; Goldberg, David

2015-01-01

Introduction Studies evaluating the cost-effectiveness of screening for Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) are generally heterogeneous in terms of risk groups, settings, screening intervention, outcomes and the economic modelling framework. It is therefore difficult to compare cost-effectiveness results between studies. This systematic review aims to summarise and critically assess existing economic models for HBV and HCV in order to identify the main methodological differences in modelling approaches. Methods A structured search strategy was developed and a systematic review carried out. A critical assessment of the decision-analytic models was carried out according to the guidelines and framework developed for assessment of decision-analytic models in Health Technology Assessment of health care interventions. Results The overall approach to analysing the cost-effectiveness of screening strategies was found to be broadly consistent for HBV and HCV. However, modelling parameters and related structure differed between models, producing different results. More recent publications performed better against a performance matrix, evaluating model components and methodology. Conclusion When assessing screening strategies for HBV and HCV infection, the focus should be on more recent studies, which applied the latest treatment regimes, test methods and had better and more complete data on which to base their models. In addition to parameter selection and associated assumptions, careful consideration of dynamic versus static modelling is recommended. Future research may want to focus on these methodological issues. In addition, the ability to evaluate screening strategies for multiple infectious diseases, (HCV and HIV at the same time) might prove important for decision makers. PMID:26689908

The Students Decision Making in Solving Discount Problem

ERIC Educational Resources Information Center

Abdillah; Nusantara, Toto; Subanji; Susanto, Hery; Abadyo

2016-01-01

This research is reviewing students' process of decision making intuitively, analytically, and interactively. The research done by using discount problem which specially created to explore student's intuition, analytically, and interactively. In solving discount problems, researcher exploring student's decision in determining their attitude which…

Evaluation and purchase of an analytical flow cytometer: some of the numerous factors to consider.

PubMed

Zucker, Robert M; Fisher, Nancy C

2013-01-01

When purchasing a flow cytometer, the decision of which brand, model, specifications, and accessories may be challenging. The decisions should initially be guided by the specific applications intended for the instrument. However, many other factors need to be considered, which include hardware, software, quality assurance, support, service, and price and recommendations from colleagues. These issues are discussed to help guide the purchasing process.

Decision curve analysis: a novel method for evaluating prediction models.

PubMed

Vickers, Andrew J; Elkin, Elena B

2006-01-01

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.

Multiscale modelling and analysis of collective decision making in swarm robotics.

PubMed

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

Model and Analytic Processes for Export License Assessments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, Sandra E.; Whitney, Paul D.; Weimar, Mark R.

2011-09-29

This paper represents the Department of Energy Office of Nonproliferation Research and Development (NA-22) Simulations, Algorithms and Modeling (SAM) Program's first effort to identify and frame analytical methods and tools to aid export control professionals in effectively predicting proliferation intent; a complex, multi-step and multi-agency process. The report focuses on analytical modeling methodologies that alone, or combined, may improve the proliferation export control license approval process. It is a follow-up to an earlier paper describing information sources and environments related to international nuclear technology transfer. This report describes the decision criteria used to evaluate modeling techniques and tools to determinemore » which approaches will be investigated during the final 2 years of the project. The report also details the motivation for why new modeling techniques and tools are needed. The analytical modeling methodologies will enable analysts to evaluate the information environment for relevance to detecting proliferation intent, with specific focus on assessing risks associated with transferring dual-use technologies. Dual-use technologies can be used in both weapons and commercial enterprises. A decision-framework was developed to evaluate which of the different analytical modeling methodologies would be most appropriate conditional on the uniqueness of the approach, data availability, laboratory capabilities, relevance to NA-22 and Office of Arms Control and Nonproliferation (NA-24) research needs and the impact if successful. Modeling methodologies were divided into whether they could help micro-level assessments (e.g., help improve individual license assessments) or macro-level assessment. Macro-level assessment focuses on suppliers, technology, consumers, economies, and proliferation context. Macro-level assessment technologies scored higher in the area of uniqueness because less work has been done at the macro level. An approach to developing testable hypotheses for the macro-level assessment methodologies is provided. The outcome of this works suggests that we should develop a Bayes Net for micro-level analysis and continue to focus on Bayes Net, System Dynamics and Economic Input/Output models for assessing macro-level problems. Simultaneously, we need to develop metrics for assessing intent in export control, including the risks and consequences associated with all aspects of export control.« less

Decision science and cervical cancer.

PubMed

Cantor, Scott B; Fahs, Marianne C; Mandelblatt, Jeanne S; Myers, Evan R; Sanders, Gillian D

2003-11-01

Mathematical modeling is an effective tool for guiding cervical cancer screening, diagnosis, and treatment decisions for patients and policymakers. This article describes the use of mathematical modeling as outlined in five presentations from the Decision Science and Cervical Cancer session of the Second International Conference on Cervical Cancer held at The University of Texas M. D. Anderson Cancer Center, April 11-14, 2002. The authors provide an overview of mathematical modeling, especially decision analysis and cost-effectiveness analysis, and examples of how it can be used for clinical decision making regarding the prevention, diagnosis, and treatment of cervical cancer. Included are applications as well as theory regarding decision science and cervical cancer. Mathematical modeling can answer such questions as the optimal frequency for screening, the optimal age to stop screening, and the optimal way to diagnose cervical cancer. Results from one mathematical model demonstrated that a vaccine against high-risk strains of human papillomavirus was a cost-effective use of resources, and discussion of another model demonstrated the importance of collecting direct non-health care costs and time costs for cost-effectiveness analysis. Research presented indicated that care must be taken when applying the results of population-wide, cost-effectiveness analyses to reduce health disparities. Mathematical modeling can encompass a variety of theoretical and applied issues regarding decision science and cervical cancer. The ultimate objective of using decision-analytic and cost-effectiveness models is to identify ways to improve women's health at an economically reasonable cost. Copyright 2003 American Cancer Society.

Strategic enterprise resource planning in a health-care system using a multicriteria decision-making model.

PubMed

Lee, Chang Won; Kwak, N K

2011-04-01

This paper deals with strategic enterprise resource planning (ERP) in a health-care system using a multicriteria decision-making (MCDM) model. The model is developed and analyzed on the basis of the data obtained from a leading patient-oriented provider of health-care services in Korea. Goal criteria and priorities are identified and established via the analytic hierarchy process (AHP). Goal programming (GP) is utilized to derive satisfying solutions for designing, evaluating, and implementing an ERP. The model results are evaluated and sensitivity analyses are conducted in an effort to enhance the model applicability. The case study provides management with valuable insights for planning and controlling health-care activities and services.

Flat-plate solar array project. Volume 8: Project analysis and integration

NASA Technical Reports Server (NTRS)

Mcguire, P.; Henry, P.

1986-01-01

Project Analysis and Integration (PA&I) performed planning and integration activities to support management of the various Flat-Plate Solar Array (FSA) Project R&D activities. Technical and economic goals were established by PA&I for each R&D task within the project to coordinate the thrust toward the National Photovoltaic Program goals. A sophisticated computer modeling capability was developed to assess technical progress toward meeting the economic goals. These models included a manufacturing facility simulation, a photovoltaic power station simulation and a decision aid model incorporating uncertainty. This family of analysis tools was used to track the progress of the technology and to explore the effects of alternative technical paths. Numerous studies conducted by PA&I signaled the achievement of milestones or were the foundation of major FSA project and national program decisions. The most important PA&I activities during the project history are summarized. The PA&I planning function is discussed and how it relates to project direction and important analytical models developed by PA&I for its analytical and assessment activities are reviewed.

Research | Argonne National Laboratory

Science.gov Websites

, and Decision Analytics Energy Systems Analysis Engines and Fuels Friction, Wear, and Lubrication Vehicle Technologies Buildings and Climate-Environment Energy, Power, and Decision Analytics Energy

Physics-based and human-derived information fusion for analysts

NASA Astrophysics Data System (ADS)

Blasch, Erik; Nagy, James; Scott, Steve; Okoth, Joshua; Hinman, Michael

2017-05-01

Recent trends in physics-based and human-derived information fusion (PHIF) have amplified the capabilities of analysts; however with the big data opportunities there is a need for open architecture designs, methods of distributed team collaboration, and visualizations. In this paper, we explore recent trends in the information fusion to support user interaction and machine analytics. Challenging scenarios requiring PHIF include combing physics-based video data with human-derived text data for enhanced simultaneous tracking and identification. A driving effort would be to provide analysts with applications, tools, and interfaces that afford effective and affordable solutions for timely decision making. Fusion at scale should be developed to allow analysts to access data, call analytics routines, enter solutions, update models, and store results for distributed decision making.

Eliciting expert opinion for economic models: an applied example.

PubMed

Leal, José; Wordsworth, Sarah; Legood, Rosa; Blair, Edward

2007-01-01

Expert opinion is considered as a legitimate source of information for decision-analytic modeling where required data are unavailable. Our objective was to develop a practical computer-based tool for eliciting expert opinion about the shape of the uncertainty distribution around individual model parameters. We first developed a prepilot survey with departmental colleagues to test a number of alternative approaches to eliciting opinions on the shape of the uncertainty distribution around individual parameters. This information was used to develop a survey instrument for an applied clinical example. This involved eliciting opinions from experts to inform a number of parameters involving Bernoulli processes in an economic model evaluating DNA testing for families with a genetic disease, hypertrophic cardiomyopathy. The experts were cardiologists, clinical geneticists, and laboratory scientists working with cardiomyopathy patient populations and DNA testing. Our initial prepilot work suggested that the more complex elicitation techniques advocated in the literature were difficult to use in practice. In contrast, our approach achieved a reasonable response rate (50%), provided logical answers, and was generally rated as easy to use by respondents. The computer software user interface permitted graphical feedback throughout the elicitation process. The distributions obtained were incorporated into the model, enabling the use of probabilistic sensitivity analysis. There is clearly a gap in the literature between theoretical elicitation techniques and tools that can be used in applied decision-analytic models. The results of this methodological study are potentially valuable for other decision analysts deriving expert opinion.

Demonstrating Success: Web Analytics and Continuous Improvement

ERIC Educational Resources Information Center

Loftus, Wayne

2012-01-01

As free and low-cost Web analytics tools become more sophisticated, libraries' approach to user analysis can become more nuanced and precise. Tracking appropriate metrics with a well-formulated analytics program can inform design decisions, demonstrate the degree to which those decisions have succeeded, and thereby inform the next iteration in the…

Use of the AHP methodology in system dynamics: Modelling and simulation for health technology assessments to determine the correct prosthesis choice for hernia diseases.

PubMed

Improta, Giovanni; Russo, Mario Alessandro; Triassi, Maria; Converso, Giuseppe; Murino, Teresa; Santillo, Liberatina Carmela

2018-05-01

Health technology assessments (HTAs) are often difficult to conduct because of the decisive procedures of the HTA algorithm, which are often complex and not easy to apply. Thus, their use is not always convenient or possible for the assessment of technical requests requiring a multidisciplinary approach. This paper aims to address this issue through a multi-criteria analysis focusing on the analytic hierarchy process (AHP). This methodology allows the decision maker to analyse and evaluate different alternatives and monitor their impact on different actors during the decision-making process. However, the multi-criteria analysis is implemented through a simulation model to overcome the limitations of the AHP methodology. Simulations help decision-makers to make an appropriate decision and avoid unnecessary and costly attempts. Finally, a decision problem regarding the evaluation of two health technologies, namely, the evaluation of two biological prostheses for incisional infected hernias, will be analysed to assess the effectiveness of the model. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

A spiral model of musical decision-making.

PubMed

Bangert, Daniel; Schubert, Emery; Fabian, Dorottya

2014-01-01

This paper describes a model of how musicians make decisions about performing notated music. The model builds on psychological theories of decision-making and was developed from empirical studies of Western art music performance that aimed to identify intuitive and deliberate processes of decision-making, a distinction consistent with dual-process theories of cognition. The model proposes that the proportion of intuitive (Type 1) and deliberate (Type 2) decision-making processes changes with increasing expertise and conceptualizes this change as movement along a continually narrowing upward spiral where the primary axis signifies principal decision-making type and the vertical axis marks level of expertise. The model is intended to have implications for the development of expertise as described in two main phases. The first is movement from a primarily intuitive approach in the early stages of learning toward greater deliberation as analytical techniques are applied during practice. The second phase occurs as deliberate decisions gradually become automatic (procedural), increasing the role of intuitive processes. As a performer examines more issues or reconsiders decisions, the spiral motion toward the deliberate side and back to the intuitive is repeated indefinitely. With increasing expertise, the spiral tightens to signify greater control over decision type selection. The model draws on existing theories, particularly Evans' (2011) Intervention Model of dual-process theories, Cognitive Continuum Theory Hammond et al. (1987), Hammond (2007), Baylor's (2001) U-shaped model for the development of intuition by level of expertise. By theorizing how musical decision-making operates over time and with increasing expertise, this model could be used as a framework for future research in music performance studies and performance science more generally.

A spiral model of musical decision-making

PubMed Central

Bangert, Daniel; Schubert, Emery; Fabian, Dorottya

2014-01-01

This paper describes a model of how musicians make decisions about performing notated music. The model builds on psychological theories of decision-making and was developed from empirical studies of Western art music performance that aimed to identify intuitive and deliberate processes of decision-making, a distinction consistent with dual-process theories of cognition. The model proposes that the proportion of intuitive (Type 1) and deliberate (Type 2) decision-making processes changes with increasing expertise and conceptualizes this change as movement along a continually narrowing upward spiral where the primary axis signifies principal decision-making type and the vertical axis marks level of expertise. The model is intended to have implications for the development of expertise as described in two main phases. The first is movement from a primarily intuitive approach in the early stages of learning toward greater deliberation as analytical techniques are applied during practice. The second phase occurs as deliberate decisions gradually become automatic (procedural), increasing the role of intuitive processes. As a performer examines more issues or reconsiders decisions, the spiral motion toward the deliberate side and back to the intuitive is repeated indefinitely. With increasing expertise, the spiral tightens to signify greater control over decision type selection. The model draws on existing theories, particularly Evans’ (2011) Intervention Model of dual-process theories, Cognitive Continuum Theory Hammond et al. (1987), Hammond (2007), Baylor’s (2001) U-shaped model for the development of intuition by level of expertise. By theorizing how musical decision-making operates over time and with increasing expertise, this model could be used as a framework for future research in music performance studies and performance science more generally. PMID:24795673

Application of stochastic multiattribute analysis to assessment of single walled carbon nanotube synthesis processes.

PubMed

Canis, Laure; Linkov, Igor; Seager, Thomas P

2010-11-15

The unprecedented uncertainty associated with engineered nanomaterials greatly expands the need for research regarding their potential environmental consequences. However, decision-makers such as regulatory agencies, product developers, or other nanotechnology stakeholders may not find the results of such research directly informative of decisions intended to mitigate environmental risks. To help interpret research findings and prioritize new research needs, there is an acute need for structured decision-analytic aids that are operable in a context of extraordinary uncertainty. Whereas existing stochastic decision-analytic techniques explore uncertainty only in decision-maker preference information, this paper extends model uncertainty to technology performance. As an illustrative example, the framework is applied to the case of single-wall carbon nanotubes. Four different synthesis processes (arc, high pressure carbon monoxide, chemical vapor deposition, and laser) are compared based on five salient performance criteria. A probabilistic rank ordering of preferred processes is determined using outranking normalization and a linear-weighted sum for different weighting scenarios including completely unknown weights and four fixed-weight sets representing hypothetical stakeholder views. No single process pathway dominates under all weight scenarios, but it is likely that some inferior process technologies could be identified as low priorities for further research.

Decisions through data: analytics in healthcare.

PubMed

Wills, Mary J

2014-01-01

The amount of data in healthcare is increasing at an astonishing rate. However, in general, the industry has not deployed the level of data management and analysis necessary to make use of those data. As a result, healthcare executives face the risk of being overwhelmed by a flood of unusable data. In this essay I argue that, in order to extract actionable information, leaders must take advantage of the promise of data analytics. Small data, predictive modeling expansion, and real-time analytics are three forms of data analytics. On the basis of my analysis for this study, I recommend all three for adoption. Recognizing the uniqueness of each organization's situation, I also suggest that practices, hospitals, and healthcare systems examine small data and conduct real-time analytics and that large-scale organizations managing populations of patients adopt predictive modeling. I found that all three solutions assist in the collection, management, and analysis of raw data to improve the quality of care and decrease costs.

Political Ethnicity: A New Paradigm of Analysis

DTIC Science & Technology

1994-06-01

contemporary issue facing decision makers, ethnic conflict demands resolution. It does little good to provide an analytical paradigm if that model does...intelligentsia prefers that the indigenous majority nation make decisions, good or bad, rather than a corporalist minority. The ethnic minority Russians, on...leitmront of Nicaraguan Relfug’cc. t) (’ondion5 in Iheir (ountr) and the Reaons for Their H-lighi Garden Cit, Nil Puebla Institute, 1987 Fraser, Peter D

Influence of analytical bias and imprecision on the number of false positive results using Guideline-Driven Medical Decision Limits.

PubMed

Hyltoft Petersen, Per; Klee, George G

2014-03-20

Diagnostic decisions based on decision limits according to medical guidelines are different from the majority of clinical decisions due to the strict dichotomization of patients into diseased and non-diseased. Consequently, the influence of analytical performance is more critical than for other diagnostic decisions where much other information is included. The aim of this opinion paper is to investigate consequences of analytical quality and other circumstances for the outcome of "Guideline-Driven Medical Decision Limits". Effects of analytical bias and imprecision should be investigated separately and analytical quality specifications should be estimated accordingly. Use of sharp decision limits doesn't consider biological variation and effects of this variation are closely connected with the effects of analytical performance. Such relationships are investigated for the guidelines for HbA1c in diagnosis of diabetes and in risk of coronary heart disease based on serum cholesterol. The effects of a second sampling in diagnosis give dramatic reduction in the effects of analytical quality showing minimal influence of imprecision up to 3 to 5% for two independent samplings, whereas the reduction in bias is more moderate and a 2% increase in concentration doubles the percentage of false positive diagnoses, both for HbA1c and cholesterol. An alternative approach comes from the current application of guidelines for follow-up laboratory tests according to clinical procedure orders, e.g. frequency of parathyroid hormone requests as a function of serum calcium concentrations. Here, the specifications for bias can be evaluated from the functional increase in requests for increasing serum calcium concentrations. In consequence of the difficulties with biological variation and the practical utilization of concentration dependence of frequency of follow-up laboratory tests already in use, a kind of probability function for diagnosis as function of the key-analyte is proposed. Copyright © 2013 Elsevier B.V. All rights reserved.

Reprint of "Influence of analytical bias and imprecision on the number of false positive results using Guideline-Driven Medical Decision Limits".

PubMed

Hyltoft Petersen, Per; Klee, George G

2014-05-15

Diagnostic decisions based on decision limits according to medical guidelines are different from the majority of clinical decisions due to the strict dichotomization of patients into diseased and non-diseased. Consequently, the influence of analytical performance is more critical than for other diagnostic decisions where much other information is included. The aim of this opinion paper is to investigate consequences of analytical quality and other circumstances for the outcome of "Guideline-Driven Medical Decision Limits". Effects of analytical bias and imprecision should be investigated separately and analytical quality specifications should be estimated accordingly. Use of sharp decision limits doesn't consider biological variation and effects of this variation are closely connected with the effects of analytical performance. Such relationships are investigated for the guidelines for HbA1c in diagnosis of diabetes and in risk of coronary heart disease based on serum cholesterol. The effects of a second sampling in diagnosis give dramatic reduction in the effects of analytical quality showing minimal influence of imprecision up to 3 to 5% for two independent samplings, whereas the reduction in bias is more moderate and a 2% increase in concentration doubles the percentage of false positive diagnoses, both for HbA1c and cholesterol. An alternative approach comes from the current application of guidelines for follow-up laboratory tests according to clinical procedure orders, e.g. frequency of parathyroid hormone requests as a function of serum calcium concentrations. Here, the specifications for bias can be evaluated from the functional increase in requests for increasing serum calcium concentrations. In consequence of the difficulties with biological variation and the practical utilization of concentration dependence of frequency of follow-up laboratory tests already in use, a kind of probability function for diagnosis as function of the key-analyte is proposed. Copyright © 2014. Published by Elsevier B.V.

FEMA's Earthquake Incident Journal: A Web-Based Data Integration and Decision Support Tool for Emergency Management

NASA Astrophysics Data System (ADS)

Jones, M.; Pitts, R.

2017-12-01

For emergency managers, government officials, and others who must respond to rapidly changing natural disasters, timely access to detailed information related to affected terrain, population and infrastructure is critical for planning, response and recovery operations. Accessing, analyzing and disseminating such disparate information in near real-time are critical decision support components. However, finding a way to handle a variety of informative yet complex datasets poses a challenge when preparing for and responding to disasters. Here, we discuss the implementation of a web-based data integration and decision support tool for earthquakes developed by the Federal Emergency Management Agency (FEMA) as a solution to some of these challenges. While earthquakes are among the most well- monitored and measured of natural hazards, the spatially broad impacts of shaking, ground deformation, landslides, liquefaction, and even tsunamis, are extremely difficult to quantify without accelerated access to data, modeling, and analytics. This web-based application, deemed the "Earthquake Incident Journal", provides real-time access to authoritative and event-specific data from external (e.g. US Geological Survey, NASA, state and local governments, etc.) and internal (FEMA) data sources. The journal includes a GIS-based model for exposure analytics, allowing FEMA to assess the severity of an event, estimate impacts to structures and population in near real-time, and then apply planning factors to exposure estimates to answer questions such as: What geographic areas are impacted? Will federal support be needed? What resources are needed to support survivors? And which infrastructure elements or essential facilities are threatened? This presentation reviews the development of the Earthquake Incident Journal, detailing the data integration solutions, the methodology behind the GIS-based automated exposure model, and the planning factors as well as other analytical advances that provide near real-time decision support to the federal government.

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention

PubMed Central

Fisher, Brian; Smith, Jennifer; Pike, Ian

2017-01-01

Background: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods: Inspired by the Delphi method, we introduced a novel methodology—group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders’ observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results: The GA methodology triggered the emergence of ‘common ground’ among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders’ verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusions: Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ‘common ground’ among diverse stakeholders about health data and their implications. PMID:28895928

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention.

PubMed

Al-Hajj, Samar; Fisher, Brian; Smith, Jennifer; Pike, Ian

2017-09-12

Background : Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods : Inspired by the Delphi method, we introduced a novel methodology-group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders' observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results : The GA methodology triggered the emergence of ' common g round ' among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders' verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusion s : Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ' common ground' among diverse stakeholders about health data and their implications.

Manpower Impact Assessment Model (MIAM). An Analytic Model for Assessing the Effect of Supply Policy Changes on Manpower Requirements at Retail Supply Activities

DTIC Science & Technology

1984-09-01

to Management Science (Third Edition). St. Paul: West Publishing Co., 1982. 2. Bennett, John L. (Editor). Building Decision Support Systems. Reading...Starts 700 DCCs 5000 Units Inventoried 50000 103 * * Bibliography /, 1. Anderson, David R., Dennis J. Sweeney, and Thomas A. Williams. An Introduction

BIOCHLOR: NATURAL ATTENUATION DECISION SUPPORT SYSTEM, USER'S MANUAL, VERSION 1.0

EPA Science Inventory

BIOCHLOR is an easy-to-use screening model that simulates remediation by natural attenuation (RNA) of dissolved solvents at chlorinated solvent release sites. The software, programmed in the Microsoft Excel spreadsheet environment and based on the Domenico analytical solute tran...

BIOSCREEN: NATURAL ATTENTUATION DECISION SUPPORT SYSTEM - USER'S MANUAL, VERSION 1.3

EPA Science Inventory

BIOSCREEN is an easy-to-use screening model which simulates remediation through natural attenuation (RNA) of dissolved hydrocarbons at petroleum fuel release sites. The software, programmed in the Microsoft Excel spreadsheet environment and based on the Domenico analytical solu...

Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

PubMed Central

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable. PMID:25369026

Assessing cost-effectiveness of HPV vaccines with decision analytic models: what are the distinct challenges of low- and middle-income countries? A protocol for a systematic review.

PubMed

Ekwunife, Obinna I; Grote, Andreas Gerber; Mosch, Christoph; O'Mahony, James F; Lhachimi, Stefan K

2015-05-12

Cervical cancer poses a huge health burden, both to developed and developing nations, making prevention and control strategies necessary. However, the challenges of designing and implementing prevention strategies differ for low- and middle-income countries (LMICs) as compared to countries with fully developed health care systems. Moreover, for many LMICs, much of the data needed for decision analytic modelling, such as prevalence, will most likely only be partly available or measured with much larger uncertainty. Lastly, imperfect implementation of human papillomavirus (HPV) vaccination may influence the effectiveness of cervical cancer prevention in unpredictable ways. This systematic review aims to assess how decision analytic modelling studies of HPV cost-effectiveness in LMICs accounted for the particular challenges faced in such countries. Specifically, the study will assess the following: (1) whether the existing literature on cost-effectiveness modelling of HPV vaccines acknowledges the distinct challenges of LMICs, (2) how these challenges were accommodated in the models, (3) whether certain parameters systemically exhibited large degrees of uncertainty due to lack of data and how influential were these parameters on model-based recommendations, and (4) whether the choice of modelling herd immunity influences model-based recommendations, especially when coverage of a HPV vaccination program is not optimal. We will conduct a systematic review to identify suitable studies from MEDLINE (via PubMed), EMBASE, NHS Economic Evaluation Database (NHS EED), EconLit, Web of Science, and CEA Registry. Searches will be conducted for studies of interest published since 2006. The searches will be supplemented by hand searching of the most relevant papers found in the search. Studies will be critically appraised using Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement checklist. We will undertake a descriptive, narrative, and interpretative synthesis of data to address the study objectives. The proposed systematic review will assess how the cost-effectiveness studies of HPV vaccines accounted for the distinct challenges of LMICs. The gaps identified will expose areas for additional research as well as challenges that need to be accounted for in future modelling studies. PROSPERO CRD42015017870.

Multilevel Modeling and Policy Development: Guidelines and Applications to Medical Travel.

PubMed

Garcia-Garzon, Eduardo; Zhukovsky, Peter; Haller, Elisa; Plakolm, Sara; Fink, David; Petrova, Dafina; Mahalingam, Vaishali; Menezes, Igor G; Ruggeri, Kai

2016-01-01

Medical travel has expanded rapidly in recent years, resulting in new markets and increased access to medical care. Whereas several studies investigated the motives of individuals seeking healthcare abroad, the conventional analytical approach is limited by substantial caveats. Classical techniques as found in the literature cannot provide sufficient insight due to the nested nature of data generated. The application of adequate analytical techniques, specifically multilevel modeling, is scarce to non-existent in the context of medical travel. This study introduces the guidelines for application of multilevel techniques in public health research by presenting an application of multilevel modeling in analyzing the decision-making patterns of potential medical travelers. Benefits and potential limitations are discussed.

Multilevel Modeling and Policy Development: Guidelines and Applications to Medical Travel

PubMed Central

Garcia-Garzon, Eduardo; Zhukovsky, Peter; Haller, Elisa; Plakolm, Sara; Fink, David; Petrova, Dafina; Mahalingam, Vaishali; Menezes, Igor G.; Ruggeri, Kai

2016-01-01

Medical travel has expanded rapidly in recent years, resulting in new markets and increased access to medical care. Whereas several studies investigated the motives of individuals seeking healthcare abroad, the conventional analytical approach is limited by substantial caveats. Classical techniques as found in the literature cannot provide sufficient insight due to the nested nature of data generated. The application of adequate analytical techniques, specifically multilevel modeling, is scarce to non-existent in the context of medical travel. This study introduces the guidelines for application of multilevel techniques in public health research by presenting an application of multilevel modeling in analyzing the decision-making patterns of potential medical travelers. Benefits and potential limitations are discussed. PMID:27252672

Parametric Modeling of the Safety Effects of NextGen Terminal Maneuvering Area Conflict Scenarios

NASA Technical Reports Server (NTRS)

Rogers, William H.; Waldron, Timothy P.; Stroiney, Steven R.

2011-01-01

The goal of this work was to analytically identify and quantify the issues, challenges, technical hurdles, and pilot-vehicle interface issues associated with conflict detection and resolution (CD&R)in emerging operational concepts for a NextGen terminal aneuvering area, including surface operations. To this end, the work entailed analytical and trade studies focused on modeling the achievable safety benefits of different CD&R strategies and concepts in the current and future airport environment. In addition, crew-vehicle interface and pilot performance enhancements and potential issues were analyzed based on review of envisioned NextGen operations, expected equipage advances, and human factors expertise. The results of perturbation analysis, which quantify the high-level performance impact of changes to key parameters such as median response time and surveillance position error, show that the analytical model developed could be useful in making technology investment decisions.

A Chaotic Ordered Hierarchies Consistency Analysis Performance Evaluation Model

NASA Astrophysics Data System (ADS)

Yeh, Wei-Chang

2013-02-01

The Hierarchies Consistency Analysis (HCA) is proposed by Guh in-cooperated along with some case study on a Resort to reinforce the weakness of Analytical Hierarchy Process (AHP). Although the results obtained enabled aid for the Decision Maker to make more reasonable and rational verdicts, the HCA itself is flawed. In this paper, our objective is to indicate the problems of HCA, and then propose a revised method called chaotic ordered HCA (COH in short) which can avoid problems. Since the COH is based upon Guh's method, the Decision Maker establishes decisions in a way similar to that of the original method.

NASA Wrangler: Automated Cloud-Based Data Assembly in the RECOVER Wildfire Decision Support System

NASA Technical Reports Server (NTRS)

Schnase, John; Carroll, Mark; Gill, Roger; Wooten, Margaret; Weber, Keith; Blair, Kindra; May, Jeffrey; Toombs, William

2017-01-01

NASA Wrangler is a loosely-coupled, event driven, highly parallel data aggregation service designed to take advantageof the elastic resource capabilities of cloud computing. Wrangler automatically collects Earth observational data, climate model outputs, derived remote sensing data products, and historic biophysical data for pre-, active-, and post-wildfire decision making. It is a core service of the RECOVER decision support system, which is providing rapid-response GIS analytic capabilities to state and local government agencies. Wrangler reduces to minutes the time needed to assemble and deliver crucial wildfire-related data.

Model Multi Criteria Decision Making with Fuzzy ANP Method for Performance Measurement Small Medium Enterprise (SME)

NASA Astrophysics Data System (ADS)

Rahmanita, E.; Widyaningrum, V. T.; Kustiyahningsih, Y.; Purnama, J.

2018-04-01

SMEs have a very important role in the development of the economy in Indonesia. SMEs assist the government in terms of creating new jobs and can support household income. The number of SMEs in Madura and the number of measurement indicators in the SME mapping so that it requires a method.This research uses Fuzzy Analytic Network Process (FANP) method for performance measurement SME. The FANP method can handle data that contains uncertainty. There is consistency index in determining decisions. Performance measurement in this study is based on a perspective of the Balanced Scorecard. This research approach integrated internal business perspective, learning, and growth perspective and fuzzy Analytic Network Process (FANP). The results of this research areframework a priority weighting of assessment indicators SME.

Decision support for environmental management of industrial non-hazardous secondary materials: New analytical methods combined with simulation and optimization modeling.

PubMed

Little, Keith W; Koralegedara, Nadeesha H; Northeim, Coleen M; Al-Abed, Souhail R

2017-07-01

Non-hazardous solid materials from industrial processes, once regarded as waste and disposed in landfills, offer numerous environmental and economic advantages when put to beneficial uses (BUs). Proper management of these industrial non-hazardous secondary materials (INSM) requires estimates of their probable environmental impacts among disposal as well as BU options. The U.S. Environmental Protection Agency (EPA) has recently approved new analytical methods (EPA Methods 1313-1316) to assess leachability of constituents of potential concern in these materials. These new methods are more realistic for many disposal and BU options than historical methods, such as the toxicity characteristic leaching protocol. Experimental data from these new methods are used to parameterize a chemical fate and transport (F&T) model to simulate long-term environmental releases from flue gas desulfurization gypsum (FGDG) when disposed of in an industrial landfill or beneficially used as an agricultural soil amendment. The F&T model is also coupled with optimization algorithms, the Beneficial Use Decision Support System (BUDSS), under development by EPA to enhance INSM management. Published by Elsevier Ltd.

Big Data Analytic, Big Step for Patient Management and Care in Puerto Rico.

PubMed

Borrero, Ernesto E

2018-01-01

This letter provides an overview of the application of big data in health care system to improve quality of care, including predictive modelling for risk and resource use, precision medicine and clinical decision support, quality of care and performance measurement, public health and research applications, among others. The author delineates the tremendous potential for big data analytics and discuss how it can be successfully implemented in clinical practice, as an important component of a learning health-care system.

Assurance of Learning in the MIS Program

ERIC Educational Resources Information Center

Harper, Jeffrey S.; Harder, Joseph T.

2009-01-01

This article describes the development of a systematic and practical methodology for assessing program effectiveness and monitoring student development in undergraduate decision sciences programs. The model we present is based on a student's progression through learning stages associated with four key competencies: technical, analytical,…

Traffic analysis toolbox volume IX : work zone modeling and simulation, a guide for analysts

DOT National Transportation Integrated Search

2009-03-01

This document is the second volume in the FHWA Traffic Analysis Toolbox: Work Zone Analysis series. Whereas the first volume provides guidance to decision-makers at agencies and jurisdictions considering the role of analytical tools in work zone plan...

An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory

PubMed Central

Pelaccia, Thierry; Tardif, Jacques; Triby, Emmanuel; Charlin, Bernard

2011-01-01

Context Clinical reasoning plays a major role in the ability of doctors to make diagnoses and decisions. It is considered as the physician's most critical competence, and has been widely studied by physicians, educationalists, psychologists and sociologists. Since the 1970s, many theories about clinical reasoning in medicine have been put forward. Purpose This paper aims at exploring a comprehensive approach: the “dual-process theory”, a model developed by cognitive psychologists over the last few years. Discussion After 40 years of sometimes contradictory studies on clinical reasoning, the dual-process theory gives us many answers on how doctors think while making diagnoses and decisions. It highlights the importance of physicians’ intuition and the high level of interaction between analytical and non-analytical processes. However, it has not received much attention in the medical education literature. The implications of dual-process models of reasoning in terms of medical education will be discussed. PMID:21430797

Analytical model for minority games with evolutionary learning

NASA Astrophysics Data System (ADS)

Campos, Daniel; Méndez, Vicenç; Llebot, Josep E.; Hernández, Germán A.

2010-06-01

In a recent work [D. Campos, J.E. Llebot, V. Méndez, Theor. Popul. Biol. 74 (2009) 16] we have introduced a biological version of the Evolutionary Minority Game that tries to reproduce the intraspecific competition for limited resources in an ecosystem. In comparison with the complex decision-making mechanisms used in standard Minority Games, only two extremely simple strategies ( juveniles and adults) are accessible to the agents. Complexity is introduced instead through an evolutionary learning rule that allows younger agents to learn taking better decisions. We find that this game shows many of the typical properties found for Evolutionary Minority Games, like self-segregation behavior or the existence of an oscillation phase for a certain range of the parameter values. However, an analytical treatment becomes much easier in our case, taking advantage of the simple strategies considered. Using a model consisting of a simple dynamical system, the phase diagram of the game (which differentiates three phases: adults crowd, juveniles crowd and oscillations) is reproduced.

Integrated catchment modelling within a strategic planning and decision making process: Werra case study

NASA Astrophysics Data System (ADS)

Dietrich, Jörg; Funke, Markus

Integrated water resources management (IWRM) redefines conventional water management approaches through a closer cross-linkage between environment and society. The role of public participation and socio-economic considerations becomes more important within the planning and decision making process. In this paper we address aspects of the integration of catchment models into such a process taking the implementation of the European Water Framework Directive (WFD) as an example. Within a case study situated in the Werra river basin (Central Germany), a systems analytic decision process model was developed. This model uses the semantics of the Unified Modeling Language (UML) activity model. As an example application, the catchment model SWAT and the water quality model RWQM1 were applied to simulate the effect of phosphorus emissions from non-point and point sources on water quality. The decision process model was able to guide the participants of the case study through the interdisciplinary planning and negotiation of actions. Further improvements of the integration framework include tools for quantitative uncertainty analyses, which are crucial for real life application of models within an IWRM decision making toolbox. For the case study, the multi-criteria assessment of actions indicates that the polluter pays principle can be met at larger scales (sub-catchment or river basin) without significantly compromising cost efficiency for the local situation.

Heuristic and analytic processing in online sports betting.

PubMed

d'Astous, Alain; Di Gaspero, Marc

2015-06-01

This article presents the results of two studies that examine the occurrence of heuristic (i.e., intuitive and fast) and analytic (i.e., deliberate and slow) processes among people who engage in online sports betting on a regular basis. The first study was qualitative and was conducted with a convenience sample of 12 regular online sports gamblers who described the processes by which they arrive at a sports betting decision. The results of this study showed that betting online on sports events involves a mix of heuristic and analytic processes. The second study consisted in a survey of 161 online sports gamblers where performance in terms of monetary gains, experience in online sports betting, propensity to collect and analyze relevant information prior to betting, and use of bookmaker odds were measured. This study showed that heuristic and analytic processes act as mediators of the relationship between experience and performance. The findings stemming of these two studies give some insights into gamblers' modes of thinking and behaviors in an online sports betting context and show the value of the dual mediation process model for research that looks at gambling activities from a judgment and decision making perspective.

Interaction Junk: User Interaction-Based Evaluation of Visual Analytic Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; North, Chris

2012-10-14

With the growing need for visualization to aid users in understanding large, complex datasets, the ability for users to interact and explore these datasets is critical. As visual analytic systems have advanced to leverage powerful computational models and data analytics capabilities, the modes by which users engage and interact with the information are limited. Often, users are taxed with directly manipulating parameters of these models through traditional GUIs (e.g., using sliders to directly manipulate the value of a parameter). However, the purpose of user interaction in visual analytic systems is to enable visual data exploration – where users can focusmore » on their task, as opposed to the tool or system. As a result, users can engage freely in data exploration and decision-making, for the purpose of gaining insight. In this position paper, we discuss how evaluating visual analytic systems can be approached through user interaction analysis, where the goal is to minimize the cognitive translation between the visual metaphor and the mode of interaction (i.e., reducing the “Interactionjunk”). We motivate this concept through a discussion of traditional GUIs used in visual analytics for direct manipulation of model parameters, and the importance of designing interactions the support visual data exploration.« less

P.C. disposal decisions: a banking industry case study

NASA Astrophysics Data System (ADS)

Shah, Sejal P.; Sarkis, Joseph

2002-02-01

The service industry and the manufacturing industry are interlinked in a supply chain situation. Part of the effectiveness of some manufacturing industry environmental performance based on remanufacturing and recycling is dependent on service industry decisions. In the information technology arena, personal computers (PCs) are the hard equipment of the service industry. The end-of-life decisions made by the service industry, and in this case the banking industry will have implications for the amount of systems within the waste or reverse logistics stream for manufacturers. Looking at some of the issues (and presenting a model for evaluation) related to decision making concerning end-of-life disposition for PCs is something this paper investigates. The analytical hierarchy process (AHP) is applied in this circumstance. The development of the model, its application, and results, provide the basis for much of the discussion in this paper.

A 3-states magnetic model of binary decisions in sociophysics

NASA Astrophysics Data System (ADS)

Fernandez, Miguel A.; Korutcheva, Elka; de la Rubia, F. Javier

2016-11-01

We study a diluted Blume-Capel model of 3-states sites as an attempt to understand how some social processes as cooperation or organization happen. For this aim, we study the effect of the complex network topology on the equilibrium properties of the model, by focusing on three different substrates: random graph, Watts-Strogatz and Newman substrates. Our computer simulations are in good agreement with the corresponding analytical results.

Analyzing stakeholder preferences for managing elk and bison at the National Elk Refuge and Grand Teton National Park: An example of the disparate stakeholder management approach

USGS Publications Warehouse

Koontz, Lynne; Hoag, Dana L.

2005-01-01

Many programs and tools have been developed by different disciplines to facilitate group negotiation and decision making. Three examples are relevant here. First, decision analysis models such as the Analytical Hierarchy Process (AHP) are commonly used to prioritize the goals and objectives of stakeholders’ preferences for resource planning by formally structuring conflicts and assisting decision makers in developing a compromised solution (Forman, 1998). Second, institutional models such as the Legal Institutional Analysis Model (LIAM) have been used to describe the organizational rules of behavior and the institutional boundaries constraining management decisions (Lamb and others, 1998). Finally, public choice models have been used to predict the potential success of rent-seeking activity (spending additional time and money to exert political pressure) to change the political rules (Becker, 1983). While these tools have been successful at addressing various pieces of the natural resource decision making process, their use in isolation is not enough to fully depict the complexities of the physical and biological systems with the rules and constraints of the underlying economic and political systems. An approach is needed that combines natural sciences, economics, and politics.

Proactive Supply Chain Performance Management with Predictive Analytics

PubMed Central

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

Proactive supply chain performance management with predictive analytics.

PubMed

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

Model performance evaluation (validation and calibration) in model-based studies of therapeutic interventions for cardiovascular diseases : a review and suggested reporting framework.

PubMed

Haji Ali Afzali, Hossein; Gray, Jodi; Karnon, Jonathan

2013-04-01

Decision analytic models play an increasingly important role in the economic evaluation of health technologies. Given uncertainties around the assumptions used to develop such models, several guidelines have been published to identify and assess 'best practice' in the model development process, including general modelling approach (e.g., time horizon), model structure, input data and model performance evaluation. This paper focuses on model performance evaluation. In the absence of a sufficient level of detail around model performance evaluation, concerns regarding the accuracy of model outputs, and hence the credibility of such models, are frequently raised. Following presentation of its components, a review of the application and reporting of model performance evaluation is presented. Taking cardiovascular disease as an illustrative example, the review investigates the use of face validity, internal validity, external validity, and cross model validity. As a part of the performance evaluation process, model calibration is also discussed and its use in applied studies investigated. The review found that the application and reporting of model performance evaluation across 81 studies of treatment for cardiovascular disease was variable. Cross-model validation was reported in 55 % of the reviewed studies, though the level of detail provided varied considerably. We found that very few studies documented other types of validity, and only 6 % of the reviewed articles reported a calibration process. Considering the above findings, we propose a comprehensive model performance evaluation framework (checklist), informed by a review of best-practice guidelines. This framework provides a basis for more accurate and consistent documentation of model performance evaluation. This will improve the peer review process and the comparability of modelling studies. Recognising the fundamental role of decision analytic models in informing public funding decisions, the proposed framework should usefully inform guidelines for preparing submissions to reimbursement bodies.

Aviation Modeling and Simulation Needs and Requirements Workshop: January 27-28, 1999

DOT National Transportation Integrated Search

1999-01-01

A two-day workshop was held at the Volpe Center on January 27-28, 1999. The purpose of the workshop was to: 1) identify and understand the requirements for analytical and planning tool initiatives that will give decision makers insight into the capac...

Generalisability in economic evaluation studies in healthcare: a review and case studies.

PubMed

Sculpher, M J; Pang, F S; Manca, A; Drummond, M F; Golder, S; Urdahl, H; Davies, L M; Eastwood, A

2004-12-01

To review, and to develop further, the methods used to assess and to increase the generalisability of economic evaluation studies. Electronic databases. Methodological studies relating to economic evaluation in healthcare were searched. This included electronic searches of a range of databases, including PREMEDLINE, MEDLINE, EMBASE and EconLit, and manual searches of key journals. The case studies of a decision analytic model involved highlighting specific features of previously published economic studies related to generalisability and location-related variability. The case-study involving the secondary analysis of cost-effectiveness analyses was based on the secondary analysis of three economic studies using data from randomised trials. The factor most frequently cited as generating variability in economic results between locations was the unit costs associated with particular resources. In the context of studies based on the analysis of patient-level data, regression analysis has been advocated as a means of looking at variability in economic results across locations. These methods have generally accepted that some components of resource use and outcomes are exchangeable across locations. Recent studies have also explored, in cost-effectiveness analysis, the use of tests of heterogeneity similar to those used in clinical evaluation in trials. The decision analytic model has been the main means by which cost-effectiveness has been adapted from trial to non-trial locations. Most models have focused on changes to the cost side of the analysis, but it is clear that the effectiveness side may also need to be adapted between locations. There have been weaknesses in some aspects of the reporting in applied cost-effectiveness studies. These may limit decision-makers' ability to judge the relevance of a study to their specific situations. The case study demonstrated the potential value of multilevel modelling (MLM). Where clustering exists by location (e.g. centre or country), MLM can facilitate correct estimates of the uncertainty in cost-effectiveness results, and also a means of estimating location-specific cost-effectiveness. The review of applied economic studies based on decision analytic models showed that few studies were explicit about their target decision-maker(s)/jurisdictions. The studies in the review generally made more effort to ensure that their cost inputs were specific to their target jurisdiction than their effectiveness parameters. Standard sensitivity analysis was the main way of dealing with uncertainty in the models, although few studies looked explicitly at variability between locations. The modelling case study illustrated how effectiveness and cost data can be made location-specific. In particular, on the effectiveness side, the example showed the separation of location-specific baseline events and pooled estimates of relative treatment effect, where the latter are assumed exchangeable across locations. A large number of factors are mentioned in the literature that might be expected to generate variation in the cost-effectiveness of healthcare interventions across locations. Several papers have demonstrated differences in the volume and cost of resource use between locations, but few studies have looked at variability in outcomes. In applied trial-based cost-effectiveness studies, few studies provide sufficient evidence for decision-makers to establish the relevance or to adjust the results of the study to their location of interest. Very few studies utilised statistical methods formally to assess the variability in results between locations. In applied economic studies based on decision models, most studies either stated their target decision-maker/jurisdiction or provided sufficient information from which this could be inferred. There was a greater tendency to ensure that cost inputs were specific to the target jurisdiction than clinical parameters. Methods to assess generalisability and variability in economic evaluation studies have been discussed extensively in the literature relating to both trial-based and modelling studies. Regression-based methods are likely to offer a systematic approach to quantifying variability in patient-level data. In particular, MLM has the potential to facilitate estimates of cost-effectiveness, which both reflect the variation in costs and outcomes between locations and also enable the consistency of cost-effectiveness estimates between locations to be assessed directly. Decision analytic models will retain an important role in adapting the results of cost-effectiveness studies between locations. Recommendations for further research include: the development of methods of evidence synthesis which model the exchangeability of data across locations and allow for the additional uncertainty in this process; assessment of alternative approaches to specifying multilevel models to the analysis of cost-effectiveness data alongside multilocation randomised trials; identification of a range of appropriate covariates relating to locations (e.g. hospitals) in multilevel models; and further assessment of the role of econometric methods (e.g. selection models) for cost-effectiveness analysis alongside observational datasets, and to increase the generalisability of randomised trials.

Knowledge Style Profiling: An Exploration of Cognitive, Temperament, Demographic and Organizational Characteristics among Decision Makers Using Advanced Analytical Technologies

ERIC Educational Resources Information Center

Polito, Vincent A., Jr.

2010-01-01

The objective of this research was to explore the possibilities of identifying knowledge style factors that could be used as central elements of a professional business analyst's (PBA) performance attributes at work for those decision makers that use advanced analytical technologies on decision making tasks. Indicators of knowledge style were…

Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making.

PubMed

Capalbo, Susan M; Antle, John M; Seavert, Clark

2017-07-01

Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, Camelina sativa , which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.

Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units.

PubMed

Yun Chen; Hui Yang

2014-01-01

The rapid advancements of biomedical instrumentation and healthcare technology have resulted in data-rich environments in hospitals. However, the meaningful information extracted from rich datasets is limited. There is a dire need to go beyond current medical practices, and develop data-driven methods and tools that will enable and help (i) the handling of big data, (ii) the extraction of data-driven knowledge, (iii) the exploitation of acquired knowledge for optimizing clinical decisions. This present study focuses on the prediction of mortality rates in Intensive Care Units (ICU) using patient-specific healthcare recordings. It is worth mentioning that postsurgical monitoring in ICU leads to massive datasets with unique properties, e.g., variable heterogeneity, patient heterogeneity, and time asyncronization. To cope with the challenges in ICU datasets, we developed the postsurgical decision support system with a series of analytical tools, including data categorization, data pre-processing, feature extraction, feature selection, and predictive modeling. Experimental results show that the proposed data-driven methodology outperforms traditional approaches and yields better results based on the evaluation of real-world ICU data from 4000 subjects in the database. This research shows great potentials for the use of data-driven analytics to improve the quality of healthcare services.

The role of clinician emotion in clinical reasoning: Balancing the analytical process.

PubMed

Langridge, Neil; Roberts, Lisa; Pope, Catherine

2016-02-01

This review paper identifies and describes the role of clinicians' memory, emotions and physical responses in clinical reasoning processes. Clinical reasoning is complex and multi-factorial and key models of clinical reasoning within musculoskeletal physiotherapy are discussed, highlighting the omission of emotion and subsequent physical responses and how these can impact upon a clinician when making a decision. It is proposed that clinicians should consider the emotions associated with decision-making, especially when there is concern surrounding a presentation. Reflecting on practice in the clinical environment and subsequently applying this to a patient presentation should involve some acknowledgement of clinicians' physical responses, emotions and how they may play a part in any decision made. Presenting intuition and gut-feeling as separate reasoning methods and how these processes co-exist with other more accepted reasoning such as hypothetico-deductive is also discussed. Musculoskeletal physiotherapy should consider the elements of feelings, emotions and physical responses when applying reflective practice principles. Furthermore, clinicians dealing with difficult and challenging presentations should look at the emotional as well as the analytical experience when justifying decisions and learning from practice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment.

PubMed

Liu, Shan; Brandeau, Margaret L; Goldhaber-Fiebert, Jeremy D

2017-03-01

How long should a patient with a treatable chronic disease wait for more effective treatments before accepting the best available treatment? We develop a framework to guide optimal treatment decisions for a deteriorating chronic disease when treatment technologies are improving over time. We formulate an optimal stopping problem using a discrete-time, finite-horizon Markov decision process. The goal is to maximize a patient's quality-adjusted life expectancy. We derive structural properties of the model and analytically solve a three-period treatment decision problem. We illustrate the model with the example of treatment for chronic hepatitis C virus (HCV). Chronic HCV affects 3-4 million Americans and has been historically difficult to treat, but increasingly effective treatments have been commercialized in the past few years. We show that the optimal treatment decision is more likely to be to accept currently available treatment-despite expectations for future treatment improvement-for patients who have high-risk history, who are older, or who have more comorbidities. Insights from this study can guide HCV treatment decisions for individual patients. More broadly, our model can guide treatment decisions for curable chronic diseases by finding the optimal treatment policy for individual patients in a heterogeneous population.

Optimizing Patient Treatment Decisions in an Era of Rapid Technological Advances: The Case of Hepatitis C Treatment

PubMed Central

Liu, Shan; Goldhaber-Fiebert, Jeremy D.; Brandeau, Margaret L.

2015-01-01

How long should a patient with a treatable chronic disease wait for more effective treatments before accepting the best available treatment? We develop a framework to guide optimal treatment decisions for a deteriorating chronic disease when treatment technologies are improving over time. We formulate an optimal stopping problem using a discrete-time, finite-horizon Markov decision process. The goal is to maximize a patient’s quality-adjusted life expectancy. We derive structural properties of the model and analytically solve a three-period treatment decision problem. We illustrate the model with the example of treatment for chronic hepatitis C virus (HCV). Chronic HCV affects 3–4 million Americans and has been historically difficult to treat, but increasingly effective treatments have been commercialized in the past few years. We show that the optimal treatment decision is more likely to be to accept currently available treatment—despite expectations for future treatment improvement—for patients who have high-risk history, who are older, or who have more comorbidities. Insights from this study can guide HCV treatment decisions for individual patients. More broadly, our model can guide treatment decisions for curable chronic diseases by finding the optimal treatment policy for individual patients in a heterogeneous population. PMID:26188961

78 FR 28631 - Experian, Experian Healthcare (Medical Present Value (MPV)-Credit Services and Decision Analytics...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

...), Experian, Experian U.S. Headquarters: Corporate Departments (finance, HRMD, Contracts, Corporate Marketing...: Corporate Departments (finance, HRMD, Contracts, Corporate Marketing, Global Corporate Systems, Legal..., Business Information Services, Corporate Marketing, Credit Services, Data Management, Decision Analytics...

Principles of health economic evaluations of lipid-lowering strategies.

PubMed

Ara, Roberta; Basarir, Hasan; Ward, Sue Elizabeth

2012-08-01

Policy decision-making in cardiovascular disease is increasingly informed by the results generated from decision-analytic models (DAMs). The methodological approaches and assumptions used in these DAMs impact on the results generated and can influence a policy decision based on a cost per quality-adjusted life year (QALY) threshold. Decision makers need to be provided with a clear understanding of the key sources of evidence and how they are used in the DAM to make an informed judgement on the quality and appropriateness of the results generated. Our review identified 12 studies exploring the cost-effectiveness of pharmaceutical lipid-lowering interventions published since January 2010. All studies used Markov models with annual cycles to represent the long-term clinical pathway. Important differences in the model structures and evidence base used within the DAMs were identified. Whereas the reporting standards were reasonably good, there were many instances when reporting of methods could be improved, particularly relating to baseline risk levels, long-term benefit of treatment and health state utility values. There is a scope for improvement in the reporting of evidence and modelling approaches used within DAMs to provide decision makers with a clearer understanding of the quality and validity of the results generated. This would be assisted by fuller publication of models, perhaps through detailed web appendices.

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

PubMed Central

Masías, Víctor H.; Krause, Mariane; Valdés, Nelson; Pérez, J. C.; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice. PMID:25914657

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process.

PubMed

Masías, Víctor H; Krause, Mariane; Valdés, Nelson; Pérez, J C; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

Evidence used in model-based economic evaluations for evaluating pharmacogenetic and pharmacogenomic tests: a systematic review protocol

PubMed Central

Peters, Jaime L; Cooper, Chris; Buchanan, James

2015-01-01

Introduction Decision models can be used to conduct economic evaluations of new pharmacogenetic and pharmacogenomic tests to ensure they offer value for money to healthcare systems. These models require a great deal of evidence, yet research suggests the evidence used is diverse and of uncertain quality. By conducting a systematic review, we aim to investigate the test-related evidence used to inform decision models developed for the economic evaluation of genetic tests. Methods and analysis We will search electronic databases including MEDLINE, EMBASE and NHS EEDs to identify model-based economic evaluations of pharmacogenetic and pharmacogenomic tests. The search will not be limited by language or date. Title and abstract screening will be conducted independently by 2 reviewers, with screening of full texts and data extraction conducted by 1 reviewer, and checked by another. Characteristics of the decision problem, the decision model and the test evidence used to inform the model will be extracted. Specifically, we will identify the reported evidence sources for the test-related evidence used, describe the study design and how the evidence was identified. A checklist developed specifically for decision analytic models will be used to critically appraise the models described in these studies. Variations in the test evidence used in the decision models will be explored across the included studies, and we will identify gaps in the evidence in terms of both quantity and quality. Dissemination The findings of this work will be disseminated via a peer-reviewed journal publication and at national and international conferences. PMID:26560056

End-of-life decision making is more than rational.

PubMed

Eliott, Jaklin A; Olver, Ian N

2005-01-01

Most medical models of end-of-life decision making by patients assume a rational autonomous adult obtaining and deliberating over information to arrive at some conclusion. If the patient is deemed incapable of this, family members are often nominated as substitutes, with assumptions that the family are united and rational. These are problematic assumptions. We interviewed 23 outpatients with cancer about the decision not to resuscitate a patient following cardiopulmonary arrest and examined their accounts of decision making using discourse analytical techniques. Our analysis suggests that participants access two different interpretative repertoires regarding the construct of persons, invoking a 'modernist' repertoire to assert the appropriateness of someone, a patient or family, making a decision, and a 'romanticist' repertoire when identifying either a patient or family as ineligible to make the decision. In determining the appropriateness of an individual to make decisions, participants informally apply 'Sanity' and 'Stability' tests, assessing both an inherent ability to reason (modernist repertoire) and the presence of emotion (romanticist repertoire) which might impact on the decision making process. Failure to pass the tests respectively excludes or excuses individuals from decision making. The absence of the romanticist repertoire in dominant models of patient decision making has ethical implications for policy makers and medical practitioners dealing with dying patients and their families.

Decision Support for Environmental Management of Industrial Non-Hazardous Secondary Materials: New Analytical Methods Combined with Simulation and Optimization Modeling

EPA Science Inventory

Non-hazardous solid materials from industrial processes, once regarded as waste and disposed in landfills, offer numerous environmental and economic advantages when put to beneficial uses (BUs). Proper management of these industrial non-hazardous secondary materials (INSM) requir...

Measuring research progress in photovoltaics

NASA Technical Reports Server (NTRS)

Jackson, B.; Mcguire, P.

1986-01-01

The role and some results of the project analysis and integration function in the Flat-plate Solar Array (FSA) Project are presented. Activities included supporting the decision-making process, preparation of plans for project direction, setting goals for project activities, measuring progress within the project, and the development and maintenance of analytical models.

Research on Accounting Should Learn from the Past

ERIC Educational Resources Information Center

Granof, Michael H.; Zeff, Stephen A.

2008-01-01

Starting in the 1960s, academic research on accounting became significantly more quantitative and analytical than in previous decades. The new paradigms have greatly increased our understanding of how financial information affects the decisions of investors as well as managers. However, those models have also crowded out other forms of…

Implementing Operational Analytics using Big Data Technologies to Detect and Predict Sensor Anomalies

NASA Astrophysics Data System (ADS)

Coughlin, J.; Mital, R.; Nittur, S.; SanNicolas, B.; Wolf, C.; Jusufi, R.

2016-09-01

Operational analytics when combined with Big Data technologies and predictive techniques have been shown to be valuable in detecting mission critical sensor anomalies that might be missed by conventional analytical techniques. Our approach helps analysts and leaders make informed and rapid decisions by analyzing large volumes of complex data in near real-time and presenting it in a manner that facilitates decision making. It provides cost savings by being able to alert and predict when sensor degradations pass a critical threshold and impact mission operations. Operational analytics, which uses Big Data tools and technologies, can process very large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, and other relevant information. When combined with predictive techniques, it provides a mechanism to monitor and visualize these data sets and provide insight into degradations encountered in large sensor systems such as the space surveillance network. In this study, data from a notional sensor is simulated and we use big data technologies, predictive algorithms and operational analytics to process the data and predict sensor degradations. This study uses data products that would commonly be analyzed at a site. This study builds on a big data architecture that has previously been proven valuable in detecting anomalies. This paper outlines our methodology of implementing an operational analytic solution through data discovery, learning and training of data modeling and predictive techniques, and deployment. Through this methodology, we implement a functional architecture focused on exploring available big data sets and determine practical analytic, visualization, and predictive technologies.

The EVOTION Decision Support System: Utilizing It for Public Health Policy-Making in Hearing Loss.

PubMed

Katrakazas, Panagiotis; Trenkova, Lyubov; Milas, Josip; Brdaric, Dario; Koutsouris, Dimitris

2017-01-01

As Decision Support Systems start to play a significant role in decision making, especially in the field of public-health policy making, we present an initial attempt to formulate such a system in the concept of public health policy making for hearing loss related problems. Justification for the system's conceptual architecture and its key functionalities are presented. The introduction of the EVOTION DSS sets a key innovation and a basis for paradigm shift in policymaking, by incorporating relevant models, big data analytics and generic demographic data. Expected outcomes for this joint effort are discussed from a public-health point of view.

Expanded envelope concepts for aircraft control-element failure detection and identification

NASA Technical Reports Server (NTRS)

Weiss, Jerold L.; Hsu, John Y.

1988-01-01

The purpose of this effort was to develop and demonstrate concepts for expanding the envelope of failure detection and isolation (FDI) algorithms for aircraft-path failures. An algorithm which uses analytic-redundancy in the form of aerodynamic force and moment balance equations was used. Because aircraft-path FDI uses analytical models, there is a tradeoff between accuracy and the ability to detect and isolate failures. For single flight condition operation, design and analysis methods are developed to deal with this robustness problem. When the departure from the single flight condition is significant, algorithm adaptation is necessary. Adaptation requirements for the residual generation portion of the FDI algorithm are interpreted as the need for accurate, large-motion aero-models, over a broad range of velocity and altitude conditions. For the decision-making part of the algorithm, adaptation may require modifications to filtering operations, thresholds, and projection vectors that define the various hypothesis tests performed in the decision mechanism. Methods of obtaining and evaluating adequate residual generation and decision-making designs have been developed. The application of the residual generation ideas to a high-performance fighter is demonstrated by developing adaptive residuals for the AFTI-F-16 and simulating their behavior under a variety of maneuvers using the results of a NASA F-16 simulation.

Next generation terminology infrastructure to support interprofessional care planning.

PubMed

Collins, Sarah; Klinkenberg-Ramirez, Stephanie; Tsivkin, Kira; Mar, Perry L; Iskhakova, Dina; Nandigam, Hari; Samal, Lipika; Rocha, Roberto A

2017-11-01

Develop a prototype of an interprofessional terminology and information model infrastructure that can enable care planning applications to facilitate patient-centered care, learn care plan linkages and associations, provide decision support, and enable automated, prospective analytics. The study steps included a 3 step approach: (1) Process model and clinical scenario development, and (2) Requirements analysis, and (3) Development and validation of information and terminology models. Components of the terminology model include: Health Concerns, Goals, Decisions, Interventions, Assessments, and Evaluations. A terminology infrastructure should: (A) Include discrete care plan concepts; (B) Include sets of profession-specific concerns, decisions, and interventions; (C) Communicate rationales, anticipatory guidance, and guidelines that inform decisions among the care team; (D) Define semantic linkages across clinical events and professions; (E) Define sets of shared patient goals and sub-goals, including patient stated goals; (F) Capture evaluation toward achievement of goals. These requirements were mapped to AHRQ Care Coordination Measures Framework. This study used a constrained set of clinician-validated clinical scenarios. Terminology models for goals and decisions are unavailable in SNOMED CT, limiting the ability to evaluate these aspects of the proposed infrastructure. Defining and linking subsets of care planning concepts appears to be feasible, but also essential to model interprofessional care planning for common co-occurring conditions and chronic diseases. We recommend the creation of goal dynamics and decision concepts in SNOMED CT to further enable the necessary models. Systems with flexible terminology management infrastructure may enable intelligent decision support to identify conflicting and aligned concerns, goals, decisions, and interventions in shared care plans, ultimately decreasing documentation effort and cognitive burden for clinicians and patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Gut feelings as a third track in general practitioners' diagnostic reasoning.

PubMed

Stolper, Erik; Van de Wiel, Margje; Van Royen, Paul; Van Bokhoven, Marloes; Van der Weijden, Trudy; Dinant, Geert Jan

2011-02-01

General practitioners (GPs) are often faced with complicated, vague problems in situations of uncertainty that they have to solve at short notice. In such situations, gut feelings seem to play a substantial role in their diagnostic process. Qualitative research distinguished a sense of alarm and a sense of reassurance. However, not every GP trusted their gut feelings, since a scientific explanation is lacking. This paper explains how gut feelings arise and function in GPs' diagnostic reasoning. The paper reviews literature from medical, psychological and neuroscientific perspectives. Gut feelings in general practice are based on the interaction between patient information and a GP's knowledge and experience. This is visualized in a knowledge-based model of GPs' diagnostic reasoning emphasizing that this complex task combines analytical and non-analytical cognitive processes. The model integrates the two well-known diagnostic reasoning tracks of medical decision-making and medical problem-solving, and adds gut feelings as a third track. Analytical and non-analytical diagnostic reasoning interacts continuously, and GPs use elements of all three tracks, depending on the task and the situation. In this dual process theory, gut feelings emerge as a consequence of non-analytical processing of the available information and knowledge, either reassuring GPs or alerting them that something is wrong and action is required. The role of affect as a heuristic within the physician's knowledge network explains how gut feelings may help GPs to navigate in a mostly efficient way in the often complex and uncertain diagnostic situations of general practice. Emotion research and neuroscientific data support the unmistakable role of affect in the process of making decisions and explain the bodily sensation of gut feelings.The implications for health care practice and medical education are discussed.

Gut Feelings as a Third Track in General Practitioners’ Diagnostic Reasoning

PubMed Central

Van de Wiel, Margje; Van Royen, Paul; Van Bokhoven, Marloes; Van der Weijden, Trudy; Dinant, Geert Jan

2010-01-01

Background General practitioners (GPs) are often faced with complicated, vague problems in situations of uncertainty that they have to solve at short notice. In such situations, gut feelings seem to play a substantial role in their diagnostic process. Qualitative research distinguished a sense of alarm and a sense of reassurance. However, not every GP trusted their gut feelings, since a scientific explanation is lacking. Objective This paper explains how gut feelings arise and function in GPs’ diagnostic reasoning. Approach The paper reviews literature from medical, psychological and neuroscientific perspectives. Conclusions Gut feelings in general practice are based on the interaction between patient information and a GP’s knowledge and experience. This is visualized in a knowledge-based model of GPs’ diagnostic reasoning emphasizing that this complex task combines analytical and non-analytical cognitive processes. The model integrates the two well-known diagnostic reasoning tracks of medical decision-making and medical problem-solving, and adds gut feelings as a third track. Analytical and non-analytical diagnostic reasoning interacts continuously, and GPs use elements of all three tracks, depending on the task and the situation. In this dual process theory, gut feelings emerge as a consequence of non-analytical processing of the available information and knowledge, either reassuring GPs or alerting them that something is wrong and action is required. The role of affect as a heuristic within the physician’s knowledge network explains how gut feelings may help GPs to navigate in a mostly efficient way in the often complex and uncertain diagnostic situations of general practice. Emotion research and neuroscientific data support the unmistakable role of affect in the process of making decisions and explain the bodily sensation of gut feelings.The implications for health care practice and medical education are discussed. PMID:20967509

Clinical decision making: how surgeons do it.

PubMed

Crebbin, Wendy; Beasley, Spencer W; Watters, David A K

2013-06-01

Clinical decision making is a core competency of surgical practice. It involves two distinct types of mental process best considered as the ends of a continuum, ranging from intuitive and subconscious to analytical and conscious. In practice, individual decisions are usually reached by a combination of each, according to the complexity of the situation and the experience/expertise of the surgeon. An expert moves effortlessly along this continuum, according to need, able to apply learned rules or algorithms to specific presentations, choosing these as a result of either pattern recognition or analytical thinking. The expert recognizes and responds quickly to any mismatch between what is observed and what was expected, coping with gaps in information and making decisions even where critical data may be uncertain or unknown. Even for experts, the cognitive processes involved are difficult to articulate as they tend to be very complex. However, if surgeons are to assist trainees in developing their decision-making skills, the processes need to be identified and defined, and the competency needs to be measurable. This paper examines the processes of clinical decision making in three contexts: making a decision about how to manage a patient; preparing for an operative procedure; and reviewing progress during an operative procedure. The models represented here are an exploration of the complexity of the processes, designed to assist surgeons understand how expert clinical decision making occurs and to highlight the challenge of teaching these skills to surgical trainees. © 2013 The Authors. ANZ Journal of Surgery © 2013 Royal Australasian College of Surgeons.

Shared Problem Models and Crew Decision Making

NASA Technical Reports Server (NTRS)

Orasanu, Judith; Statler, Irving C. (Technical Monitor)

1994-01-01

The importance of crew decision making to aviation safety has been well established through NTSB accident analyses: Crew judgment and decision making have been cited as causes or contributing factors in over half of all accidents in commercial air transport, general aviation, and military aviation. Yet the bulk of research on decision making has not proven helpful in improving the quality of decisions in the cockpit. One reason is that traditional analytic decision models are inappropriate to the dynamic complex nature of cockpit decision making and do not accurately describe what expert human decision makers do when they make decisions. A new model of dynamic naturalistic decision making is offered that may prove more useful for training or aiding cockpit decision making. Based on analyses of crew performance in full-mission simulation and National Transportation Safety Board accident reports, features that define effective decision strategies in abnormal or emergency situations have been identified. These include accurate situation assessment (including time and risk assessment), appreciation of the complexity of the problem, sensitivity to constraints on the decision, timeliness of the response, and use of adequate information. More effective crews also manage their workload to provide themselves with time and resources to make good decisions. In brief, good decisions are appropriate to the demands of the situation and reflect the crew's metacognitive skill. Effective crew decision making and overall performance are mediated by crew communication. Communication contributes to performance because it assures that all crew members have essential information, but it also regulates and coordinates crew actions and is the medium of collective thinking in response to a problem. This presentation will examine the relation between communication that serves to build performance. Implications of these findings for crew training will be discussed.

Determinants of judgment and decision making quality: the interplay between information processing style and situational factors

PubMed Central

Ayal, Shahar; Rusou, Zohar; Zakay, Dan; Hochman, Guy

2015-01-01

A framework is presented to better characterize the role of individual differences in information processing style and their interplay with contextual factors in determining decision making quality. In Experiment 1, we show that individual differences in information processing style are flexible and can be modified by situational factors. Specifically, a situational manipulation that induced an analytical mode of thought improved decision quality. In Experiment 2, we show that this improvement in decision quality is highly contingent on the compatibility between the dominant thinking mode and the nature of the task. That is, encouraging an intuitive mode of thought led to better performance on an intuitive task but hampered performance on an analytical task. The reverse pattern was obtained when an analytical mode of thought was encouraged. We discuss the implications of these results for the assessment of decision making competence, and suggest practical directions to help individuals better adjust their information processing style to the situation at hand and make optimal decisions. PMID:26284011

Determinants of judgment and decision making quality: the interplay between information processing style and situational factors.

PubMed

Ayal, Shahar; Rusou, Zohar; Zakay, Dan; Hochman, Guy

2015-01-01

A framework is presented to better characterize the role of individual differences in information processing style and their interplay with contextual factors in determining decision making quality. In Experiment 1, we show that individual differences in information processing style are flexible and can be modified by situational factors. Specifically, a situational manipulation that induced an analytical mode of thought improved decision quality. In Experiment 2, we show that this improvement in decision quality is highly contingent on the compatibility between the dominant thinking mode and the nature of the task. That is, encouraging an intuitive mode of thought led to better performance on an intuitive task but hampered performance on an analytical task. The reverse pattern was obtained when an analytical mode of thought was encouraged. We discuss the implications of these results for the assessment of decision making competence, and suggest practical directions to help individuals better adjust their information processing style to the situation at hand and make optimal decisions.

Precautionary principles: a jurisdiction-free framework for decision-making under risk.

PubMed

Ricci, Paolo F; Cox, Louis A; MacDonald, Thomas R

2004-12-01

Fundamental principles of precaution are legal maxims that ask for preventive actions, perhaps as contingent interim measures while relevant information about causality and harm remains unavailable, to minimize the societal impact of potentially severe or irreversible outcomes. Such principles do not explain how to make choices or how to identify what is protective when incomplete and inconsistent scientific evidence of causation characterizes the potential hazards. Rather, they entrust lower jurisdictions, such as agencies or authorities, to make current decisions while recognizing that future information can contradict the scientific basis that supported the initial decision. After reviewing and synthesizing national and international legal aspects of precautionary principles, this paper addresses the key question: How can society manage potentially severe, irreversible or serious environmental outcomes when variability, uncertainty, and limited causal knowledge characterize their decision-making? A decision-analytic solution is outlined that focuses on risky decisions and accounts for prior states of information and scientific beliefs that can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision-making under risk, inherent to precautionary decision-making, these (Bayesian) methods help decision-makers and stakeholders because they formally account for probabilistic outcomes, new information, and are consistent and replicable. Rational choice of an action from among various alternatives--defined as a choice that makes preferred consequences more likely--requires accounting for costs, benefits and the change in risks associated with each candidate action. Decisions under any form of the precautionary principle reviewed must account for the contingent nature of scientific information, creating a link to the decision-analytic principle of expected value of information (VOI), to show the relevance of new information, relative to the initial (and smaller) set of data on which the decision was based. We exemplify this seemingly simple situation using risk management of BSE. As an integral aspect of causal analysis under risk, the methods developed in this paper permit the addition of non-linear, hormetic dose-response models to the current set of regulatory defaults such as the linear, non-threshold models. This increase in the number of defaults is an important improvement because most of the variants of the precautionary principle require cost-benefit balancing. Specifically, increasing the set of causal defaults accounts for beneficial effects at very low doses. We also show and conclude that quantitative risk assessment dominates qualitative risk assessment, supporting the extension of the set of default causal models.

A Study of Driver's Route Choice Behavior Based on Evolutionary Game Theory

PubMed Central

Jiang, Xiaowei; Ji, Yanjie; Deng, Wei

2014-01-01

This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers' route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver's route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver's route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent. PMID:25610455

A study of driver's route choice behavior based on evolutionary game theory.

PubMed

Jiang, Xiaowei; Ji, Yanjie; Du, Muqing; Deng, Wei

2014-01-01

This paper proposes a route choice analytic method that embeds cumulative prospect theory in evolutionary game theory to analyze how the drivers adjust their route choice behaviors under the influence of the traffic information. A simulated network with two alternative routes and one variable message sign is built to illustrate the analytic method. We assume that the drivers in the transportation system are bounded rational, and the traffic information they receive is incomplete. An evolutionary game model is constructed to describe the evolutionary process of the drivers' route choice decision-making behaviors. Here we conclude that the traffic information plays an important role in the route choice behavior. The driver's route decision-making process develops towards different evolutionary stable states in accordance with different transportation situations. The analysis results also demonstrate that employing cumulative prospect theory and evolutionary game theory to study the driver's route choice behavior is effective. This analytic method provides an academic support and suggestion for the traffic guidance system, and may optimize the travel efficiency to a certain extent.

Benefits and limitations of using decision analytic tools to assess uncertainty and prioritize Landscape Conservation Cooperative information needs

USGS Publications Warehouse

Post van der Burg, Max; Cullinane Thomas, Catherine; Holcombe, Tracy R.; Nelson, Richard D.

2016-01-01

The Landscape Conservation Cooperatives (LCCs) are a network of partnerships throughout North America that are tasked with integrating science and management to support more effective delivery of conservation at a landscape scale. In order to achieve this integration, some LCCs have adopted the approach of providing their partners with better scientific information in an effort to facilitate more effective and coordinated conservation decisions. Taking this approach has led many LCCs to begin funding research to provide the information for improved decision making. To ensure that funding goes to research projects with the highest likelihood of leading to more integrated broad scale conservation, some LCCs have also developed approaches for prioritizing which information needs will be of most benefit to their partnerships. We describe two case studies in which decision analytic tools were used to quantitatively assess the relative importance of information for decisions made by partners in the Plains and Prairie Potholes LCC. The results of the case studies point toward a few valuable lessons in terms of using these tools with LCCs. Decision analytic tools tend to help shift focus away from research oriented discussions and toward discussions about how information is used in making better decisions. However, many technical experts do not have enough knowledge about decision making contexts to fully inform the latter type of discussion. When assessed in the right decision context, however, decision analyses can point out where uncertainties actually affect optimal decisions and where they do not. This helps technical experts understand that not all research is valuable in improving decision making. But perhaps most importantly, our results suggest that decision analytic tools may be more useful for LCCs as way of developing integrated objectives for coordinating partner decisions across the landscape, rather than simply ranking research priorities.

Merging spatially variant physical process models under an optimized systems dynamics framework.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cain, William O.; Lowry, Thomas Stephen; Pierce, Suzanne A.

The complexity of water resource issues, its interconnectedness to other systems, and the involvement of competing stakeholders often overwhelm decision-makers and inhibit the creation of clear management strategies. While a range of modeling tools and procedures exist to address these problems, they tend to be case specific and generally emphasize either a quantitative and overly analytic approach or present a qualitative dialogue-based approach lacking the ability to fully explore consequences of different policy decisions. The integration of these two approaches is needed to drive toward final decisions and engender effective outcomes. Given these limitations, the Computer Assisted Dispute Resolution systemmore » (CADRe) was developed to aid in stakeholder inclusive resource planning. This modeling and negotiation system uniquely addresses resource concerns by developing a spatially varying system dynamics model as well as innovative global optimization search techniques to maximize outcomes from participatory dialogues. Ultimately, the core system architecture of CADRe also serves as the cornerstone upon which key scientific innovation and challenges can be addressed.« less

On the Multilevel Nature of Meta-Analysis: A Tutorial, Comparison of Software Programs, and Discussion of Analytic Choices.

PubMed

Pastor, Dena A; Lazowski, Rory A

2018-01-01

The term "multilevel meta-analysis" is encountered not only in applied research studies, but in multilevel resources comparing traditional meta-analysis to multilevel meta-analysis. In this tutorial, we argue that the term "multilevel meta-analysis" is redundant since all meta-analysis can be formulated as a special kind of multilevel model. To clarify the multilevel nature of meta-analysis the four standard meta-analytic models are presented using multilevel equations and fit to an example data set using four software programs: two specific to meta-analysis (metafor in R and SPSS macros) and two specific to multilevel modeling (PROC MIXED in SAS and HLM). The same parameter estimates are obtained across programs underscoring that all meta-analyses are multilevel in nature. Despite the equivalent results, not all software programs are alike and differences are noted in the output provided and estimators available. This tutorial also recasts distinctions made in the literature between traditional and multilevel meta-analysis as differences between meta-analytic choices, not between meta-analytic models, and provides guidance to inform choices in estimators, significance tests, moderator analyses, and modeling sequence. The extent to which the software programs allow flexibility with respect to these decisions is noted, with metafor emerging as the most favorable program reviewed.

Anterior surgical management of single-level cervical disc disease: a cost-effectiveness analysis.

PubMed

Lewis, Daniel J; Attiah, Mark A; Malhotra, Neil R; Burnett, Mark G; Stein, Sherman C

2014-12-01

Cost-effectiveness analysis with decision analysis and meta-analysis. To determine the relative cost-effectiveness of anterior cervical discectomy with fusion (with autograft, allograft, or spacers), anterior cervical discectomy without fusion (ACD), and cervical disc replacement (CDR) for the treatment of 1-level cervical disc disease. There is debate as to the optimal anterior surgical strategy to treat single-level cervical disc disease. Surgical strategies include 3 techniques of anterior cervical discectomy with fusion (autograft, allograft, or spacer-assisted fusion), ACD, and CDR. Several controlled trials have compared these treatments but have yielded mixed results. Decision analysis provides a structure for making a quantitative comparison of the costs and outcomes of each treatment. A literature search was performed and yielded 156 case series that fulfilled our search criteria describing nearly 17,000 cases. Data were abstracted from these publications and pooled meta-analytically to estimate the incidence of various outcomes, including index-level and adjacent-level reoperation. A decision analytic model calculated the expected costs in US dollars and outcomes in quality-adjusted life years for a typical adult patient with 1-level cervical radiculopathy subjected to each of the 5 approaches. At 5 years postoperatively, patients who had undergone ACD alone had significantly (P < 0.001) more quality-adjusted life years (4.885 ± 0.041) than those receiving other treatments. Patients with ACD also exhibited highly significant (P < 0.001) differences in costs, incurring the lowest societal costs ($16,558 ± $539). Follow-up data were inadequate for comparison beyond 5 years. The results of our decision analytic model indicate advantages for ACD, both in effectiveness and costs, over other strategies. Thus, ACD is a cost-effective alternative to anterior cervical discectomy with fusion and CDR in patients with single-level cervical disc disease. Definitive conclusions about degenerative changes after ACD and adjacent-level disease after CDR await longer follow-up. 4.

Lightweight Expression of Granular Objects (LEGO) Content Modeling Using the SNOMED CT Observables Model to Represent Nursing Assessment Data.

PubMed

Johnson, Christie

2016-01-01

This poster presentation presents a content modeling strategy using the SNOMED CT Observable Model to represent large amounts of detailed clinical data in a consistent and computable manner that can support multiple use cases. Lightweight Expression of Granular Objects (LEGOs) represent question/answer pairs on clinical data collection forms, where a question is modeled by a (usually) post-coordinated SNOMED CT expression. LEGOs transform electronic patient data into a normalized consumable, which means that the expressions can be treated as extensions of the SNOMED CT hierarchies for the purpose of performing subsumption queries and other analytics. Utilizing the LEGO approach for modeling clinical data obtained from a nursing admission assessment provides a foundation for data exchange across disparate information systems and software applications. Clinical data exchange of computable LEGO patient information enables the development of more refined data analytics, data storage and clinical decision support.

A Subjective Assessment of Alternative Mission Architecture Operations Concepts for the Human Exploration of Mars at NASA Using a Three-Dimensional Multi-Criteria Decision Making Model

NASA Technical Reports Server (NTRS)

Tavana, Madjid

2003-01-01

The primary driver for developing missions to send humans to other planets is to generate significant scientific return. NASA plans human planetary explorations with an acceptable level of risk consistent with other manned operations. Space exploration risks can not be completely eliminated. Therefore, an acceptable level of cost, technical, safety, schedule, and political risks and benefits must be established for exploratory missions. This study uses a three-dimensional multi-criteria decision making model to identify the risks and benefits associated with three alternative mission architecture operations concepts for the human exploration of Mars identified by the Mission Operations Directorate at Johnson Space Center. The three alternatives considered in this study include split, combo lander, and dual scenarios. The model considers the seven phases of the mission including: 1) Earth Vicinity/Departure; 2) Mars Transfer; 3) Mars Arrival; 4) Planetary Surface; 5) Mars Vicinity/Departure; 6) Earth Transfer; and 7) Earth Arrival. Analytic Hierarchy Process (AHP) and subjective probability estimation are used to captures the experts belief concerning the risks and benefits of the three alternative scenarios through a series of sequential, rational, and analytical processes.

Systems thinking, complexity and managerial decision-making: an analytical review.

PubMed

Cramp, D G; Carson, E R

2009-05-01

One feature that characterizes the organization and delivery of health care is its inherent complexity. All too often, with so much information and so many activities involved, it is difficult for decision-makers to determine in an objective fashion an appropriate course of action. It would appear that a holistic rather than a reductionist approach would be advantageous. The aim of this paper is to review how formal systems thinking can aid decision-making in complex situations. Consideration is given as to how the use of a number of systems modelling methodologies can help in gaining an understanding of a complex decision situation. This in turn can enhance the possibility of a decision being made in a more rational, explicit and transparent fashion. The arguments and approaches are illustrated using examples taken from the public health arena.

Meta-analysis in evidence-based healthcare: a paradigm shift away from random effects is overdue.

PubMed

Doi, Suhail A R; Furuya-Kanamori, Luis; Thalib, Lukman; Barendregt, Jan J

2017-12-01

Each year up to 20 000 systematic reviews and meta-analyses are published whose results influence healthcare decisions, thus making the robustness and reliability of meta-analytic methods one of the world's top clinical and public health priorities. The evidence synthesis makes use of either fixed-effect or random-effects statistical methods. The fixed-effect method has largely been replaced by the random-effects method as heterogeneity of study effects led to poor error estimation. However, despite the widespread use and acceptance of the random-effects method to correct this, it too remains unsatisfactory and continues to suffer from defective error estimation, posing a serious threat to decision-making in evidence-based clinical and public health practice. We discuss here the problem with the random-effects approach and demonstrate that there exist better estimators under the fixed-effect model framework that can achieve optimal error estimation. We argue for an urgent return to the earlier framework with updates that address these problems and conclude that doing so can markedly improve the reliability of meta-analytical findings and thus decision-making in healthcare.

Real-life decision making in college students. II: Do individual differences show reliable effects?

PubMed

Galotti, Kathleen M; Tandler, Jane M; Wiener, Hillary J D

2014-01-01

First-year undergraduates participated in a short-term longitudinal study of real-life decision making over their first 14 months of college. They were surveyed about 7 different decisions: choosing courses for upcoming terms (on 3 different occasions), choosing an academic major (twice), planning for the upcoming summer, and planning for sophomore-year housing. They also completed a survey of self-reported decision-making styles and the Need for Cognition survey (Cacioppo & Petty, 1982) to assess their focus on rationality and enjoyment of analytic thinking. Results showed few statistically significant correlations between stylistic measures and behavioral measures of decision making, in either the amount of information considered or the way in which the information integration tracked predictions of linear models of decision making applied to each participant's data. However, there were consistent correlations, across the 7 decisions, between stylistic measures and affective reactions to, or retrospective descriptions of, episodes of decision making. We suggest that decision-making styles instruments may better reflect the construction of narratives of self as a decision maker more than they do actual behavior during decision making.

A Practical Application of Value of Information and Prospective Payback of Research to Prioritize Evaluative Research.

PubMed

Andronis, Lazaros; Billingham, Lucinda J; Bryan, Stirling; James, Nicholas D; Barton, Pelham M

2016-04-01

Efforts to ensure that funded research represents "value for money" have led to increasing calls for the use of analytic methods in research prioritization. A number of analytic approaches have been proposed to assist research funding decisions, the most prominent of which are value of information (VOI) and prospective payback of research (PPoR). Despite the increasing interest in the topic, there are insufficient VOI and PPoR applications on the same case study to contrast their methods and compare their outcomes. We undertook VOI and PPoR analyses to determine the value of conducting 2 proposed research programs. The application served as a vehicle for identifying differences and similarities between the methods, provided insight into the assumptions and practical requirements of undertaking prospective analyses for research prioritization, and highlighted areas for future research. VOI and PPoR were applied to case studies representing proposals for clinical trials in advanced non-small-cell lung cancer and prostate cancer. Decision models were built to synthesize the evidence available prior to the funding decision. VOI (expected value of perfect and sample information) and PPoR (PATHS model) analyses were undertaken using the developed models. VOI and PPoR results agreed in direction, suggesting that the proposed trials would be cost-effective investments. However, results differed in magnitude, largely due to the way each method conceptualizes the possible outcomes of further research and the implementation of research results in practice. Compared with VOI, PPoR is less complex but requires more assumptions. Although the approaches are not free from limitations, they can provide useful input for research funding decisions. © The Author(s) 2015.

Multiple methods for multiple futures: Integrating qualitative scenario planning and quantitative simulation modeling for natural resource decision making

USGS Publications Warehouse

Symstad, Amy J.; Fisichelli, Nicholas A.; Miller, Brian W.; Rowland, Erika; Schuurman, Gregor W.

2017-01-01

Scenario planning helps managers incorporate climate change into their natural resource decision making through a structured “what-if” process of identifying key uncertainties and potential impacts and responses. Although qualitative scenarios, in which ecosystem responses to climate change are derived via expert opinion, often suffice for managers to begin addressing climate change in their planning, this approach may face limits in resolving the responses of complex systems to altered climate conditions. In addition, this approach may fall short of the scientific credibility managers often require to take actions that differ from current practice. Quantitative simulation modeling of ecosystem response to climate conditions and management actions can provide this credibility, but its utility is limited unless the modeling addresses the most impactful and management-relevant uncertainties and incorporates realistic management actions. We use a case study to compare and contrast management implications derived from qualitative scenario narratives and from scenarios supported by quantitative simulations. We then describe an analytical framework that refines the case study’s integrated approach in order to improve applicability of results to management decisions. The case study illustrates the value of an integrated approach for identifying counterintuitive system dynamics, refining understanding of complex relationships, clarifying the magnitude and timing of changes, identifying and checking the validity of assumptions about resource responses to climate, and refining management directions. Our proposed analytical framework retains qualitative scenario planning as a core element because its participatory approach builds understanding for both managers and scientists, lays the groundwork to focus quantitative simulations on key system dynamics, and clarifies the challenges that subsequent decision making must address.

A study on building data warehouse of hospital information system.

PubMed

Li, Ping; Wu, Tao; Chen, Mu; Zhou, Bin; Xu, Wei-guo

2011-08-01

Existing hospital information systems with simple statistical functions cannot meet current management needs. It is well known that hospital resources are distributed with private property rights among hospitals, such as in the case of the regional coordination of medical services. In this study, to integrate and make full use of medical data effectively, we propose a data warehouse modeling method for the hospital information system. The method can also be employed for a distributed-hospital medical service system. To ensure that hospital information supports the diverse needs of health care, the framework of the hospital information system has three layers: datacenter layer, system-function layer, and user-interface layer. This paper discusses the role of a data warehouse management system in handling hospital information from the establishment of the data theme to the design of a data model to the establishment of a data warehouse. Online analytical processing tools assist user-friendly multidimensional analysis from a number of different angles to extract the required data and information. Use of the data warehouse improves online analytical processing and mitigates deficiencies in the decision support system. The hospital information system based on a data warehouse effectively employs statistical analysis and data mining technology to handle massive quantities of historical data, and summarizes from clinical and hospital information for decision making. This paper proposes the use of a data warehouse for a hospital information system, specifically a data warehouse for the theme of hospital information to determine latitude, modeling and so on. The processing of patient information is given as an example that demonstrates the usefulness of this method in the case of hospital information management. Data warehouse technology is an evolving technology, and more and more decision support information extracted by data mining and with decision-making technology is required for further research.

Dual processing model of medical decision-making.

PubMed

Djulbegovic, Benjamin; Hozo, Iztok; Beckstead, Jason; Tsalatsanis, Athanasios; Pauker, Stephen G

2012-09-03

Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. We show that physician's beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker's threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).

Providing guidance for genomics-based cancer treatment decisions: insights from stakeholder engagement for post-prostatectomy radiation therapy.

PubMed

Abe, James; Lobo, Jennifer M; Trifiletti, Daniel M; Showalter, Timothy N

2017-08-24

Despite the emergence of genomics-based risk prediction tools in oncology, there is not yet an established framework for communication of test results to cancer patients to support shared decision-making. We report findings from a stakeholder engagement program that aimed to develop a framework for using Markov models with individualized model inputs, including genomics-based estimates of cancer recurrence probability, to generate personalized decision aids for prostate cancer patients faced with radiation therapy treatment decisions after prostatectomy. We engaged a total of 22 stakeholders, including: prostate cancer patients, urological surgeons, radiation oncologists, genomic testing industry representatives, and biomedical informatics faculty. Slides were at each meeting to provide background information regarding the analytical framework. Participants were invited to provide feedback during the meeting, including revising the overall project aims. Stakeholder meeting content was reviewed and summarized by stakeholder group and by theme. The majority of stakeholder suggestions focused on aspects of decision aid design and formatting. Stakeholders were enthusiastic about the potential value of using decision analysis modeling with personalized model inputs for cancer recurrence risk, as well as competing risks from age and comorbidities, to generate a patient-centered tool to assist decision-making. Stakeholders did not view privacy considerations as a major barrier to the proposed decision aid program. A common theme was that decision aids should be portable across multiple platforms (electronic and paper), should allow for interaction by the user to adjust model inputs iteratively, and available to patients both before and during consult appointments. Emphasis was placed on the challenge of explaining the model's composite result of quality-adjusted life years. A range of stakeholders provided valuable insights regarding the design of a personalized decision aid program, based upon Markov modeling with individualized model inputs, to provide a patient-centered framework to support for genomic-based treatment decisions for cancer patients. The guidance provided by our stakeholders may be broadly applicable to the communication of genomic test results to patients in a patient-centered fashion that supports effective shared decision-making that represents a spectrum of personal factors such as age, medical comorbidities, and individual priorities and values.

Assessing the Operational Effectiveness of a Small Surface Combat Ship in an Anti-Surface Warfare Environment

DTIC Science & Technology

2013-06-01

realistically representing the world in a simulation environment. A screenshot of the combat model used for this research is shown below. There are six...changes in use of technology (Ryan & Jons, 1992). Cost effectiveness and operational effectiveness are important, and it is extremely hard to achieve...effectiveness of ships using simulation and analytical models, to create a ship synthesis model, and most importantly, to develop decision making tools

A Hierarchical Model and Analysis of Factors Affecting the Adoption of Timber as a Bridge

Treesearch

Robert L. Smith; Robert J. Bush; Daniel L. Schmoldt

1995-01-01

The Analytical Hierarchy Process was used to characterize the bridge material selection decisions of highway engineers and local highway officials across the United States. State Department of Transportation engineers, private consulting engineers, and local highway officials were personally interviewed in Mississippi, Virginia, Washington, and Wisconsin to identify...

Comparing Classic and Interval Analytical Hierarchy Process Methodologies for Measuring Area-Level Deprivation to Analyze Health Inequalities.

PubMed

Cabrera-Barona, Pablo; Ghorbanzadeh, Omid

2018-01-16

Deprivation indices are useful measures to study health inequalities. Different techniques are commonly applied to construct deprivation indices, including multi-criteria decision methods such as the analytical hierarchy process (AHP). The multi-criteria deprivation index for the city of Quito is an index in which indicators are weighted by applying the AHP. In this research, a variation of this index is introduced that is calculated using interval AHP methodology. Both indices are compared by applying logistic generalized linear models and multilevel models, considering self-reported health as the dependent variable and deprivation and self-reported quality of life as the independent variables. The obtained results show that the multi-criteria deprivation index for the city of Quito is a meaningful measure to assess neighborhood effects on self-reported health and that the alternative deprivation index using the interval AHP methodology more thoroughly represents the local knowledge of experts and stakeholders. These differences could support decision makers in improving health planning and in tackling health inequalities in more deprived areas.

Comparing Classic and Interval Analytical Hierarchy Process Methodologies for Measuring Area-Level Deprivation to Analyze Health Inequalities

PubMed Central

Cabrera-Barona, Pablo

2018-01-01

Deprivation indices are useful measures to study health inequalities. Different techniques are commonly applied to construct deprivation indices, including multi-criteria decision methods such as the analytical hierarchy process (AHP). The multi-criteria deprivation index for the city of Quito is an index in which indicators are weighted by applying the AHP. In this research, a variation of this index is introduced that is calculated using interval AHP methodology. Both indices are compared by applying logistic generalized linear models and multilevel models, considering self-reported health as the dependent variable and deprivation and self-reported quality of life as the independent variables. The obtained results show that the multi-criteria deprivation index for the city of Quito is a meaningful measure to assess neighborhood effects on self-reported health and that the alternative deprivation index using the interval AHP methodology more thoroughly represents the local knowledge of experts and stakeholders. These differences could support decision makers in improving health planning and in tackling health inequalities in more deprived areas. PMID:29337915

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.

PubMed

Chung, Younjin; Salvador-Carulla, Luis; Salinas-Pérez, José A; Uriarte-Uriarte, Jose J; Iruin-Sanz, Alvaro; García-Alonso, Carlos R

2018-04-25

Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

Graphical assessment of incremental value of novel markers in prediction models: From statistical to decision analytical perspectives.

PubMed

Steyerberg, Ewout W; Vedder, Moniek M; Leening, Maarten J G; Postmus, Douwe; D'Agostino, Ralph B; Van Calster, Ben; Pencina, Michael J

2015-07-01

New markers may improve prediction of diagnostic and prognostic outcomes. We aimed to review options for graphical display and summary measures to assess the predictive value of markers over standard, readily available predictors. We illustrated various approaches using previously published data on 3264 participants from the Framingham Heart Study, where 183 developed coronary heart disease (10-year risk 5.6%). We considered performance measures for the incremental value of adding HDL cholesterol to a prediction model. An initial assessment may consider statistical significance (HR = 0.65, 95% confidence interval 0.53 to 0.80; likelihood ratio p < 0.001), and distributions of predicted risks (densities or box plots) with various summary measures. A range of decision thresholds is considered in predictiveness and receiver operating characteristic curves, where the area under the curve (AUC) increased from 0.762 to 0.774 by adding HDL. We can furthermore focus on reclassification of participants with and without an event in a reclassification graph, with the continuous net reclassification improvement (NRI) as a summary measure. When we focus on one particular decision threshold, the changes in sensitivity and specificity are central. We propose a net reclassification risk graph, which allows us to focus on the number of reclassified persons and their event rates. Summary measures include the binary AUC, the two-category NRI, and decision analytic variants such as the net benefit (NB). Various graphs and summary measures can be used to assess the incremental predictive value of a marker. Important insights for impact on decision making are provided by a simple graph for the net reclassification risk. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

PubMed

Jones, Stephen; Cournane, Seán; Sheehy, Niall; Hederman, Lucy

2016-12-01

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

Review of guidelines for good practice in decision-analytic modelling in health technology assessment.

PubMed

Philips, Z; Ginnelly, L; Sculpher, M; Claxton, K; Golder, S; Riemsma, R; Woolacoot, N; Glanville, J

2004-09-01

To identify existing guidelines and develop a synthesised guideline plus accompanying checklist. In addition to provide guidance on key theoretical, methodological and practical issues and consider the implications of this research for what might be expected of future decision-analytic models. Electronic databases. A systematic review of existing good practice guidelines was undertaken to identify and summarise guidelines currently available for assessing the quality of decision-analytic models that have been undertaken for health technology assessment. A synthesised good practice guidance and accompanying checklist was developed. Two specific methods areas in decision modelling were considered. The first method's topic is the identification of parameter estimates from published literature. Parameter searches were developed and piloted using a case-study model. The second topic relates to bias in parameter estimates; that is, how to adjust estimates of treatment effect from observational studies where there are risks of selection bias. A systematic literature review was conducted to identify those studies looking at quantification of bias in parameter estimates and the implication of this bias. Fifteen studies met the inclusion criteria and were reviewed and consolidated into a single set of brief statements of good practice. From this, a checklist was developed and applied to three independent decision-analytic models. Although the checklist provided excellent guidance on some key issues for model evaluation, it was too general to pick up on the specific nuances of each model. The searches that were developed helped to identify important data for inclusion in the model. However, the quality of life searches proved to be problematic: the published search filters did not focus on those measures specific to cost-effectiveness analysis and although the strategies developed as part of this project were more successful few data were found. Of the 11 studies meeting the criteria on the effect of selection bias, five concluded that a non-randomised trial design is associated with bias and six studies found 'similar' estimates of treatment effects from observational studies or non-randomised clinical trials and randomised controlled trials (RCTs). One purpose of developing the synthesised guideline and checklist was to provide a framework for critical appraisal by the various parties involved in the health technology assessment process. First, the guideline and checklist can be used by groups that are reviewing other analysts' models and, secondly, the guideline and checklist could be used by the various analysts as they develop their models (to use it as a check on how they are developing and reporting their analyses). The Expert Advisory Group (EAG) that was convened to discuss the potential role of the guidance and checklist felt that, in general, the guidance and checklist would be a useful tool, although the checklist is not meant to be used exclusively to determine a model's quality, and so should not be used as a substitute for critical appraisal. The review of current guidelines showed that although authors may provide a consistent message regarding some aspects of modelling, in other areas conflicting attributes are presented in different guidelines. In general, the checklist appears to perform well, in terms of identifying those aspects of the model that should be of particular concern to the reader. The checklist cannot, however, provide answers to the appropriateness of the model structure and structural assumptions, as these may be seen as a general problem with generic checklists and do not reflect any shortcoming with the synthesised guidance and checklist developed here. The assessment of the checklist, as well as feedback from the EAG, indicated the importance of its use in conjunction with a more general checklist or guidelines on economic evaluation. Further methods research into the following areas would be valuable: the quantification of selection bias in non-controlled studies and in controlled observational studies; the level of bias in the different non-RCT study designs; a comparison of results from RCTs with those from other non-randomised studies; assessment of the strengths and weaknesses of alternative ways to adjust for bias in a decision model; and how to prioritise searching for parameter estimates.

Group decision making with the analytic hierarchy process in benefit-risk assessment: a tutorial.

PubMed

Hummel, J Marjan; Bridges, John F P; IJzerman, Maarten J

2014-01-01

The analytic hierarchy process (AHP) has been increasingly applied as a technique for multi-criteria decision analysis in healthcare. The AHP can aid decision makers in selecting the most valuable technology for patients, while taking into account multiple, and even conflicting, decision criteria. This tutorial illustrates the procedural steps of the AHP in supporting group decision making about new healthcare technology, including (1) identifying the decision goal, decision criteria, and alternative healthcare technologies to compare, (2) structuring the decision criteria, (3) judging the value of the alternative technologies on each decision criterion, (4) judging the importance of the decision criteria, (5) calculating group judgments, (6) analyzing the inconsistency in judgments, (7) calculating the overall value of the technologies, and (8) conducting sensitivity analyses. The AHP is illustrated via a hypothetical example, adapted from an empirical AHP analysis on the benefits and risks of tissue regeneration to repair small cartilage lesions in the knee.

Evaluation and determination of soil remediation schemes using a modified AHP model and its application in a contaminated coking plant.

PubMed

Li, Xingang; Li, Jia; Sui, Hong; He, Lin; Cao, Xingtao; Li, Yonghong

2018-07-05

Soil remediation has been considered as one of the most difficult pollution treatment tasks due to its high complexity in contaminants, geological conditions, usage, urgency, etc. The diversity in remediation technologies further makes quick selection of suitable remediation schemes much tougher even the site investigation has been done. Herein, a sustainable decision support hierarchical model has been developed to select, evaluate and determine preferred soil remediation schemes comprehensively based on modified analytic hierarchy process (MAHP). This MAHP method combines competence model and the Grubbs criteria with the conventional AHP. It not only considers the competence differences among experts in group decision, but also adjusts the big deviation caused by different experts' preference through sample analysis. This conversion allows the final remediation decision more reasonable. In this model, different evaluation criteria, including economic effect, environmental effect and technological effect, are employed to evaluate the integrated performance of remediation schemes followed by a strict computation using above MAHP. To confirm the feasibility of this developed model, it has been tested by a benzene workshop contaminated site in Beijing coking plant. Beyond soil remediation, this MAHP model would also be applied in other fields referring to multi-criteria group decision making. Copyright © 2018 Elsevier B.V. All rights reserved.

Methodologies for Optimum Capital Expenditure Decisions for New Medical Technology

PubMed Central

Landau, Thomas P.; Ledley, Robert S.

1980-01-01

This study deals with the development of a theory and an analytical model to support decisions regarding capital expenditures for complex new medical technology. Formal methodologies and quantitative techniques developed by applied mathematicians and management scientists can be used by health planners to develop cost-effective plans for the utilization of medical technology on a community or region-wide basis. In order to maximize the usefulness of the model, it was developed and tested against multiple technologies. The types of technologies studied include capital and labor-intensive technologies, technologies whose utilization rates vary with hospital occupancy rate, technologies whose use can be scheduled, and limited-use and large-use technologies.

An integrated model of clinical reasoning: dual-process theory of cognition and metacognition.

PubMed

Marcum, James A

2012-10-01

Clinical reasoning is an important component for providing quality medical care. The aim of the present paper is to develop a model of clinical reasoning that integrates both the non-analytic and analytic processes of cognition, along with metacognition. The dual-process theory of cognition (system 1 non-analytic and system 2 analytic processes) and the metacognition theory are used to develop an integrated model of clinical reasoning. In the proposed model, clinical reasoning begins with system 1 processes in which the clinician assesses a patient's presenting symptoms, as well as other clinical evidence, to arrive at a differential diagnosis. Additional clinical evidence, if necessary, is acquired and analysed utilizing system 2 processes to assess the differential diagnosis, until a clinical decision is made diagnosing the patient's illness and then how best to proceed therapeutically. Importantly, the outcome of these processes feeds back, in terms of metacognition's monitoring function, either to reinforce or to alter cognitive processes, which, in turn, enhances synergistically the clinician's ability to reason quickly and accurately in future consultations. The proposed integrated model has distinct advantages over other models proposed in the literature for explicating clinical reasoning. Moreover, it has important implications for addressing the paradoxical relationship between experience and expertise, as well as for designing a curriculum to teach clinical reasoning skills. © 2012 Blackwell Publishing Ltd.

Naturalistic Decision Making for Power System Operators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greitzer, Frank L.; Podmore, Robin; Robinson, Marck

2010-02-01

Motivation – Investigations of large-scale outages in the North American interconnected electric system often attribute the causes to three T’s: Trees, Training and Tools. To document and understand the mental processes used by expert operators when making critical decisions, a naturalistic decision making (NDM) model was developed. Transcripts of conversations were analyzed to reveal and assess NDM-based performance criteria. Findings/Design – An item analysis indicated that the operators’ Situation Awareness Levels, mental models, and mental simulations can be mapped at different points in the training scenario. This may identify improved training methods or analytical/ visualization tools. Originality/Value – This studymore » applies for the first time, the concepts of Recognition Primed Decision Making, Situation Awareness Levels and Cognitive Task Analysis to training of electric power system operators. Take away message – The NDM approach provides a viable framework for systematic training management to accelerate learning in simulator-based training scenarios for power system operators and teams.« less

The second iteration of the Systems Prioritization Method: A systems prioritization and decision-aiding tool for the Waste Isolation Pilot Plant: Volume 2, Summary of technical input and model implementation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prindle, N.H.; Mendenhall, F.T.; Trauth, K.

1996-05-01

The Systems Prioritization Method (SPM) is a decision-aiding tool developed by Sandia National Laboratories (SNL). SPM provides an analytical basis for supporting programmatic decisions for the Waste Isolation Pilot Plant (WIPP) to meet selected portions of the applicable US EPA long-term performance regulations. The first iteration of SPM (SPM-1), the prototype for SPM< was completed in 1994. It served as a benchmark and a test bed for developing the tools needed for the second iteration of SPM (SPM-2). SPM-2, completed in 1995, is intended for programmatic decision making. This is Volume II of the three-volume final report of the secondmore » iteration of the SPM. It describes the technical input and model implementation for SPM-2, and presents the SPM-2 technical baseline and the activities, activity outcomes, outcome probabilities, and the input parameters for SPM-2 analysis.« less

Strategies in Forecasting Outcomes in Ethical Decision-making: Identifying and Analyzing the Causes of the Problem

PubMed Central

Beeler, Cheryl K.; Antes, Alison L.; Wang, Xiaoqian; Caughron, Jared J.; Thiel, Chase E.; Mumford, Michael D.

2010-01-01

This study examined the role of key causal analysis strategies in forecasting and ethical decision-making. Undergraduate participants took on the role of the key actor in several ethical problems and were asked to identify and analyze the causes, forecast potential outcomes, and make a decision about each problem. Time pressure and analytic mindset were manipulated while participants worked through these problems. The results indicated that forecast quality was associated with decision ethicality, and the identification of the critical causes of the problem was associated with both higher quality forecasts and higher ethicality of decisions. Neither time pressure nor analytic mindset impacted forecasts or ethicality of decisions. Theoretical and practical implications of these findings are discussed. PMID:20352056

Recognition and source memory as multivariate decision processes.

PubMed

Banks, W P

2000-07-01

Recognition memory, source memory, and exclusion performance are three important domains of study in memory, each with its own findings, it specific theoretical developments, and its separate research literature. It is proposed here that results from all three domains can be treated with a single analytic model. This article shows how to generate a comprehensive memory representation based on multidimensional signal detection theory and how to make predictions for each of these paradigms using decision axes drawn through the space. The detection model is simpler than the comparable multinomial model, it is more easily generalizable, and it does not make threshold assumptions. An experiment using the same memory set for all three tasks demonstrates the analysis and tests the model. The results show that some seemingly complex relations between the paradigms derive from an underlying simplicity of structure.

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, S. M. Shamimul; Fox, Edward A.; Bisset, Keith

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. Asmore » a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK—a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.« less

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

DOE PAGES

Hasan, S. M. Shamimul; Fox, Edward A.; Bisset, Keith; ...

2017-11-06

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. Asmore » a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK—a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.« less

Systematic assessment of benefits and risks: study protocol for a multi-criteria decision analysis using the Analytic Hierarchy Process for comparative effectiveness research

PubMed Central

Singh, Sonal

2013-01-01

Background: Regulatory decision-making involves assessment of risks and benefits of medications at the time of approval or when relevant safety concerns arise with a medication. The Analytic Hierarchy Process (AHP) facilitates decision-making in complex situations involving tradeoffs by considering risks and benefits of alternatives. The AHP allows a more structured method of synthesizing and understanding evidence in the context of importance assigned to outcomes. Our objective is to evaluate the use of an AHP in a simulated committee setting selecting oral medications for type 2 diabetes.  Methods: This study protocol describes the AHP in five sequential steps using a small group of diabetes experts representing various clinical disciplines. The first step will involve defining the goal of the decision and developing the AHP model. In the next step, we will collect information about how well alternatives are expected to fulfill the decision criteria. In the third step, we will compare the ability of the alternatives to fulfill the criteria and judge the importance of eight criteria relative to the decision goal of the optimal medication choice for type 2 diabetes. We will use pairwise comparisons to sequentially compare the pairs of alternative options regarding their ability to fulfill the criteria. In the fourth step, the scales created in the third step will be combined to create a summary score indicating how well the alternatives met the decision goal. The resulting scores will be expressed as percentages and will indicate the alternative medications' relative abilities to fulfill the decision goal. The fifth step will consist of sensitivity analyses to explore the effects of changing the estimates. We will also conduct a cognitive interview and process evaluation.  Discussion: Multi-criteria decision analysis using the AHP will aid, support and enhance the ability of decision makers to make evidence-based informed decisions consistent with their values and preferences. PMID:24555077

Systematic assessment of benefits and risks: study protocol for a multi-criteria decision analysis using the Analytic Hierarchy Process for comparative effectiveness research.

PubMed

Maruthur, Nisa M; Joy, Susan; Dolan, James; Segal, Jodi B; Shihab, Hasan M; Singh, Sonal

2013-01-01

Regulatory decision-making involves assessment of risks and benefits of medications at the time of approval or when relevant safety concerns arise with a medication. The Analytic Hierarchy Process (AHP) facilitates decision-making in complex situations involving tradeoffs by considering risks and benefits of alternatives. The AHP allows a more structured method of synthesizing and understanding evidence in the context of importance assigned to outcomes. Our objective is to evaluate the use of an AHP in a simulated committee setting selecting oral medications for type 2 diabetes.  This study protocol describes the AHP in five sequential steps using a small group of diabetes experts representing various clinical disciplines. The first step will involve defining the goal of the decision and developing the AHP model. In the next step, we will collect information about how well alternatives are expected to fulfill the decision criteria. In the third step, we will compare the ability of the alternatives to fulfill the criteria and judge the importance of eight criteria relative to the decision goal of the optimal medication choice for type 2 diabetes. We will use pairwise comparisons to sequentially compare the pairs of alternative options regarding their ability to fulfill the criteria. In the fourth step, the scales created in the third step will be combined to create a summary score indicating how well the alternatives met the decision goal. The resulting scores will be expressed as percentages and will indicate the alternative medications' relative abilities to fulfill the decision goal. The fifth step will consist of sensitivity analyses to explore the effects of changing the estimates. We will also conduct a cognitive interview and process evaluation.  Multi-criteria decision analysis using the AHP will aid, support and enhance the ability of decision makers to make evidence-based informed decisions consistent with their values and preferences.

Evaluating the decision accuracy and speed of clinical data visualizations.

PubMed

Pieczkiewicz, David S; Finkelstein, Stanley M

2010-01-01

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

Incorporating uncertainty in watershed management decision-making: A mercury TMDL case study

USGS Publications Warehouse

Labiosa, W.; Leckie, J.; Shachter, R.; Freyberg, D.; Rytuba, J.; ,

2005-01-01

Water quality impairment due to high mercury fish tissue concentrations and high mercury aqueous concentrations is a widespread problem in several sub-watersheds that are major sources of mercury to the San Francisco Bay. Several mercury Total Maximum Daily Load regulations are currently being developed to address this problem. Decisions about control strategies are being made despite very large uncertainties about current mercury loading behavior, relationships between total mercury loading and methyl mercury formation, and relationships between potential controls and mercury fish tissue levels. To deal with the issues of very large uncertainties, data limitations, knowledge gaps, and very limited State agency resources, this work proposes a decision analytical alternative for mercury TMDL decision support. The proposed probabilistic decision model is Bayesian in nature and is fully compatible with a "learning while doing" adaptive management approach. Strategy evaluation, sensitivity analysis, and information collection prioritization are examples of analyses that can be performed using this approach.

Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Saxena, Abhinav; Daigle, Matthew; Goebel, Kai

2013-01-01

This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.

When can scientific studies promote consensus among conflicting stakeholders?

PubMed

Small, Mitchell J; Güvenç, Ümit; DeKay, Michael L

2014-11-01

While scientific studies may help conflicting stakeholders come to agreement on a best management option or policy, often they do not. We review the factors affecting trust in the efficacy and objectivity of scientific studies in an analytical-deliberative process where conflict is present, and show how they may be incorporated in an extension to the traditional Bayesian decision model. The extended framework considers stakeholders who differ in their prior beliefs regarding the probability of possible outcomes (in particular, whether a proposed technology is hazardous), differ in their valuations of these outcomes, and differ in their assessment of the ability of a proposed study to resolve the uncertainty in the outcomes and their hazards--as measured by their perceived false positive and false negative rates for the study. The Bayesian model predicts stakeholder-specific preposterior probabilities of consensus, as well as pathways for increasing these probabilities, providing important insights into the value of scientific information in an analytic-deliberative decision process where agreement is sought. It also helps to identify the interactions among perceived risk and benefit allocations, scientific beliefs, and trust in proposed scientific studies when determining whether a consensus can be achieved. The article provides examples to illustrate the method, including an adaptation of a recent decision analysis for managing the health risks of electromagnetic fields from high voltage transmission lines. © 2014 Society for Risk Analysis.

Treatment effects model for assessing disease management: measuring outcomes and strengthening program management.

PubMed

Wendel, Jeanne; Dumitras, Diana

2005-06-01

This paper describes an analytical methodology for obtaining statistically unbiased outcomes estimates for programs in which participation decisions may be correlated with variables that impact outcomes. This methodology is particularly useful for intraorganizational program evaluations conducted for business purposes. In this situation, data is likely to be available for a population of managed care members who are eligible to participate in a disease management (DM) program, with some electing to participate while others eschew the opportunity. The most pragmatic analytical strategy for in-house evaluation of such programs is likely to be the pre-intervention/post-intervention design in which the control group consists of people who were invited to participate in the DM program, but declined the invitation. Regression estimates of program impacts may be statistically biased if factors that impact participation decisions are correlated with outcomes measures. This paper describes an econometric procedure, the Treatment Effects model, developed to produce statistically unbiased estimates of program impacts in this type of situation. Two equations are estimated to (a) estimate the impacts of patient characteristics on decisions to participate in the program, and then (b) use this information to produce a statistically unbiased estimate of the impact of program participation on outcomes. This methodology is well-established in economics and econometrics, but has not been widely applied in the DM outcomes measurement literature; hence, this paper focuses on one illustrative application.

A cost-effectiveness analysis of prophylactic surgery versus gynecologic surveillance for women from hereditary non-polyposis colorectal cancer (HNPCC) Families.

PubMed

Yang, Kathleen Y; Caughey, Aaron B; Little, Sarah E; Cheung, Michael K; Chen, Lee-May

2011-09-01

Women at risk for Lynch Syndrome/HNPCC have an increased lifetime risk of endometrial and ovarian cancer. This study investigates the cost-effectiveness of prophylactic surgery versus surveillance in women with Lynch Syndrome. A decision analytic model was designed incorporating key clinical decisions and existing probabilities, costs, and outcomes from the literature. Clinical forum where risk-reducing surgery and surveillance were considered. A theoretical population of women with Lynch Syndrome at age 30 was used for the analysis. A decision analytic model was designed comparing the health outcomes of prophylactic hysterectomy with bilateral salpingo-oophorectomy at age 30 versus annual gynecologic screening versus annual gynecologic exam. The literature was searched for probabilities of different health outcomes, results of screening modalities, and costs of cancer diagnosis and treatment. Cost-effectiveness expressed in dollars per discounted life-years. Risk-reducing surgery is the least expensive option, costing $23,422 per patient for 25.71 quality-adjusted life-years (QALYs). Annual screening costs $68,392 for 25.17 QALYs; and annual examination without screening costs $100,484 for 24.60 QALYs. Further, because risk-reducing surgery leads to both the lowest costs and the highest number of QALYs, it is a dominant strategy. Risk-reducing surgery is the most cost-effective option from a societal healthcare cost perspective.

The effect of stimulus strength on the speed and accuracy of a perceptual decision.

PubMed

Palmer, John; Huk, Alexander C; Shadlen, Michael N

2005-05-02

Both the speed and the accuracy of a perceptual judgment depend on the strength of the sensory stimulation. When stimulus strength is high, accuracy is high and response time is fast; when stimulus strength is low, accuracy is low and response time is slow. Although the psychometric function is well established as a tool for analyzing the relationship between accuracy and stimulus strength, the corresponding chronometric function for the relationship between response time and stimulus strength has not received as much consideration. In this article, we describe a theory of perceptual decision making based on a diffusion model. In it, a decision is based on the additive accumulation of sensory evidence over time to a bound. Combined with simple scaling assumptions, the proportional-rate and power-rate diffusion models predict simple analytic expressions for both the chronometric and psychometric functions. In a series of psychophysical experiments, we show that this theory accounts for response time and accuracy as a function of both stimulus strength and speed-accuracy instructions. In particular, the results demonstrate a close coupling between response time and accuracy. The theory is also shown to subsume the predictions of Piéron's Law, a power function dependence of response time on stimulus strength. The theory's analytic chronometric function allows one to extend theories of accuracy to response time.

Cost-effectiveness modelling in diagnostic imaging: a stepwise approach.

PubMed

Sailer, Anna M; van Zwam, Wim H; Wildberger, Joachim E; Grutters, Janneke P C

2015-12-01

Diagnostic imaging (DI) is the fastest growing sector in medical expenditures and takes a central role in medical decision-making. The increasing number of various and new imaging technologies induces a growing demand for cost-effectiveness analysis (CEA) in imaging technology assessment. In this article we provide a comprehensive framework of direct and indirect effects that should be considered for CEA in DI, suitable for all imaging modalities. We describe and explain the methodology of decision analytic modelling in six steps aiming to transfer theory of CEA to clinical research by demonstrating key principles of CEA in a practical approach. We thereby provide radiologists with an introduction to the tools necessary to perform and interpret CEA as part of their research and clinical practice. • DI influences medical decision making, affecting both costs and health outcome. • This article provides a comprehensive framework for CEA in DI. • A six-step methodology for conducting and interpreting cost-effectiveness modelling is proposed.

Parental decisions, child health and valuation of avoiding arsenic in drinking water in rural Bangladesh.

PubMed

Aziz, Sonia N; Boyle, Kevin J; Crocker, Tom

2015-03-01

Arsenic contamination of groundwater in Bangladesh is a widespread public health hazard. Water sources without high arsenic levels are scarce, affecting people's availability for work and other activities when they have to seek safe water to drink. While children are particularly susceptible to chronic arsenic exposure, limited information and heavy constraints on resources may preclude people in developing countries from taking protective actions. Since parents are primary decision-makers for children, a model of stochastic decision-making analytically linking parent health and child health is used to frame the valuation of avoiding arsenic exposure using an averting behavior model. The results show that safe drinking water programs do work and that people do take protective actions. The results can help guide public health mitigation policies, and examine whether factors such as child health and time required for remediation have an effect on mitigation measures.

Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample

PubMed Central

Oakley, Jeremy E.; Brennan, Alan; Breeze, Penny

2015-01-01

Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method. PMID:25810269

A proposed model for economic evaluations of major depressive disorder.

PubMed

Haji Ali Afzali, Hossein; Karnon, Jonathan; Gray, Jodi

2012-08-01

In countries like UK and Australia, the comparability of model-based analyses is an essential aspect of reimbursement decisions for new pharmaceuticals, medical services and technologies. Within disease areas, the use of models with alternative structures, type of modelling techniques and/or data sources for common parameters reduces the comparability of evaluations of alternative technologies for the same condition. The aim of this paper is to propose a decision analytic model to evaluate long-term costs and benefits of alternative management options in patients with depression. The structure of the proposed model is based on the natural history of depression and includes clinical events that are important from both clinical and economic perspectives. Considering its greater flexibility with respect to handling time, discrete event simulation (DES) is an appropriate simulation platform for modelling studies of depression. We argue that the proposed model can be used as a reference model in model-based studies of depression improving the quality and comparability of studies.

A Dual-Process Account of the Development of Scientific Reasoning: The Nature and Development of Metacognitive Intercession Skills

ERIC Educational Resources Information Center

Amsel, Eric; Klaczynski, Paul A.; Johnston, Adam; Bench, Shane; Close, Jason; Sadler, Eric; Walker, Rick

2008-01-01

Metacognitive knowledge of the dual-processing basis of judgment is critical to resolving conflict between analytic and experiential processing responses [Klaczynski, P. A. (2004). A dual-process model of adolescent development: Implications for decision making, reasoning, and identity. In R. V. Kail (Ed.), "Advances in child development and…

Quantifying dispersal of southern pine beetles with mark-recapture experiments and a diffusion model

Treesearch

P. Turchin; W.T. Thoeny

1993-01-01

Pest management decisions should take into consideration quantitative information on dispersal of insect pests, but such information is often lacking.The goal of this study was to measure intraforest dispersal in the southern pine beetle (SPB).We developed an analytical formula for interpreting data from mark-recapture studies of insect dispersal.The proposed...

The Relationships between Perceived Teaching Behaviors and Motivation in Physical Education: A One-Year Longitudinal Study

ERIC Educational Resources Information Center

Koka, Andre

2013-01-01

This study aimed to examine the direction of relationships between specific dimensions of perceived teaching behaviors and motivation in physical education over time among 330 secondary school students. Cross-lagged path-analytic models revealed that autonomous motivation was reciprocally related over time with perceived decision-making style, and…

Assessing Generalisability in Model-Based Economic Evaluation Studies: A Structured Review in Osteoporosis

PubMed Central

Urdahl, Hege; Manca, Andrea; Sculpher, Mark J

2008-01-01

Background To support decision making many countries have now introduced some formal assessment process to evaluate whether health technologies represent good ‘value for money’. These often take the form of decision models which can be used to explore elements of importance to generalisability of study results across clinical settings and jurisdictions. The objectives of the present review were to assess: (i) whether the published studies clearly defined the decision-making audience for the model; (ii) the transparency of the reporting in terms of study question, structure and data inputs; (iii) the relevance of the data inputs used in the model to the stated decision-maker or jurisdiction; and (iv) how fully the robustness of the model's results to variation in data inputs between locations was assessed. Methods Articles reporting decision-analytic models in the area of osteoporosis were assessed to establish the extent to which the information provided enabled decision makers in different countries/jurisdictions to fully appreciate the variability of results according to location, and the relevance to their own. Results Of the 18 articles included in the review, only three explicitly stated the decision-making audience. It was not possible to infer a decision-making audience in eight studies. Target population was well reported, as was resource and cost data, and clinical data used for estimates of relative risk reduction. However, baseline risk was rarely adapted to the relevant jurisdiction, and when no decision-maker was explicit it was difficult to assess whether the reported cost and resource use data was in fact relevant. A few studies used sensitivity analysis to explore elements of generalisability, such as compliance rates and baseline fracture risk rates, although such analyses were generally restricted to evaluating parameter uncertainty. Conclusion This review found that variability in cost-effectiveness across locations is addressed to a varying extent in modelling studies in the field of osteoporosis, limiting their use for decision-makers across different locations. Transparency of reporting is expected to increase as methodology develops, and decision-makers publish “reference case” type guidance. PMID:17129074

Laboratory diagnosis of gestational diabetes: An in silico investigation into the effects of pre-analytical processing on the diagnostic sensitivity and specificity of the oral glucose tolerance test.

PubMed

Mansell, Erin; Lunt, Helen; Docherty, Paul

2017-06-01

Delayed separation of red cells from plasma causes pre analytical glucose loss, which in turn results in an under-diagnosis of GDM (gestational diabetes) based on the OGTT (oral glucose tolerance test). In silico investigations may help laboratory decision making, when exploring pragmatic improvements to sample processing. Late pregnancy 0, 1 and 2h 75g OGTT values were obtained from two distinct populations of pregnant women: 1. Values derived from the HAPO (Hyperglycemia and Adverse Pregnancy Outcome) Study and 2. New Zealand women identified as at higher risk of GDM by their caregivers, undergoing OGTT during routine antenatal care. In both populations studied, in silico modelling focussed on the effects of pre-analytical delays in plasma separation, when using fluoride collection tubes. Using a model that 'batched' samples from the three OGTT collection times, diagnostic sensitivity was estimated as follows: 66.1% for HAPO research population and 48.4% for the 1305 women receiving routine antenatal care. If samples were not batched, but processed shortly after each blood sample was collected, then sensitivity increased to 81%. Exploration of a range of clinical and laboratory scenarios using in silico modelling, showed that delaying the processing of pregnancy OGTT samples, using batched sample collection into fluoride tubes, causes unacceptable loss of GDM diagnostic sensitivity across two distinct population groups. This modelling approach will hopefully provide information that helps with final decision making around improved laboratory processing techniques. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

PubMed

El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher

2018-01-01

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

Fusion Analytics: A Data Integration System for Public Health and Medical Disaster Response Decision Support

PubMed Central

Passman, Dina B.

2013-01-01

Objective The objective of this demonstration is to show conference attendees how they can integrate, analyze, and visualize diverse data type data from across a variety of systems by leveraging an off-the-shelf enterprise business intelligence (EBI) solution to support decision-making in disasters. Introduction Fusion Analytics is the data integration system developed by the Fusion Cell at the U.S. Department of Health and Human Services (HHS), Office of the Assistant Secretary for Preparedness and Response (ASPR). Fusion Analytics meaningfully augments traditional public and population health surveillance reporting by providing web-based data analysis and visualization tools. Methods Fusion Analytics serves as a one-stop-shop for the web-based data visualizations of multiple real-time data sources within ASPR. The 24-7 web availability makes it an ideal analytic tool for situational awareness and response allowing stakeholders to access the portal from any internet-enabled device without installing any software. The Fusion Analytics data integration system was built using off-the-shelf EBI software. Fusion Analytics leverages the full power of statistical analysis software and delivers reports to users in a secure web-based environment. Fusion Analytics provides an example of how public health staff can develop and deploy a robust public health informatics solution using an off-the shelf product and with limited development funding. It also provides the unique example of a public health information system that combines patient data for traditional disease surveillance with manpower and resource data to provide overall decision support for federal public health and medical disaster response operations. Conclusions We are currently in a unique position within public health. One the one hand, we have been gaining greater and greater access to electronic data of all kinds over the last few years. On the other, we are working in a time of reduced government spending to support leveraging this data for decision support with robust analytics and visualizations. Fusion Analytics provides an opportunity for attendees to see how various types of data are integrated into a single application for population health decision support. It also can provide them with ideas of how they can use their own staff to create analyses and reports that support their public health activities.

Appropriate evidence sources for populating decision analytic models within health technology assessment (HTA): a systematic review of HTA manuals and health economic guidelines.

PubMed

Zechmeister-Koss, Ingrid; Schnell-Inderst, Petra; Zauner, Günther

2014-04-01

An increasing number of evidence sources are relevant for populating decision analytic models. What is needed is detailed methodological advice on which type of data is to be used for what type of model parameter. We aim to identify standards in health technology assessment manuals and economic (modeling) guidelines on appropriate evidence sources and on the role different types of data play within a model. Documents were identified via a call among members of the International Network of Agencies for Health Technology Assessment and by hand search. We included documents from Europe, the United States, Canada, Australia, and New Zealand as well as transnational guidelines written in English or German. We systematically summarized in a narrative manner information on appropriate evidence sources for model parameters, their advantages and limitations, data identification methods, and data quality issues. A large variety of evidence sources for populating models are mentioned in the 28 documents included. They comprise research- and non-research-based sources. Valid and less appropriate sources are identified for informing different types of model parameters, such as clinical effect size, natural history of disease, resource use, unit costs, and health state utility values. Guidelines do not provide structured and detailed advice on this issue. The article does not include information from guidelines in languages other than English or German, and the information is not tailored to specific modeling techniques. The usability of guidelines and manuals for modeling could be improved by addressing the issue of evidence sources in a more structured and comprehensive format.

A novel computer based expert decision making model for prostate cancer disease management.

PubMed

Richman, Martin B; Forman, Ernest H; Bayazit, Yildirim; Einstein, Douglas B; Resnick, Martin I; Stovsky, Mark D

2005-12-01

We propose a strategic, computer based, prostate cancer decision making model based on the analytic hierarchy process. We developed a model that improves physician-patient joint decision making and enhances the treatment selection process by making this critical decision rational and evidence based. Two groups (patient and physician-expert) completed a clinical study comparing an initial disease management choice with the highest ranked option generated by the computer model. Participants made pairwise comparisons to derive priorities for the objectives and subobjectives related to the disease management decision. The weighted comparisons were then applied to treatment options to yield prioritized rank lists that reflect the likelihood that a given alternative will achieve the participant treatment goal. Aggregate data were evaluated by inconsistency ratio analysis and sensitivity analysis, which assessed the influence of individual objectives and subobjectives on the final rank list of treatment options. Inconsistency ratios less than 0.05 were reliably generated, indicating that judgments made within the model were mathematically rational. The aggregate prioritized list of treatment options was tabulated for the patient and physician groups with similar outcomes for the 2 groups. Analysis of the major defining objectives in the treatment selection decision demonstrated the same rank order for the patient and physician groups with cure, survival and quality of life being more important than controlling cancer, preventing major complications of treatment, preventing blood transfusion complications and limiting treatment cost. Analysis of subobjectives, including quality of life and sexual dysfunction, produced similar priority rankings for the patient and physician groups. Concordance between initial treatment choice and the highest weighted model option differed between the groups with the patient group having 59% concordance and the physician group having only 42% concordance. This study successfully validated the usefulness of a computer based prostate cancer management decision making model to produce individualized, rational, clinically appropriate disease management decisions without physician bias.

Career Decision Statuses among Portuguese Secondary School Students: A Cluster Analytical Approach

ERIC Educational Resources Information Center

Santos, Paulo Jorge; Ferreira, Joaquim Armando

2012-01-01

Career indecision is a complex phenomenon and an increasing number of authors have proposed that undecided individuals do not form a group with homogeneous characteristics. This study examines career decision statuses among a sample of 362 12th-grade Portuguese students. A cluster-analytical procedure, based on a battery of instruments designed to…

An improved hybrid multi-criteria/multidimensional model for strategic industrial location selection: Casablanca industrial zones as a case study.

PubMed

Boutkhoum, Omar; Hanine, Mohamed; Agouti, Tarik; Tikniouine, Abdessadek

2015-01-01

In this paper, we examine the issue of strategic industrial location selection in uncertain decision making environments for implanting new industrial corporation. In fact, the industrial location issue is typically considered as a crucial factor in business research field which is related to many calculations about natural resources, distributors, suppliers, customers, and most other things. Based on the integration of environmental, economic and social decisive elements of sustainable development, this paper presents a hybrid decision making model combining fuzzy multi-criteria analysis with analytical capabilities that OLAP systems can provide for successful and optimal industrial location selection. The proposed model mainly consists in three stages. In the first stage, a decision-making committee has been established to identify the evaluation criteria impacting the location selection process. In the second stage, we develop fuzzy AHP software based on the extent analysis method to assign the importance weights to the selected criteria, which allows us to model the linguistic vagueness, ambiguity, and incomplete knowledge. In the last stage, OLAP analysis integrated with multi-criteria analysis employs these weighted criteria as inputs to evaluate, rank and select the strategic industrial location for implanting new business corporation in the region of Casablanca, Morocco. Finally, a sensitivity analysis is performed to evaluate the impact of criteria weights and the preferences given by decision makers on the final rankings of strategic industrial locations.

Diverting the tourists: a spatial decision-support system for tourism planning on a developing island

NASA Astrophysics Data System (ADS)

Beedasy, Jaishree; Whyatt, Duncan

Mauritius is a small island (1865 km 2) in the Indian Ocean. Tourism is the third largest economic sector of the country, after manufacturing and agriculture. A limitation of space and the island's vulnerable ecosystem warrants a rational approach to tourism development. The main problems so far have been to manipulate and integrate all the factors affecting tourism planning and to match spatial data with their relevant attributes. A Spatial Decision Support System (SDSS) for sustainable tourism planning is therefore proposed. The proposed SDSS design would include a GIS as its core component. A first GIS model has already been constructed with available data. Supporting decision-making in a spatial context is implicit in the use of GIS. However the analytical capability of the GIS has to be enhanced to solve semi-structured problems, where subjective judgements come into play. The second part of the paper deals with the choice, implementation and customisation of a relevant model to develop a specialised SDSS. Different types of models and techniques are discussed, in particular a comparison of compensatory and non-compensatory approaches to multicriteria evaluation (MCE). It is concluded that compensatory multicriteria evaluation techniques increase the scope of the present GIS model as a decision-support tool. This approach gives the user or decision-maker the flexibility to change the importance of each criterion depending on relevant objectives.

XWeB: The XML Warehouse Benchmark

NASA Astrophysics Data System (ADS)

Mahboubi, Hadj; Darmont, Jérôme

With the emergence of XML as a standard for representing business data, new decision support applications are being developed. These XML data warehouses aim at supporting On-Line Analytical Processing (OLAP) operations that manipulate irregular XML data. To ensure feasibility of these new tools, important performance issues must be addressed. Performance is customarily assessed with the help of benchmarks. However, decision support benchmarks do not currently support XML features. In this paper, we introduce the XML Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from the relational decision support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision support workload. XWeB's usage is illustrated by experiments on several XML database management systems.

Context is everything or how could I have been that stupid?

PubMed

Croskerry, Pat

2009-01-01

Dual Process Theory provides a useful working model of decision-making. It broadly divides decision-making into intuitive (System 1) and analytical (System 2) processes. System 1 is especially dependent on contextual cues. There appears to be a universal human tendency to contextualize information, mostly in an effort to imbue meaning but also, perhaps, to conserve cognitive energy. Most decision errors occur in System 1, and this has two major implications. The first is that insufficient account may have been taken out of context when the original decision was made. Secondly, in trying to learn from decision failures, we need the highest fidelity of context reconstruction as possible. It should be appreciated that learning from past events is inevitably an imperfect process. Retrospective investigations, such as root-cause analysis, critical incident review, morbidity and mortality rounds and legal investigations, all suffer the limitation that they cannot faithfully reconstruct the context in which decisions were made and from which actions followed.

Cost-Utility Analysis of Bariatric Surgery in Italy: Results of Decision-Analytic Modelling

PubMed Central

Lucchese, Marcello; Borisenko, Oleg; Mantovani, Lorenzo Giovanni; Cortesi, Paolo Angelo; Cesana, Giancarlo; Adam, Daniel; Burdukova, Elisabeth; Lukyanov, Vasily; Di Lorenzo, Nicola

2017-01-01

Objective To evaluate the cost-effectiveness of bariatric surgery in Italy from a third-party payer perspective over a medium-term (10 years) and a long-term (lifetime) horizon. Methods A state-transition Markov model was developed, in which patients may experience surgery, post-surgery complications, diabetes mellitus type 2, cardiovascular diseases or die. Transition probabilities, costs, and utilities were obtained from the Italian and international literature. Three types of surgeries were considered: gastric bypass, sleeve gastrectomy, and adjustable gastric banding. A base-case analysis was performed for the population, the characteristics of which were obtained from surgery candidates in Italy. Results In the base-case analysis, over 10 years, bariatric surgery led to cost increment of EUR 2,661 and generated additional 1.1 quality-adjusted life years (QALYs). Over a lifetime, surgery led to savings of EUR 8,649, additional 0.5 life years and 3.2 QALYs. Bariatric surgery was cost-effective at 10 years with an incremental cost-effectiveness ratio of EUR 2,412/QALY and dominant over conservative management over a lifetime. Conclusion In a comprehensive decision analytic model, a current mix of surgical methods for bariatric surgery was cost-effective at 10 years and cost-saving over the lifetime of the Italian patient cohort considered in this analysis. PMID:28601866

Cost utility analysis of endoscopic biliary stent in unresectable hilar cholangiocarcinoma: decision analytic modeling approach.

PubMed

Sangchan, Apichat; Chaiyakunapruk, Nathorn; Supakankunti, Siripen; Pugkhem, Ake; Mairiang, Pisaln

2014-01-01

Endoscopic biliary drainage using metal and plastic stent in unresectable hilar cholangiocarcinoma (HCA) is widely used but little is known about their cost-effectiveness. This study evaluated the cost-utility of endoscopic metal and plastic stent drainage in unresectable complex, Bismuth type II-IV, HCA patients. Decision analytic model, Markov model, was used to evaluate cost and quality-adjusted life year (QALY) of endoscopic biliary drainage in unresectable HCA. Costs of treatment and utilities of each Markov state were retrieved from hospital charges and unresectable HCA patients from tertiary care hospital in Thailand, respectively. Transition probabilities were derived from international literature. Base case analyses and sensitivity analyses were performed. Under the base-case analysis, metal stent is more effective but more expensive than plastic stent. An incremental cost per additional QALY gained is 192,650 baht (US$ 6,318). From probabilistic sensitivity analysis, at the willingness to pay threshold of one and three times GDP per capita or 158,000 baht (US$ 5,182) and 474,000 baht (US$ 15,546), the probability of metal stent being cost-effective is 26.4% and 99.8%, respectively. Based on the WHO recommendation regarding the cost-effectiveness threshold criteria, endoscopic metal stent drainage is cost-effective compared to plastic stent in unresectable complex HCA.

A Quantum-Like View to a Generalized Two Players Game

NASA Astrophysics Data System (ADS)

Bagarello, F.

2015-10-01

This paper consider the possibility of using some quantum tools in decision making strategies. In particular, we consider here a dynamical open quantum system helping two players, and , to take their decisions in a specific context. We see that, within our approach, the final choices of the players do not depend in general on their initial mental states, but they are driven essentially by the environment which interacts with them. The model proposed here also considers interactions of different nature between the two players, and it is simple enough to allow for an analytical solution of the equations of motion.

An agent-based simulation model to study accountable care organizations.

PubMed

Liu, Pai; Wu, Shinyi

2016-03-01

Creating accountable care organizations (ACOs) has been widely discussed as a strategy to control rapidly rising healthcare costs and improve quality of care; however, building an effective ACO is a complex process involving multiple stakeholders (payers, providers, patients) with their own interests. Also, implementation of an ACO is costly in terms of time and money. Immature design could cause safety hazards. Therefore, there is a need for analytical model-based decision-support tools that can predict the outcomes of different strategies to facilitate ACO design and implementation. In this study, an agent-based simulation model was developed to study ACOs that considers payers, healthcare providers, and patients as agents under the shared saving payment model of care for congestive heart failure (CHF), one of the most expensive causes of sometimes preventable hospitalizations. The agent-based simulation model has identified the critical determinants for the payment model design that can motivate provider behavior changes to achieve maximum financial and quality outcomes of an ACO. The results show nonlinear provider behavior change patterns corresponding to changes in payment model designs. The outcomes vary by providers with different quality or financial priorities, and are most sensitive to the cost-effectiveness of CHF interventions that an ACO implements. This study demonstrates an increasingly important method to construct a healthcare system analytics model that can help inform health policy and healthcare management decisions. The study also points out that the likely success of an ACO is interdependent with payment model design, provider characteristics, and cost and effectiveness of healthcare interventions.

To Issue of Mathematical Management Methods Applied for Investment-Building Complex under Conditions of Economic Crisis

NASA Astrophysics Data System (ADS)

Novikova, V.; Nikolaeva, O.

2017-11-01

In the article the authors consider a cognitive management method of the investment-building complex in the crisis conditions. The factors influencing the choice of an investment strategy are studied, the basic lines of the activity in the field of crisis-management from a position of mathematical modelling are defined. The general approach to decision-making on investment in real assets on the basis of the discrete systems based on the optimum control theory is offered. With the use of a discrete maximum principle the task in view of the decision is found. The numerical algorithm to define the optimum control is formulated by investments. Analytical decisions for the case of constant profitability of the basic means are obtained.

Decision Support | Solar Research | NREL

Science.gov Websites

informed solar decision making with credible, objective, accessible, and timely resources. Solar Energy Decision Support Decision Support NREL provides technical and analytical support to support provide unbiased information on solar policies and issues for state and local government decision makers

Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints

PubMed Central

Thompson, John R; Spata, Enti; Abrams, Keith R

2015-01-01

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing–remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions. PMID:26271918

Uncertainty in the Bayesian meta-analysis of normally distributed surrogate endpoints.

PubMed

Bujkiewicz, Sylwia; Thompson, John R; Spata, Enti; Abrams, Keith R

2017-10-01

We investigate the effect of the choice of parameterisation of meta-analytic models and related uncertainty on the validation of surrogate endpoints. Different meta-analytical approaches take into account different levels of uncertainty which may impact on the accuracy of the predictions of treatment effect on the target outcome from the treatment effect on a surrogate endpoint obtained from these models. A range of Bayesian as well as frequentist meta-analytical methods are implemented using illustrative examples in relapsing-remitting multiple sclerosis, where the treatment effect on disability worsening is the primary outcome of interest in healthcare evaluation, while the effect on relapse rate is considered as a potential surrogate to the effect on disability progression, and in gastric cancer, where the disease-free survival has been shown to be a good surrogate endpoint to the overall survival. Sensitivity analysis was carried out to assess the impact of distributional assumptions on the predictions. Also, sensitivity to modelling assumptions and performance of the models were investigated by simulation. Although different methods can predict mean true outcome almost equally well, inclusion of uncertainty around all relevant parameters of the model may lead to less certain and hence more conservative predictions. When investigating endpoints as candidate surrogate outcomes, a careful choice of the meta-analytical approach has to be made. Models underestimating the uncertainty of available evidence may lead to overoptimistic predictions which can then have an effect on decisions made based on such predictions.

A comparative assessment of tools for ecosystem services quantification and valuation

USGS Publications Warehouse

Bagstad, Kenneth J.; Semmens, Darius; Waage, Sissel; Winthrop, Robert

2013-01-01

To enter widespread use, ecosystem service assessments need to be quantifiable, replicable, credible, flexible, and affordable. With recent growth in the field of ecosystem services, a variety of decision-support tools has emerged to support more systematic ecosystem services assessment. Despite the growing complexity of the tool landscape, thorough reviews of tools for identifying, assessing, modeling and in some cases monetarily valuing ecosystem services have generally been lacking. In this study, we describe 17 ecosystem services tools and rate their performance against eight evaluative criteria that gauge their readiness for widespread application in public- and private-sector decision making. We describe each of the tools′ intended uses, services modeled, analytical approaches, data requirements, and outputs, as well time requirements to run seven tools in a first comparative concurrent application of multiple tools to a common location – the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. Based on this work, we offer conclusions about these tools′ current ‘readiness’ for widespread application within both public- and private-sector decision making processes. Finally, we describe potential pathways forward to reduce the resource requirements for running ecosystem services models, which are essential to facilitate their more widespread use in environmental decision making.

Whole mind and shared mind in clinical decision-making.

PubMed

Epstein, Ronald Mark

2013-02-01

To review the theory, research evidence and ethical implications regarding "whole mind" and "shared mind" in clinical practice in the context of chronic and serious illnesses. Selective critical review of the intersection of classical and naturalistic decision-making theories, cognitive neuroscience, communication research and ethics as they apply to decision-making and autonomy. Decision-making involves analytic thinking as well as affect and intuition ("whole mind") and sharing cognitive and affective schemas of two or more individuals ("shared mind"). Social relationships can help processing of complex information that otherwise would overwhelm individuals' cognitive capacities. Medical decision-making research, teaching and practice should consider both analytic and non-analytic cognitive processes. Further, research should consider that decisions emerge not only from the individual perspectives of patients, their families and clinicians, but also the perspectives that emerge from the interactions among them. Social interactions have the potential to enhance individual autonomy, as well as to promote relational autonomy based on shared frames of reference. Shared mind has the potential to result in wiser decisions, greater autonomy and self-determination; yet, clinicians and patients should be vigilant for the potential of hierarchical relationships to foster coercion or silencing of the patient's voice. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Cost-effectiveness of orthoptic screening in kindergarten: a decision-analytic model.

PubMed

König, H H; Barry, J C; Leidl, R; Zrenner, E

2000-06-01

The purpose of this study was to analyze the cost-effectiveness of orthoptic screening for amblyopia in kindergarten. A decision-analytic model was used. In this model all kindergarten children in Germany aged 3 years were examined by an orthoptist. Children with positive screening results were referred to an ophthalmologist for diagnosis. The number of newly diagnosed cases of amblyopia, amblyogenic non-obvious strabismus and amblyogenic refractive errors was used as the measure of effectiveness. Direct costs were measured form a third-party payer perspective. Data for model parameters were obtained from the literature and from own measurements in kindergartens. A base analysis was performed using median parameter values. The influence of uncertain parameters was tested in sensitivity analyses. According to the base analysis, the cost of one orthoptic screening test was 7.87 euro. One ophthalmologic examination cost 36.40 euro. The total cost of the screening program in all kindergartens was 3.1 million euro. A total of 4,261 new cases would be detected. The cost-effectiveness ratio was 727 euro per case detected. Sensitivity analysis showed considerable influence of the prevalence rate of target conditions and of the specificity of the orthopic examination on the cost-effectiveness ratio. This analysis provides information which is useful for discussion about the implementation of orthoptic screening and for planning a field study.

Supporting Fisheries Management by Means of Complex Models: Can We Point out Isles of Robustness in a Sea of Uncertainty?

PubMed Central

Gasche, Loïc; Mahévas, Stéphanie; Marchal, Paul

2013-01-01

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters. PMID:24204873

Supporting fisheries management by means of complex models: can we point out isles of robustness in a sea of uncertainty?

PubMed

Gasche, Loïc; Mahévas, Stéphanie; Marchal, Paul

2013-01-01

Ecosystems are usually complex, nonlinear and strongly influenced by poorly known environmental variables. Among these systems, marine ecosystems have high uncertainties: marine populations in general are known to exhibit large levels of natural variability and the intensity of fishing efforts can change rapidly. These uncertainties are a source of risks that threaten the sustainability of both fish populations and fishing fleets targeting them. Appropriate management measures have to be found in order to reduce these risks and decrease sensitivity to uncertainties. Methods have been developed within decision theory that aim at allowing decision making under severe uncertainty. One of these methods is the information-gap decision theory. The info-gap method has started to permeate ecological modelling, with recent applications to conservation. However, these practical applications have so far been restricted to simple models with analytical solutions. Here we implement a deterministic approach based on decision theory in a complex model of the Eastern English Channel. Using the ISIS-Fish modelling platform, we model populations of sole and plaice in this area. We test a wide range of values for ecosystem, fleet and management parameters. From these simulations, we identify management rules controlling fish harvesting that allow reaching management goals recommended by ICES (International Council for the Exploration of the Sea) working groups while providing the highest robustness to uncertainties on ecosystem parameters.

An optimization model for energy generation and distribution in a dynamic facility

NASA Technical Reports Server (NTRS)

Lansing, F. L.

1981-01-01

An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.

Shared decision-making – transferring research into practice: the Analytic Hierarchy Process (AHP)

PubMed Central

Dolan, James G.

2008-01-01

Objective To illustrate how the Analytic Hierarchy Process (AHP) can be used to promote shared decision-making and enhance clinician-patient communication. Methods Tutorial review. Results The AHP promotes shared decision making by creating a framework that is used to define the decision, summarize the information available, prioritize information needs, elicit preferences and values, and foster meaningful communication among decision stakeholders. Conclusions The AHP and related multi-criteria methods have the potential for improving the quality of clinical decisions and overcoming current barriers to implementing shared decision making in busy clinical settings. Further research is needed to determine the best way to implement these tools and to determine their effectiveness. Practice Implications Many clinical decisions involve preference-based trade-offs between competing risks and benefits. The AHP is a well-developed method that provides a practical approach for improving patient-provider communication, clinical decision-making, and the quality of patient care in these situations. PMID:18760559

The Impact of the Mode of Thought in Complex Decisions: Intuitive Decisions are Better

PubMed Central

Usher, Marius; Russo, Zohar; Weyers, Mark; Brauner, Ran; Zakay, Dan

2011-01-01

A number of recent studies have reported that decision quality is enhanced under conditions of inattention or distraction (unconscious thought; Dijksterhuis, 2004; Dijksterhuis and Nordgren, 2006; Dijksterhuis et al., 2006). These reports have generated considerable controversy, for both experimental (problems of replication) and theoretical reasons (interpretation). Here we report the results of four experiments. The first experiment replicates the unconscious thought effect, under conditions that validate and control the subjective criterion of decision quality. The second and third experiments examine the impact of a mode of thought manipulation (without distraction) on decision quality in immediate decisions. Here we find that intuitive or affective manipulations improve decision quality compared to analytic/deliberation manipulations. The fourth experiment combines the two methods (distraction and mode of thought manipulations) and demonstrates enhanced decision quality, in a situation that attempts to preserve ecological validity. The results are interpreted within a framework that is based on two interacting subsystems of decision-making: an affective/intuition based system and an analytic/deliberation system. PMID:21716605

Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schneider, K. P.; Mather, B. A.; Pal, B. C.

For nearly 20 years the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges thatmore » they were originally designed to examine. Additionally, the paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work.« less

Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders

DOE PAGES

Schneider, K. P.; Mather, B. A.; Pal, B. C.; ...

2017-10-10

For nearly 20 years the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges thatmore » they were originally designed to examine. Additionally, the paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work.« less

Dual processing model of medical decision-making

PubMed Central

2012-01-01

Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories). PMID:22943520

A cognitive prosthesis for complex decision-making.

PubMed

Tremblay, Sébastien; Gagnon, Jean-François; Lafond, Daniel; Hodgetts, Helen M; Doiron, Maxime; Jeuniaux, Patrick P J M H

2017-01-01

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies - using simplified as opposed to analytic decision rules - have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called "IMAGE" to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Patient decision making among older individuals with cancer.

PubMed

Strohschein, Fay J; Bergman, Howard; Carnevale, Franco A; Loiselle, Carmen G

2011-07-01

Patient decision making is an area of increasing inquiry. For older individuals experiencing cancer, variations in health and functional status, physiologic aspects of aging, and tension between quality and quantity of life present unique challenges to treatment-related decision making. We used the pragmatic utility method to analyze the concept of patient decision making in the context of older individuals with cancer. We first evaluated its maturity in existing literature and then posed analytical questions to clarify aspects found to be only partially mature. In this context, we found patient decision making to be an ongoing process, changing with time, reflecting individual and relational components, as well as analytical and emotional ones. Assumptions frequently associated with patient decision making were not consistent with the empirical literature. Careful attention to the multifaceted components of patient decision making among older individuals with cancer provides guidance for research, supportive interventions, and targeted follow-up care.

Green analytical chemistry introduction to chloropropanols determination at no economic and analytical performance costs?

PubMed

Jędrkiewicz, Renata; Orłowski, Aleksander; Namieśnik, Jacek; Tobiszewski, Marek

2016-01-15

In this study we perform ranking of analytical procedures for 3-monochloropropane-1,2-diol determination in soy sauces by PROMETHEE method. Multicriteria decision analysis was performed for three different scenarios - metrological, economic and environmental, by application of different weights to decision making criteria. All three scenarios indicate capillary electrophoresis-based procedure as the most preferable. Apart from that the details of ranking results differ for these three scenarios. The second run of rankings was done for scenarios that include metrological, economic and environmental criteria only, neglecting others. These results show that green analytical chemistry-based selection correlates with economic, while there is no correlation with metrological ones. This is an implication that green analytical chemistry can be brought into laboratories without analytical performance costs and it is even supported by economic reasons. Copyright © 2015 Elsevier B.V. All rights reserved.

DE-CERTS: A Decision Support System for a Comparative Evaluation Method for Risk Management Methodologies and Tools

DTIC Science & Technology

1991-09-01

iv III. THE ANALYTIC HIERARCHY PROCESS ..... ........ 15 A. INTRODUCTION ...... ................. 15 B. THE AHP PROCESS ...... ................ 16 C...INTRODUCTION ...... ................. 26 B. IMPLEMENTATION OF CERTS USING AHP ........ .. 27 1. Consistency ...... ................ 29 2. User Interface...the proposed technique into a Decision Support System. Expert Choice implements the Analytic Hierarchy Process ( AHP ), an approach to multi- criteria

Development of An Analytic Approach to Determine How Environmental Protection Agency’s Integrated Risk Information System (IRIS) Is Used by Non-EPA Decision Makers (Final Contractor Report)

EPA Science Inventory

EPA announced the availability of the final contractor report entitled, Development of an Analytic Approach to Determine How Environmental Protection Agency’s Integrated Risk Information System (IRIS) Is Used By Non EPA Decision Makers. This contractor report analyzed how ...

Growth and yield model application in tropical rain forest management

Treesearch

James Atta-Boateng; John W., Jr. Moser

2000-01-01

Analytical tools are needed to evaluate the impact of management policies on the sustainable use of rain forest. Optimal decisions concerning the level of management inputs require accurate predictions of output at all relevant input levels. Using growth data from 40 l-hectare permanent plots obtained from the semi-deciduous forest of Ghana, a system of 77 differential...

Multiattribute Decision Modeling Techniques: A Comparative Analysis

DTIC Science & Technology

1988-08-01

Analytic Hierarchy Process ( AHP ). It is structurally similar to SMART, but elicitation methods are different and there are several algorithms for...reconciliation of inconsistent judgments and for consistency checks that are not available in any of the utility procedures. The AHP has been applied...of commercially available software packages that implement the AHP algorithms. Elicitation Methods. The AHP builds heavily on value trees, which

Final Report: PAGE: Policy Analytics Generation Engine

DTIC Science & Technology

2016-08-12

develop a parallel framework for it. We also developed policies and methods by which a group of defensive resources (e.g. checkpoints) could be...Sarit Kraus. Learning to Reveal Information in Repeated Human -Computer Negotiation, Human -Agent Interaction Design and Models Workshop 2012. 04-JUN...Joseph Keshet, Sarit Kraus. Predicting Human Strategic Decisions Using Facial Expressions, International Joint Conference on Artificial

A Collaborative Approach to Designing Graduate Admission Studies: A Model for Influencing Program Planning and Policy. AIR 1999 Annual Forum Paper.

ERIC Educational Resources Information Center

Delaney, Anne Marie

This paper presents the rationale, research design, analytical approaches, and results of a graduate admission study which examined the motivation and enrollment decision processes of students accepted to a newly redesigned Master of Business Administration (MBA) Program. The study was developed collaboratively by the institution's Office of…

Research Initiatives and Preliminary Results In Automation Design In Airspace Management in Free Flight

NASA Technical Reports Server (NTRS)

Corker, Kevin; Lebacqz, J. Victor (Technical Monitor)

1997-01-01

The NASA and the FAA have entered into a joint venture to explore, define, design and implement a new airspace management operating concept. The fundamental premise of that concept is that technologies and procedures need to be developed for flight deck and ground operations to improve the efficiency, the predictability, the flexibility and the safety of airspace management and operations. To that end NASA Ames has undertaken an initial development and exploration of "key concepts" in the free flight airspace management technology development. Human Factors issues in automation aiding design, coupled aiding systems between air and ground, communication protocols in distributed decision making, and analytic techniques for definition of concepts of airspace density and operator cognitive load have been undertaken. This paper reports the progress of these efforts, which are not intended to definitively solve the many evolving issues of design for future ATM systems, but to provide preliminary results to chart the parameters of performance and the topology of the analytic effort required. The preliminary research in provision of cockpit display of traffic information, dynamic density definition, distributed decision making, situation awareness models and human performance models is discussed as they focus on the theme of "design requirements".

A Decision Analytic Approach to Exposure-Based Chemical Prioritization

PubMed Central

Mitchell, Jade; Pabon, Nicolas; Collier, Zachary A.; Egeghy, Peter P.; Cohen-Hubal, Elaine; Linkov, Igor; Vallero, Daniel A.

2013-01-01

The manufacture of novel synthetic chemicals has increased in volume and variety, but often the environmental and health risks are not fully understood in terms of toxicity and, in particular, exposure. While efforts to assess risks have generally been effective when sufficient data are available, the hazard and exposure data necessary to assess risks adequately are unavailable for the vast majority of chemicals in commerce. The US Environmental Protection Agency has initiated the ExpoCast Program to develop tools for rapid chemical evaluation based on potential for exposure. In this context, a model is presented in which chemicals are evaluated based on inherent chemical properties and behaviorally-based usage characteristics over the chemical’s life cycle. These criteria are assessed and integrated within a decision analytic framework, facilitating rapid assessment and prioritization for future targeted testing and systems modeling. A case study outlines the prioritization process using 51 chemicals. The results show a preliminary relative ranking of chemicals based on exposure potential. The strength of this approach is the ability to integrate relevant statistical and mechanistic data with expert judgment, allowing for an initial tier assessment that can further inform targeted testing and risk management strategies. PMID:23940664

Does it make sense to modify tropical cyclones? A decision-analytic assessment.

PubMed

Klima, Kelly; Morgan, M Granger; Grossmann, Iris; Emanuel, Kerry

2011-05-15

Recent dramatic increases in damages caused by tropical cyclones (TCs) and improved understanding of TC physics have led DHS to fund research on intentional hurricane modification. We present a decision analytic assessment of whether it is potentially cost-effective to attempt to lower the wind speed of TCs approaching South Florida by reducing sea surface temperatures with wind-wave pumps. Using historical data on hurricanes approaching South Florida, we develop prior probabilities of how storms might evolve. The effects of modification are estimated using a modern TC model. The FEMA HAZUS-MH MR3 damage model and census data on the value of property at risk are used to estimate expected economic losses. We compare wind damages after storm modification with damages after implementing hardening strategies protecting buildings. We find that if it were feasible and properly implemented, modification could reduce net losses from an intense storm more than hardening structures. However, hardening provides "fail safe" protection for average storms that might not be achieved if the only option were modification. The effect of natural variability is larger than that of either strategy. Damage from storm surge is modest in the scenario studied but might be abated by modification.

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0.

PubMed

Wang, Shirley V; Schneeweiss, Sebastian; Berger, Marc L; Brown, Jeffrey; de Vries, Frank; Douglas, Ian; Gagne, Joshua J; Gini, Rosa; Klungel, Olaf; Mullins, C Daniel; Nguyen, Michael D; Rassen, Jeremy A; Smeeth, Liam; Sturkenboom, Miriam

2017-09-01

Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases. We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list. Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

The MSCA Program: Developing Analytic Unicorns

ERIC Educational Resources Information Center

Houghton, David M.; Schertzer, Clint; Beck, Scott

2018-01-01

Marketing analytics students who can communicate effectively with decision makers are in high demand. These "analytic unicorns" are hard to find. The Master of Science in Customer Analytics (MSCA) degree program at Xavier University seeks to fill that need. In this paper, we discuss the process of creating the MSCA program. We outline…

Decision aids for multiple-decision disease management as affected by weather input errors.

PubMed

Pfender, W F; Gent, D H; Mahaffee, W F; Coop, L B; Fox, A D

2011-06-01

Many disease management decision support systems (DSSs) rely, exclusively or in part, on weather inputs to calculate an indicator for disease hazard. Error in the weather inputs, typically due to forecasting, interpolation, or estimation from off-site sources, may affect model calculations and management decision recommendations. The extent to which errors in weather inputs affect the quality of the final management outcome depends on a number of aspects of the disease management context, including whether management consists of a single dichotomous decision, or of a multi-decision process extending over the cropping season(s). Decision aids for multi-decision disease management typically are based on simple or complex algorithms of weather data which may be accumulated over several days or weeks. It is difficult to quantify accuracy of multi-decision DSSs due to temporally overlapping disease events, existence of more than one solution to optimizing the outcome, opportunities to take later recourse to modify earlier decisions, and the ongoing, complex decision process in which the DSS is only one component. One approach to assessing importance of weather input errors is to conduct an error analysis in which the DSS outcome from high-quality weather data is compared with that from weather data with various levels of bias and/or variance from the original data. We illustrate this analytical approach for two types of DSS, an infection risk index for hop powdery mildew and a simulation model for grass stem rust. Further exploration of analysis methods is needed to address problems associated with assessing uncertainty in multi-decision DSSs.

Just-in-time Time Data Analytics and Visualization of Climate Simulations using the Bellerophon Framework

NASA Astrophysics Data System (ADS)

Anantharaj, V. G.; Venzke, J.; Lingerfelt, E.; Messer, B.

2015-12-01

Climate model simulations are used to understand the evolution and variability of earth's climate. Unfortunately, high-resolution multi-decadal climate simulations can take days to weeks to complete. Typically, the simulation results are not analyzed until the model runs have ended. During the course of the simulation, the output may be processed periodically to ensure that the model is preforming as expected. However, most of the data analytics and visualization are not performed until the simulation is finished. The lengthy time period needed for the completion of the simulation constrains the productivity of climate scientists. Our implementation of near real-time data visualization analytics capabilities allows scientists to monitor the progress of their simulations while the model is running. Our analytics software executes concurrently in a co-scheduling mode, monitoring data production. When new data are generated by the simulation, a co-scheduled data analytics job is submitted to render visualization artifacts of the latest results. These visualization output are automatically transferred to Bellerophon's data server located at ORNL's Compute and Data Environment for Science (CADES) where they are processed and archived into Bellerophon's database. During the course of the experiment, climate scientists can then use Bellerophon's graphical user interface to view animated plots and their associated metadata. The quick turnaround from the start of the simulation until the data are analyzed permits research decisions and projections to be made days or sometimes even weeks sooner than otherwise possible! The supercomputer resources used to run the simulation are unaffected by co-scheduling the data visualization jobs, so the model runs continuously while the data are visualized. Our just-in-time data visualization software looks to increase climate scientists' productivity as climate modeling moves into exascale era of computing.

Incorporating uncertainty into mercury-offset decisions with a probabilistic network for National Pollutant Discharge Elimination System permit holders: an interim report

USGS Publications Warehouse

Wood, Alexander

2004-01-01

This interim report describes an alternative approach for evaluating the efficacy of using mercury (Hg) offsets to improve water quality. Hg-offset programs may allow dischargers facing higher-pollution control costs to meet their regulatory obligations by making more cost effective pollutant-reduction decisions. Efficient Hg management requires methods to translate that science and economics into a regulatory decision framework. This report documents the work in progress by the U.S. Geological Surveys Western Geographic Science Center in collaboration with Stanford University toward developing this decision framework to help managers, regulators, and other stakeholders decide whether offsets can cost effectively meet the Hg total maximum daily load (TMDL) requirements in the Sacramento River watershed. Two key approaches being considered are: (1) a probabilistic approach that explicitly incorporates scientific uncertainty, cost information, and value judgments; and (2) a quantitative approach that captures uncertainty in testing the feasibility of Hg offsets. Current fate and transport-process models commonly attempt to predict chemical transformations and transport pathways deterministically. However, the physical, chemical, and biologic processes controlling the fate and transport of Hg in aquatic environments are complex and poorly understood. Deterministic models of Hg environmental behavior contain large uncertainties, reflecting this lack of understanding. The uncertainty in these underlying physical processes may produce similarly large uncertainties in the decisionmaking process. However, decisions about control strategies are still being made despite the large uncertainties in current Hg loadings, the relations between total Hg (HgT) loading and methylmercury (MeHg) formation, and the relations between control efforts and Hg content in fish. The research presented here focuses on an alternative analytical approach to the current use of safety factors and deterministic methods for Hg TMDL decision support, one that is fully compatible with an adaptive management approach. This alternative approach uses empirical data and informed judgment to provide a scientific and technical basis for helping National Pollutant Discharge Elimination System (NPDES) permit holders make management decisions. An Hg-offset system would be an option if a wastewater-treatment plant could not achieve NPDES permit requirements for HgT reduction. We develop a probabilistic decision-analytical model consisting of three submodels for HgT loading, MeHg, and cost mitigation within a Bayesian network that integrates information of varying rigor and detail into a simple model of a complex system. Hg processes are identified and quantified by using a combination of historical data, statistical models, and expert judgment. Such an integrated approach to uncertainty analysis allows easy updating of prediction and inference when observations of model variables are made. We demonstrate our approach with data from the Cache Creek watershed (a subbasin of the Sacramento River watershed). The empirical models used to generate the needed probability distributions are based on the same empirical models currently being used by the Central Valley Regional Water Quality Control Cache Creek Hg TMDL working group. The significant difference is that input uncertainty and error are explicitly included in the model and propagated throughout its algorithms. This work demonstrates how to integrate uncertainty into the complex and highly uncertain Hg TMDL decisionmaking process. The various sources of uncertainty are propagated as decision risk that allows decisionmakers to simultaneously consider uncertainties in remediation/implementation costs while attempting to meet environmental/ecologic targets. We must note that this research is on going. As more data are collected, the HgT and cost-mitigation submodels are updated and the uncer

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

NASA Astrophysics Data System (ADS)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Big Data and Predictive Analytics: Applications in the Care of Children.

PubMed

Suresh, Srinivasan

2016-04-01

Emerging changes in the United States' healthcare delivery model have led to renewed interest in data-driven methods for managing quality of care. Analytics (Data plus Information) plays a key role in predictive risk assessment, clinical decision support, and various patient throughput measures. This article reviews the application of a pediatric risk score, which is integrated into our hospital's electronic medical record, and provides an early warning sign for clinical deterioration. Dashboards that are a part of disease management systems, are a vital tool in peer benchmarking, and can help in reducing unnecessary variations in care. Copyright © 2016 Elsevier Inc. All rights reserved.

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation

PubMed Central

2017-01-01

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes “winner-take-all” processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans’ value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light. PMID:29077746

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

PubMed

Colas, Jaron T

2017-01-01

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.

Expert Elicitation of Multinomial Probabilities for Decision-Analytic Modeling: An Application to Rates of Disease Progression in Undiagnosed and Untreated Melanoma.

PubMed

Wilson, Edward C F; Usher-Smith, Juliet A; Emery, Jon; Corrie, Pippa G; Walter, Fiona M

2018-06-01

Expert elicitation is required to inform decision making when relevant "better quality" data either do not exist or cannot be collected. An example of this is to inform decisions as to whether to screen for melanoma. A key input is the counterfactual, in this case the natural history of melanoma in patients who are undiagnosed and hence untreated. To elicit expert opinion on the probability of disease progression in patients with melanoma that is undetected and hence untreated. A bespoke webinar-based expert elicitation protocol was administered to 14 participants in the United Kingdom, Australia, and New Zealand, comprising 12 multinomial questions on the probability of progression from one disease stage to another in the absence of treatment. A modified Connor-Mosimann distribution was fitted to individual responses to each question. Individual responses were pooled using a Monte-Carlo simulation approach. Participants were asked to provide feedback on the process. A pooled modified Connor-Mosimann distribution was successfully derived from participants' responses. Feedback from participants was generally positive, with 86% willing to take part in such an exercise again. Nevertheless, only 57% of participants felt that this was a valid approach to determine the risk of disease progression. Qualitative feedback reflected some understanding of the need to rely on expert elicitation in the absence of "hard" data. We successfully elicited and pooled the beliefs of experts in melanoma regarding the probability of disease progression in a format suitable for inclusion in a decision-analytic model. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Vasa previa screening strategies: a decision and cost-effectiveness analysis.

PubMed

Sinkey, R G; Odibo, A O

2018-05-22

The aim of this study is to perform a decision and cost-effectiveness analysis comparing four screening strategies for the antenatal diagnosis of vasa previa among singleton pregnancies. A decision-analytic model was constructed comparing vasa previa screening strategies. Published probabilities and costs were applied to four transvaginal screening scenarios which occurred at the time of mid-trimester ultrasound: no screening, ultrasound-indicated screening, screening pregnancies conceived by in vitro fertilization (IVF), and universal screening. Ultrasound-indicated screening was defined as performing a transvaginal ultrasound at the time of routine anatomy ultrasound in response to one of the following sonographic findings associated with an increased risk of vasa previa: low-lying placenta, marginal or velamentous cord insertion, or bilobed or succenturiate lobed placenta. The primary outcome was cost per quality adjusted life years (QALY) in U.S. dollars. The analysis was from a healthcare system perspective with a willingness to pay (WTP) threshold of $100,000 per QALY selected. One-way and multivariate sensitivity analyses (Monte-Carlo simulation) were performed. This decision-analytic model demonstrated that screening pregnancies conceived by IVF was the most cost-effective strategy with an incremental cost effectiveness ratio (ICER) of $29,186.50 / QALY. Ultrasound-indicated screening was the second most cost-effective with an ICER of $56,096.77 / QALY. These data were robust to all one-way and multivariate sensitivity analyses performed. Within our baseline assumptions, transvaginal ultrasound screening for vasa previa appears to be most cost-effective when performed among IVF pregnancies. However, both IVF and ultrasound-indicated screening strategies fall within contemporary willingness-to-pay thresholds, suggesting that both strategies may be appropriate to apply in clinical practice. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

The Integration of Production-Distribution on Newspapers Supply Chain for Cost Minimization using Analytic Models: Case Study

NASA Astrophysics Data System (ADS)

Febriana Aqidawati, Era; Sutopo, Wahyudi; Hisjam, Muh.

2018-03-01

Newspapers are products with special characteristics which are perishable, have a shorter range of time between the production and distribution, zero inventory, and decreasing sales value along with increasing in time. Generally, the problem of production and distribution in the paper supply chain is the integration of production planning and distribution to minimize the total cost. The approach used in this article to solve the problem is using an analytical model. In this article, several parameters and constraints have been considered in the calculation of the total cost of the integration of production and distribution of newspapers during the determined time horizon. This model can be used by production and marketing managers as decision support in determining the optimal quantity of production and distribution in order to obtain minimum cost so that company's competitiveness level can be increased.

The doctor-patient relationship as a toolkit for uncertain clinical decisions.

PubMed

Diamond-Brown, Lauren

2016-06-01

Medical uncertainty is a well-recognized problem in healthcare, yet how doctors make decisions in the face of uncertainty remains to be understood. This article draws on interdisciplinary literature on uncertainty and physician decision-making to examine a specific physician response to uncertainty: using the doctor-patient relationship as a toolkit. Additionally, I ask what happens to this process when the doctor-patient relationship becomes fragmented. I answer these questions by examining obstetrician-gynecologists' narratives regarding how they make decisions when faced with uncertainty in childbirth. Between 2013 and 2014, I performed 21 semi-structured interviews with obstetricians in the United States. Obstetricians were selected to maximize variation in relevant physician, hospital, and practice characteristics. I began with grounded theory and moved to analytical coding of themes in relation to relevant literature. My analysis renders it evident that some physicians use the doctor-patient relationship as a toolkit for dealing with uncertainty. I analyze how this process varies for physicians in different models of care by comparing doctors' experiences in models with continuous versus fragmented doctor-patient relationships. My key findings are that obstetricians in both models appealed to the ideal of patient-centered decision-making to cope with uncertain decisions, but in practice physicians in fragmented care faced a number of challenges to using the doctor-patient relationship as a toolkit for decision-making. These challenges led to additional uncertainties and in some cases to poor outcomes for doctors and/or patients; they also raised concerns about the reproduction of inequality. Thus organization of care delivery mitigates the efficacy of doctors' use of the doctor-patient relationship toolkit for uncertain decisions. These findings have implications for theorizing about decision-making under conditions of medical uncertainty, for understanding how the doctor-patient relationship and model of care affect physician decision-making, and for forming policy on the optimal structure of medical work. Copyright © 2016 Elsevier Ltd. All rights reserved.

Cost-Effectiveness of Procedures for Treatment of Ostium Secundum Atrial Septal Defects Occlusion Comparing Conventional Surgery and Septal Percutaneous Implant

PubMed Central

da Costa, Márcia Gisele Santos; Santos, Marisa da Silva; Sarti, Flávia Mori; Senna, Kátia Marie Simões e.; Tura, Bernardo Rangel; Goulart, Marcelo Correia

2014-01-01

Objectives The study performs a cost-effectiveness analysis of procedures for atrial septal defects occlusion, comparing conventional surgery to septal percutaneous implant. Methods A model of analytical decision was structured with symmetric branches to estimate cost-effectiveness ratio between the procedures. The decision tree model was based on evidences gathered through meta-analysis of literature, and validated by a panel of specialists. The lower number of surgical procedures performed for atrial septal defects occlusion at each branch was considered as the effectiveness outcome. Direct medical costs and probabilities for each event were inserted in the model using data available from Brazilian public sector database system and information extracted from the literature review, using micro-costing technique. Sensitivity analysis included price variations of percutaneous implant. Results The results obtained from the decision model demonstrated that the percutaneous implant was more cost effective in cost-effectiveness analysis at a cost of US$8,936.34 with a reduction in the probability of surgery occurrence in 93% of the cases. Probability of atrial septal communication occlusion and cost of the implant are the determinant factors of cost-effectiveness ratio. Conclusions The proposal of a decision model seeks to fill a void in the academic literature. The decision model proposed includes the outcomes that present major impact in relation to the overall costs of the procedure. The atrial septal defects occlusion using percutaneous implant reduces the physical and psychological distress to the patients in relation to the conventional surgery, which represent intangible costs in the context of economic evaluation. PMID:25302806

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

Cost-effectiveness of procedures for treatment of ostium secundum atrial septal defects occlusion comparing conventional surgery and septal percutaneous implant.

PubMed

da Costa, Márcia Gisele Santos; Santos, Marisa da Silva; Sarti, Flávia Mori; Simões e Senna, Kátia Marie; Tura, Bernardo Rangel; Correia, Marcelo Goulart; Goulart, Marcelo Correia

2014-01-01

The study performs a cost-effectiveness analysis of procedures for atrial septal defects occlusion, comparing conventional surgery to septal percutaneous implant. A model of analytical decision was structured with symmetric branches to estimate cost-effectiveness ratio between the procedures. The decision tree model was based on evidences gathered through meta-analysis of literature, and validated by a panel of specialists. The lower number of surgical procedures performed for atrial septal defects occlusion at each branch was considered as the effectiveness outcome. Direct medical costs and probabilities for each event were inserted in the model using data available from Brazilian public sector database system and information extracted from the literature review, using micro-costing technique. Sensitivity analysis included price variations of percutaneous implant. The results obtained from the decision model demonstrated that the percutaneous implant was more cost effective in cost-effectiveness analysis at a cost of US$8,936.34 with a reduction in the probability of surgery occurrence in 93% of the cases. Probability of atrial septal communication occlusion and cost of the implant are the determinant factors of cost-effectiveness ratio. The proposal of a decision model seeks to fill a void in the academic literature. The decision model proposed includes the outcomes that present major impact in relation to the overall costs of the procedure. The atrial septal defects occlusion using percutaneous implant reduces the physical and psychological distress to the patients in relation to the conventional surgery, which represent intangible costs in the context of economic evaluation.

The German cervical cancer screening model: development and validation of a decision-analytic model for cervical cancer screening in Germany.

PubMed

Siebert, Uwe; Sroczynski, Gaby; Hillemanns, Peter; Engel, Jutta; Stabenow, Roland; Stegmaier, Christa; Voigt, Kerstin; Gibis, Bernhard; Hölzel, Dieter; Goldie, Sue J

2006-04-01

We sought to develop and validate a decision-analytic model for the natural history of cervical cancer for the German health care context and to apply it to cervical cancer screening. We developed a Markov model for the natural history of cervical cancer and cervical cancer screening in the German health care context. The model reflects current German practice standards for screening, diagnostic follow-up and treatment regarding cervical cancer and its precursors. Data for disease progression and cervical cancer survival were obtained from the literature and German cancer registries. Accuracy of Papanicolaou (Pap) testing was based on meta-analyses. We performed internal and external model validation using observed epidemiological data for unscreened women from different German cancer registries. The model predicts life expectancy, incidence of detected cervical cancer cases, lifetime cervical cancer risks and mortality. The model predicted a lifetime cervical cancer risk of 3.0% and a lifetime cervical cancer mortality of 1.0%, with a peak cancer incidence of 84/100,000 at age 51 years. These results were similar to observed data from German cancer registries, German literature data and results from other international models. Based on our model, annual Pap screening could prevent 98.7% of diagnosed cancer cases and 99.6% of deaths due to cervical cancer in women completely adherent to screening and compliant to treatment. Extending the screening interval from 1 year to 2, 3 or 5 years resulted in reduced screening effectiveness. This model provides a tool for evaluating the long-term effectiveness of different cervical cancer screening tests and strategies.

"Utilizing" signal detection theory.

PubMed

Lynn, Spencer K; Barrett, Lisa Feldman

2014-09-01

What do inferring what a person is thinking or feeling, judging a defendant's guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, for which different responses are appropriate) and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial, we show how incorporating the economic concept of utility allows signal detection theory to serve as a model of optimal decision making, going beyond its common use as an analytic method. This utility approach to signal detection theory clarifies otherwise enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (an inverse relationship between bias magnitude and sensitivity optimizes utility). A "utilized" signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. © The Author(s) 2014.

“UTILIZING” SIGNAL DETECTION THEORY

PubMed Central

Lynn, Spencer K.; Barrett, Lisa Feldman

2014-01-01

What do inferring what a person is thinking or feeling, deciding to report a symptom to your doctor, judging a defendant’s guilt, and navigating a dimly lit room have in common? They involve perceptual uncertainty (e.g., a scowling face might indicate anger or concentration, which engender different appropriate responses), and behavioral risk (e.g., a cost to making the wrong response). Signal detection theory describes these types of decisions. In this tutorial we show how, by incorporating the economic concept of utility, signal detection theory serves as a model of optimal decision making, beyond its common use as an analytic method. This utility approach to signal detection theory highlights potentially enigmatic influences of perceptual uncertainty on measures of decision-making performance (accuracy and optimality) and on behavior (a functional relationship between bias and sensitivity). A “utilized” signal detection theory offers the possibility of expanding the phenomena that can be understood within a decision-making framework. PMID:25097061

Decisions on new product development under uncertainties

NASA Astrophysics Data System (ADS)

Huang, Yeu-Shiang; Liu, Li-Chen; Ho, Jyh-Wen

2015-04-01

In an intensively competitive market, developing a new product has become a valuable strategy for companies to establish their market positions and enhance their competitive advantages. Therefore, it is essential to effectively manage the process of new product development (NPD). However, since various problems may arise in NPD projects, managers should set up some milestones and subsequently construct evaluative mechanisms to assess their feasibility. This paper employed the approach of Bayesian decision analysis to deal with the two crucial uncertainties for NPD, which are the future market share and the responses of competitors. The proposed decision process can provide a systematic analytical procedure to determine whether an NPD project should be continued or not under the consideration of whether effective usage is being made of the organisational resources. Accordingly, the proposed decision model can assist the managers in effectively addressing the NPD issue under the competitive market.

Decision making in noisy bistable systems with time-dependent asymmetry

NASA Astrophysics Data System (ADS)

Nené, Nuno R.; Zaikin, Alexey

2013-01-01

Our work draws special attention to the importance of the effects of time-dependent parameters on decision making in bistable systems. Here, we extend previous studies of the mechanism known as speed-dependent cellular decision making in genetic circuits by performing an analytical treatment of the canonical supercritical pitchfork bifurcation problem with an additional time-dependent asymmetry and control parameter. This model has an analogous behavior to the genetic switch. In the presence of transient asymmetries and fluctuations, slow passage through the critical region in both systems increases substantially the probability of specific decision outcomes. We also study the relevance for attractor selection of reaching maximum values for the external asymmetry before and after the critical region. Overall, maximum asymmetries should be reached at an instant where the position of the critical point allows for compensation of the detrimental effects of noise in retaining memory of the transient asymmetries.

Smoking as a decision among pregnant and non-pregnant women.

PubMed

Ortendahl, Monica

2006-10-01

The purpose was to examine values and beliefs related to smoking, and to test the validity of a decision model based on the product of the value of smoking-related events and states, and the belief that these will occur, (in decision research labeled Expected Utility, or EU). Over a two-week period eighty women, divided into subgroups consisting of pregnant vs. non-pregnant women, and those intending vs. those not intending to quit smoking, performed evaluations of values and beliefs for the two conditions of quitting and not quitting smoking. For both pregnant and non-pregnant women expected utility of smoking was negative. Of all the four groups pregnant women not intending to quit smoking estimated the expected utility of smoking as least negative. A decision analytic approach is applicable to describe the addictive behavior of smoking. Values as well as beliefs about smoking should be stressed in smoking cessation programs, especially among pregnant women.

Portfolio Decision Analysis Framework for Value-Focused Ecosystem Management

PubMed Central

Convertino, Matteo; Valverde, L. James

2013-01-01

Management of natural resources in coastal ecosystems is a complex process that is made more challenging by the need for stakeholders to confront the prospect of sea level rise and a host of other environmental stressors. This situation is especially true for coastal military installations, where resource managers need to balance conflicting objectives of environmental conservation against military mission. The development of restoration plans will necessitate incorporating stakeholder preferences, and will, moreover, require compliance with applicable federal/state laws and regulations. To promote the efficient allocation of scarce resources in space and time, we develop a portfolio decision analytic (PDA) framework that integrates models yielding policy-dependent predictions for changes in land cover and species metapopulations in response to restoration plans, under different climate change scenarios. In a manner that is somewhat analogous to financial portfolios, infrastructure and natural resources are classified as human and natural assets requiring management. The predictions serve as inputs to a Multi Criteria Decision Analysis model (MCDA) that is used to measure the benefits of restoration plans, as well as to construct Pareto frontiers that represent optimal portfolio allocations of restoration actions and resources. Optimal plans allow managers to maintain or increase asset values by contrasting the overall degradation of the habitat and possible increased risk of species decline against the benefits of mission success. The optimal combination of restoration actions that emerge from the PDA framework allows decision-makers to achieve higher environmental benefits, with equal or lower costs, than those achievable by adopting the myopic prescriptions of the MCDA model. The analytic framework presented here is generalizable for the selection of optimal management plans in any ecosystem where human use of the environment conflicts with the needs of threatened and endangered species. The PDA approach demonstrates the advantages of integrated, top-down management, versus bottom-up management approaches. PMID:23823331

Portfolio Decision Analysis Framework for Value-Focused Ecosystem Management.

PubMed

Convertino, Matteo; Valverde, L James

2013-01-01

Management of natural resources in coastal ecosystems is a complex process that is made more challenging by the need for stakeholders to confront the prospect of sea level rise and a host of other environmental stressors. This situation is especially true for coastal military installations, where resource managers need to balance conflicting objectives of environmental conservation against military mission. The development of restoration plans will necessitate incorporating stakeholder preferences, and will, moreover, require compliance with applicable federal/state laws and regulations. To promote the efficient allocation of scarce resources in space and time, we develop a portfolio decision analytic (PDA) framework that integrates models yielding policy-dependent predictions for changes in land cover and species metapopulations in response to restoration plans, under different climate change scenarios. In a manner that is somewhat analogous to financial portfolios, infrastructure and natural resources are classified as human and natural assets requiring management. The predictions serve as inputs to a Multi Criteria Decision Analysis model (MCDA) that is used to measure the benefits of restoration plans, as well as to construct Pareto frontiers that represent optimal portfolio allocations of restoration actions and resources. Optimal plans allow managers to maintain or increase asset values by contrasting the overall degradation of the habitat and possible increased risk of species decline against the benefits of mission success. The optimal combination of restoration actions that emerge from the PDA framework allows decision-makers to achieve higher environmental benefits, with equal or lower costs, than those achievable by adopting the myopic prescriptions of the MCDA model. The analytic framework presented here is generalizable for the selection of optimal management plans in any ecosystem where human use of the environment conflicts with the needs of threatened and endangered species. The PDA approach demonstrates the advantages of integrated, top-down management, versus bottom-up management approaches.

Prioritization of water management for sustainability using hydrologic simulation model and multicriteria decision making techniques.

PubMed

Chung, Eun-Sung; Lee, Kil Seong

2009-03-01

The objective of this study is to develop an alternative evaluation index (AEI) in order to determine the priorities of a range of alternatives using both the hydrological simulation program in FORTRAN (HSPF) and multicriteria decision making (MCDM) techniques. In order to formulate the HSPF model, sensitivity analyses of water quantity (peak discharge and total volume) and quality (BOD peak concentrations and total loads) are conducted and a number of critical parameters were selected. To achieve a more precise simulation, the study watershed is divided into four regions for calibration and verification according to landuse, location, slope, and climate data. All evaluation criteria were selected using the Driver-Pressure-State-Impact-Response (DPSIR) model, a sustainability evaluation concept. The Analytic Hierarchy Process is used to estimate the weights of the criteria and the effects of water quantity and quality were quantified by HSPF simulation. In addition, AEIs that reflected residents' preferences for management objectives are proposed in order to induce the stakeholder to participate in the decision making process.

A development of logistics management models for the Space Transportation System

NASA Technical Reports Server (NTRS)

Carrillo, M. J.; Jacobsen, S. E.; Abell, J. B.; Lippiatt, T. F.

1983-01-01

A new analytic queueing approach was described which relates stockage levels, repair level decisions, and the project network schedule of prelaunch operations directly to the probability distribution of the space transportation system launch delay. Finite source population and limited repair capability were additional factors included in this logistics management model developed specifically for STS maintenance requirements. Data presently available to support logistics decisions were based on a comparability study of heavy aircraft components. A two-phase program is recommended by which NASA would implement an integrated data collection system, assemble logistics data from previous STS flights, revise extant logistics planning and resource requirement parameters using Bayes-Lin techniques, and adjust for uncertainty surrounding logistics systems performance parameters. The implementation of these recommendations can be expected to deliver more cost-effective logistics support.

Escalation research: Providing new frontiers for applying behavior analysis to organizational behavior

PubMed Central

Goltz, Sonia M.

2000-01-01

Decision fiascoes such as escalation of commitment, the tendency of decision makers to “throw good money after bad,” can have serious consequences for organizations and are therefore of great interest in applied research. This paper discusses the use of behavior analysis in organizational behavior research on escalation. Among the most significant aspects of behavior-analytic research on escalation is that it has indicated that both the patterns of outcomes that decision makers have experienced for past decisions and the patterns of responses that they make are critical for understanding escalation. This research has also stimulated the refinement of methods by researchers to better assess decision making and the role reinforcement plays in it. Finally, behavior-analytic escalation research has not only indicated the utility of reinforcement principles for predicting more complex human behavior but has also suggested some additional areas for future exploration of decision making using behavior analysis. PMID:22478347

From the Patient Perspective: the Economic Value of Seasonal and H1N1 Influenza Vaccination

PubMed Central

Lee, Bruce Y.; Bacon, Kristina; Donohue, Julie M.; Wiringa, Ann E.; Bailey, Rachel R.; Zimmerman, Richard K.

2011-01-01

Although studies have suggested that a patient’s perceived cost-benefit of a medical intervention could affect his or her utilization of the intervention, the economic value of influenza vaccine from the patient’s perspective remains unclear. Therefore, we developed a stochastic decision analytic computer model representing an adult’s decision of whether to get vaccinated. Different scenarios explored the impact of the patient being insured versus uninsured, influenza attack rate, vaccine administration costs and vaccination time costs. Results indicated that cost of avoiding influenza was fairly low, with one driver being required vaccination time. To encourage vaccination, decision makers may want to focus on ways to reduce this time, such as vaccinating at work, churches, or other normally frequented locations. PMID:21215340

CAMELOT: Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox

NASA Astrophysics Data System (ADS)

Di Carlo, Marilena; Romero Martin, Juan Manuel; Vasile, Massimiliano

2018-03-01

Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox (CAMELOT) is a toolbox for the fast preliminary design and optimisation of low-thrust trajectories. It solves highly complex combinatorial problems to plan multi-target missions characterised by long spirals including different perturbations. To do so, CAMELOT implements a novel multi-fidelity approach combining analytical surrogate modelling and accurate computational estimations of the mission cost. Decisions are then made using two optimisation engines included in the toolbox, a single-objective global optimiser, and a combinatorial optimisation algorithm. CAMELOT has been applied to a variety of case studies: from the design of interplanetary trajectories to the optimal de-orbiting of space debris and from the deployment of constellations to on-orbit servicing. In this paper, the main elements of CAMELOT are described and two examples, solved using the toolbox, are presented.

Model For Marketing Strategy Decision Based On Multicriteria Decicion Making: A Case Study In Batik Madura Industry

NASA Astrophysics Data System (ADS)

Anna, I. D.; Cahyadi, I.; Yakin, A.

2018-01-01

Selection of marketing strategy is a prominent competitive advantage for small and medium enterprises business development. The selection process is is a multiple criteria decision-making problem, which includes evaluation of various attributes or criteria in a process of strategy formulation. The objective of this paper is to develop a model for the selection of a marketing strategy in Batik Madura industry. The current study proposes an integrated approach based on analytic network process (ANP) and technique for order preference by similarity to ideal solution (TOPSIS) to determine the best strategy for Batik Madura marketing problems. Based on the results of group decision-making technique, this study selected fourteen criteria, including consistency, cost, trend following, customer loyalty, business volume, uniqueness manpower, customer numbers, promotion, branding, bussiness network, outlet location, credibility and the inovation as Batik Madura marketing strategy evaluation criteria. A survey questionnaire developed from literature review was distributed to a sample frame of Batik Madura SMEs in Pamekasan. In the decision procedure step, expert evaluators were asked to establish the decision matrix by comparing the marketing strategy alternatives under each of the individual criteria. Then, considerations obtained from ANP and TOPSIS methods were applied to build the specific criteria constraints and range of the launch strategy in the model. The model in this study demonstrates that, under current business situation, Straight-focus marketing strategy is the best marketing strategy for Batik Madura SMEs in Pamekasan.

Analytic innovations for air quality modeling | Science ...

EPA Pesticide Factsheets

The presentation provides an overview of ongoing research activities at the U.S. EPA, focusing on improving long-term emission projections and the development of decision support systems for coordinated environmental, climate and energy planning. This presentation will be given on October 10th, 2016, at the Johns Hopkins Dept. of Environmental Health and Engineering as part of the Environmental Science and Management Seminar Series.

Collaboration and Synergy among Government, Industry and Academia in M&S Domain: Turkey’s Approach

DTIC Science & Technology

2009-10-01

Analysis, Decision Support System Design and Implementation, Simulation Output Analysis, Statistical Data Analysis, Virtual Reality , Artificial... virtual and constructive visual simulation systems as well as integrated advanced analytical models. Collaboration and Synergy among Government...simulation systems that are ready to use, credible, integrated with C4ISR systems.  Creating synthetic environments and/or virtual prototypes of concepts

Health Care Transformation: A Strategy Rooted in Data and Analytics.

PubMed

Koster, John; Stewart, Elizabeth; Kolker, Eugene

2016-02-01

Today's consumers purchasing any product or service are armed with information and have high expectations. They expect service providers and payers to know about their unique needs. Data-driven decisions can help organizations meet those expectations and fulfill those needs.Health care, however, is not strictly a retail relationship-the sacred trust between patient and doctor, the clinician-patient relationship, must be preserved. The opportunities and challenges created by the digitization of health care are at the crux of the most crucial strategic decisions for academic medicine. A transformational vision grounded in data and analytics must guide health care decisions and actions.In this Commentary, the authors describe three examples of the transformational force of data and analytics to improve health care in order to focus attention on academic medicine's vital role in guiding the needed changes.

A judgment and decision-making model for plant behavior.

PubMed

Karban, Richard; Orrock, John L

2018-06-12

Recently plant biologists have documented that plants, like animals, engage in many activities that can be considered as behaviors, although plant biologists currently lack a conceptual framework to understand these processes. Borrowing the well-established framework developed by psychologists, we propose that plant behaviors can be constructively modeled by identifying four distinct components: 1) a cue or stimulus that provides information, 2) a judgment whereby the plant perceives and processes this informative cue, 3) a decision whereby the plant chooses among several options based on their relative costs and benefits, and 4) action. Judgment for plants can be determined empirically by monitoring signaling associated with electrical, calcium, or hormonal fluxes. Decision-making can be evaluated empirically by monitoring gene expression or differential allocation of resources. We provide examples of the utility of this judgment and decision-making framework by considering cases in which plants either successfully or unsuccessfully induced resistance against attacking herbivores. Separating judgment from decision-making suggests new analytical paradigms (i.e., Bayesian methods for judgment and economic utility models for decision-making). Following this framework, we propose an experimental approach to plant behavior that explicitly manipulates the stimuli provided to plants, uses plants that vary in sensory abilities, and examines how environmental context affects plant responses. The concepts and approaches that follow from the judgment and decision-making framework can shape how we study and understand plant-herbivore interactions, biological invasions, plant responses to climate change, and the susceptibility of plants to evolutionary traps. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

An integrated fuzzy approach for strategic alliance partner selection in third-party logistics.

PubMed

Erkayman, Burak; Gundogar, Emin; Yilmaz, Aysegul

2012-01-01

Outsourcing some of the logistic activities is a useful strategy for companies in recent years. This makes it possible for firms to concentrate on their main issues and processes and presents facility to improve logistics performance, to reduce costs, and to improve quality. Therefore provider selection and evaluation in third-party logistics become important activities for companies. Making a strategic decision like this is significantly hard and crucial. In this study we proposed a fuzzy multicriteria decision making (MCDM) approach to effectively select the most appropriate provider. First we identify the provider selection criteria and build the hierarchical structure of decision model. After building the hierarchical structure we determined the selection criteria weights by using fuzzy analytical hierarchy process (AHP) technique. Then we applied fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to obtain final rankings for providers. And finally an illustrative example is also given to demonstrate the effectiveness of the proposed model.

An Integrated Fuzzy Approach for Strategic Alliance Partner Selection in Third-Party Logistics

PubMed Central

Gundogar, Emin; Yılmaz, Aysegul

2012-01-01

Outsourcing some of the logistic activities is a useful strategy for companies in recent years. This makes it possible for firms to concentrate on their main issues and processes and presents facility to improve logistics performance, to reduce costs, and to improve quality. Therefore provider selection and evaluation in third-party logistics become important activities for companies. Making a strategic decision like this is significantly hard and crucial. In this study we proposed a fuzzy multicriteria decision making (MCDM) approach to effectively select the most appropriate provider. First we identify the provider selection criteria and build the hierarchical structure of decision model. After building the hierarchical structure we determined the selection criteria weights by using fuzzy analytical hierarchy process (AHP) technique. Then we applied fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to obtain final rankings for providers. And finally an illustrative example is also given to demonstrate the effectiveness of the proposed model. PMID:23365520

Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference.

PubMed

Ceriotti, Ferruccio; Fernandez-Calle, Pilar; Klee, George G; Nordin, Gunnar; Sandberg, Sverre; Streichert, Thomas; Vives-Corrons, Joan-Lluis; Panteghini, Mauro

2017-02-01

This paper, prepared by the EFLM Task and Finish Group on Allocation of laboratory tests to different models for performance specifications (TFG-DM), is dealing with criteria for allocating measurands to the different models for analytical performance specifications (APS) recognized in the 1st EFLM Strategic Conference Consensus Statement. Model 1, based on the effect of APS on clinical outcome, is the model of choice for measurands that have a central role in the decision-making of a specific disease or clinical situation and where cut-off/decision limits are established for either diagnosing, screening or monitoring. Total cholesterol, glucose, HbA1c, serum albumin and cardiac troponins represent practical examples. Model 2 is based on components of biological variation and should be applied to measurands that do not have a central role in a specific disease or clinical situation, but where the concentration of the measurand is in a steady state. This is best achieved for measurands under strict homeostatic control in order to preserve their concentrations in the body fluid of interest, but it can also be applied to other measurands that are in a steady state in biological fluids. In this case, it is expected that the "noise" produced by the measurement procedure will not significantly alter the signal provided by the concentration of the measurand. This model especially applies to electrolytes and minerals in blood plasma (sodium, potassium, chloride, bicarbonate, calcium, magnesium, inorganic phosphate) and to creatinine, cystatin C, uric acid and total protein in plasma. Model 3, based on state-of-the-art of the measurement, should be used for all the measurands that cannot be included in models 1 or 2.

Cost-effectiveness analysis of initial treatment strategies for mild-to-moderate Clostridium difficile infection in hospitalized patients.

PubMed

Ford, Diana C; Schroeder, Mary C; Ince, Dilek; Ernst, Erika J

2018-06-14

The cost-effectiveness of initial treatment strategies for mild-to-moderate Clostridium difficile infection (CDI) in hospitalized patients was evaluated. Decision-analytic models were constructed to compare initial treatment with metronidazole, vancomycin, and fidaxomicin. The primary model included 1 recurrence, and the secondary model included up to 3 recurrences. Model variables were extracted from published literature with costs based on a healthcare system perspective. The primary outcome was the incremental cost-effective ratio (ICER) between initial treatment strategies. In the primary model, the overall percentage of patients cured was 94.23%, 95.19%, and 96.53% with metronidazole, vancomycin, and fidaxomicin, respectively. Expected costs per case were $1,553.01, $1,306.62, and $5,095.70, respectively. In both models, vancomycin was more effective and less costly than metronidazole, resulting in negative ICERs. The ICERs for fidaxomicin compared with those for metronidazole and vancomycin in the primary model were $1,540.23 and $2,828.69 per 1% gain in cure, respectively. Using these models, a hospital currently treating initial episodes of mild-to-moderate CDI with metronidazole could expect to save $246.39-$388.37 per case treated by using vancomycin for initial therapy. A decision-analytic model revealed vancomycin to be cost-effective, compared with metronidazole, for treatment of initial episodes of mild-to-moderate CDI in adult inpatients. From the hospital perspective, initial treatment with vancomycin resulted in a higher probability of cure and a lower probability of colectomy, recurrence, persistent recurrence, and cost per case treated, compared with metronidazole. Use of fidaxomicin was associated with an increased probability of cure compared with metronidazole and vancomycin, but at a substantially increased cost. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Group assessment of key indicators of sustainable waste management in developing countries.

PubMed

Tot, Bojana; Vujić, Goran; Srđević, Zorica; Ubavin, Dejan; Russo, Mário Augusto Tavares

2017-09-01

Decision makers in developing countries are struggling to solve the present problems of solid waste management. Prioritisation and ranking of the most important indicators that influence the waste management system is very useful for any decision maker for the future planning and implementation of a sustainable waste management system. The aim of this study is to evaluate key indicators and their related sub-indicators in a group decision-making environment. In order to gain insight into the subject it was necessary to obtain the qualified opinions of decision makers from different countries who understand the situation in the sector of waste management in developing countries. An assessment is performed by 43 decision makers from both developed and developing countries, and the applied methodology is based on a combined use of the analytic hierarchy process, from the multi-criteria decision-making set of tools, and the preferential voting method known as Borda Count, which belongs to social choice theory. Pairwise comparison of indicators is performed with the analytic hierarchy process, and the ranking of indicators once obtained is assessed with Borda Count. Detailed analysis of the final results showed that the Institutional-Administrative indicator was the most important one, with the maximum weight as derived by both groups of decision makers. The results also showed that the combined use of the analytic hierarchy process and Borda Count contributes to the credibility and objectivity of the decision-making process, allowing its use in more complex waste management group decision-making problems to be recommended.

Cost effectiveness of ovarian reserve testing in in vitro fertilization: a Markov decision-analytic model.

PubMed

Moolenaar, Lobke M; Broekmans, Frank J M; van Disseldorp, Jeroen; Fauser, Bart C J M; Eijkemans, Marinus J C; Hompes, Peter G A; van der Veen, Fulco; Mol, Ben Willem J

2011-10-01

To compare the cost effectiveness of ovarian reserve testing in in vitro fertilization (IVF). A Markov decision model based on data from the literature and original patient data. Decision analytic framework. Computer-simulated cohort of subfertile women aged 20 to 45 years who are eligible for IVF. [1] No treatment, [2] up to three cycles of IVF limited to women under 41 years and no ovarian reserve testing, [3] up to three cycles of IVF with dose individualization of gonadotropins according to ovarian reserve, and [4] up to three cycles of IVF with ovarian reserve testing and exclusion of expected poor responders after the first cycle, with no treatment scenario as the reference scenario. Cumulative live birth over 1 year, total costs, and incremental cost-effectiveness ratios. The cumulative live birth was 9.0% in the no treatment scenario, 54.8% for scenario 2, 70.6% for scenario 3 and 51.9% for scenario 4. Absolute costs per woman for these scenarios were €0, €6,917, €6,678, and €5,892 for scenarios 1, 2, 3, and 4, respectively. Incremental cost-effectiveness ratios (ICER) for scenarios 2, 3, and 4 were €15,166, €10,837, and €13,743 per additional live birth. Sensitivity analysis showed the model to be robust over a wide range of values. Individualization of the follicle-stimulating hormone dose according to ovarian reserve is likely to be cost effective in women who are eligible for IVF, but this effectiveness needs to be confirmed in randomized clinical trials. Copyright © 2011 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Event-related potentials during individual, cooperative, and competitive task performance differ in subjects with analytic vs. holistic thinking.

PubMed

Apanovich, V V; Bezdenezhnykh, B N; Sams, M; Jääskeläinen, I P; Alexandrov, YuI

2018-01-01

It has been presented that Western cultures (USA, Western Europe) are mostly characterized by competitive forms of social interaction, whereas Eastern cultures (Japan, China, Russia) are mostly characterized by cooperative forms. It has also been stated that thinking in Eastern countries is predominantly holistic and in Western countries analytic. Based on this, we hypothesized that subjects with analytic vs. holistic thinking styles show differences in decision making in different types of social interaction conditions. We investigated behavioural and brain-activity differences between subjects with analytic and holistic thinking during a choice reaction time (ChRT) task, wherein the subjects either cooperated, competed (in pairs), or performed the task without interaction with other participants. Healthy Russian subjects (N=78) were divided into two groups based on having analytic or holistic thinking as determined with an established questionnaire. We measured reaction times as well as event-related brain potentials. There were significant differences between the interaction conditions in task performance between subjects with analytic and holistic thinking. Both behavioral performance and physiological measures exhibited higher variance in holistic than in analytic subjects. Differences in amplitude and P300 latency suggest that decision making was easier for the holistic subjects in the cooperation condition, in contrast to analytic subjects for whom decision making based on these measures seemed to be easier in the competition condition. The P300 amplitude was higher in the individual condition as compared with the collective conditions. Overall, our results support the notion that the brains of analytic and holistic subjects work differently in different types of social interaction conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Integrating research tools to support the management of social-ecological systems under climate change

USGS Publications Warehouse

Miller, Brian W.; Morisette, Jeffrey T.

2014-01-01

Developing resource management strategies in the face of climate change is complicated by the considerable uncertainty associated with projections of climate and its impacts and by the complex interactions between social and ecological variables. The broad, interconnected nature of this challenge has resulted in calls for analytical frameworks that integrate research tools and can support natural resource management decision making in the face of uncertainty and complex interactions. We respond to this call by first reviewing three methods that have proven useful for climate change research, but whose application and development have been largely isolated: species distribution modeling, scenario planning, and simulation modeling. Species distribution models provide data-driven estimates of the future distributions of species of interest, but they face several limitations and their output alone is not sufficient to guide complex decisions for how best to manage resources given social and economic considerations along with dynamic and uncertain future conditions. Researchers and managers are increasingly exploring potential futures of social-ecological systems through scenario planning, but this process often lacks quantitative response modeling and validation procedures. Simulation models are well placed to provide added rigor to scenario planning because of their ability to reproduce complex system dynamics, but the scenarios and management options explored in simulations are often not developed by stakeholders, and there is not a clear consensus on how to include climate model outputs. We see these strengths and weaknesses as complementarities and offer an analytical framework for integrating these three tools. We then describe the ways in which this framework can help shift climate change research from useful to usable.

Challenges to Applying a Metamodel for Groundwater Flow Beyond Underlying Numerical Model Boundaries

NASA Astrophysics Data System (ADS)

Reeves, H. W.; Fienen, M. N.; Feinstein, D.

2015-12-01

Metamodels of environmental behavior offer opportunities for decision support, adaptive management, and increased stakeholder engagement through participatory modeling and model exploration. Metamodels are derived from calibrated, computationally demanding, numerical models. They may potentially be applied to non-modeled areas to provide screening or preliminary analysis tools for areas that do not yet have the benefit of more comprehensive study. In this decision-support mode, they may be fulfilling a role often accomplished by application of analytical solutions. The major challenge to transferring a metamodel to a non-modeled area is how to quantify the spatial data in the new area of interest in such a way that it is consistent with the data used to derive the metamodel. Tests based on transferring a metamodel derived from a numerical groundwater-flow model of the Lake Michigan Basin to other glacial settings across the northern U.S. show that the spatial scale of the numerical model must be appropriately scaled to adequately represent different settings. Careful GIS analysis of the numerical model, metamodel, and new area of interest is required for successful transfer of results.

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

NASA Astrophysics Data System (ADS)

Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

2016-04-01

Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more accurate measure of forecast uncertainty that could result in better decision-making. It offers different level of abstractions to help with the recalibration of the RAR method. It also has an inspection tool that displays the selected analogs, their observations and statistical data. It gives the users access to inner parts of the method, unveiling hidden information. References [GR05] GNEITING T., RAFTERY A. E.: Weather forecasting with ensemble methods. Science 310, 5746, 248-249, 2005. [KAL03] KALNAY E.: Atmospheric modeling, data assimilation and predictability. Cambridge University Press, 2003. [PH06] PALMER T., HAGEDORN R.: Predictability of weather and climate. Cambridge University Press, 2006. [HW06] HAMILL T. M., WHITAKER J. S.: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Monthly Weather Review 134, 11, 3209-3229, 2006. [DE06] DEITRICK S., EDSALL R.: The influence of uncertainty visualization on decision making: An empirical evaluation. Springer, 2006. [KMS08] KEIM D. A., MANSMANN F., SCHNEIDEWIND J., THOMAS J., ZIEGLER H.: Visual analytics: Scope and challenges. Springer, 2008.

A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers

DOE PAGES

Fredriksson, Sten-Ake; Wunschel, David S.; Lindstrom, Susanne Wiklund; ...

2018-03-28

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1 – PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods andmore » robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.« less

A Ricin Forensic Profiling Approach Based on a Complex Set of Biomarkers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fredriksson, Sten-Ake; Wunschel, David S.; Lindstrom, Susanne Wiklund

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1 – PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods andmore » robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved.« less

40 CFR Appendix G to Subpart A of... - UNEP Recommendations for Conditions Applied to Exemption for Essential Laboratory and Analytical...

Code of Federal Regulations, 2013 CFR

2013-07-01

... and laboratory purposes. Pursuant to Decision XI/15 of the Parties to the Montreal Protocol, effective... laboratory and analytical purposes is authorized provided that these laboratory and analytical chemicals..., restricted to laboratory use and analytical purposes and specifying that used or surplus substances should be...

40 CFR Appendix G to Subpart A of... - UNEP Recommendations for Conditions Applied to Exemption for Essential Laboratory and Analytical...

Code of Federal Regulations, 2010 CFR

2010-07-01

... and laboratory purposes. Pursuant to Decision XI/15 of the Parties to the Montreal Protocol, effective... laboratory and analytical purposes is authorized provided that these laboratory and analytical chemicals..., restricted to laboratory use and analytical purposes and specifying that used or surplus substances should be...

40 CFR Appendix G to Subpart A of... - UNEP Recommendations for Conditions Applied to Exemption for Essential Laboratory and Analytical...

Code of Federal Regulations, 2014 CFR

2014-07-01

... and laboratory purposes. Pursuant to Decision XI/15 of the Parties to the Montreal Protocol, effective... laboratory and analytical purposes is authorized provided that these laboratory and analytical chemicals..., restricted to laboratory use and analytical purposes and specifying that used or surplus substances should be...

40 CFR Appendix G to Subpart A of... - UNEP Recommendations for Conditions Applied to Exemption for Essential Laboratory and Analytical...

Code of Federal Regulations, 2012 CFR

2012-07-01

... and laboratory purposes. Pursuant to Decision XI/15 of the Parties to the Montreal Protocol, effective... laboratory and analytical purposes is authorized provided that these laboratory and analytical chemicals..., restricted to laboratory use and analytical purposes and specifying that used or surplus substances should be...

40 CFR Appendix G to Subpart A of... - UNEP Recommendations for Conditions Applied to Exemption for Essential Laboratory and Analytical...

Code of Federal Regulations, 2011 CFR

2011-07-01

... and laboratory purposes. Pursuant to Decision XI/15 of the Parties to the Montreal Protocol, effective... laboratory and analytical purposes is authorized provided that these laboratory and analytical chemicals..., restricted to laboratory use and analytical purposes and specifying that used or surplus substances should be...

The Ising Decision Maker: a binary stochastic network for choice response time.

PubMed

Verdonck, Stijn; Tuerlinckx, Francis

2014-07-01

The Ising Decision Maker (IDM) is a new formal model for speeded two-choice decision making derived from the stochastic Hopfield network or dynamic Ising model. On a microscopic level, it consists of 2 pools of binary stochastic neurons with pairwise interactions. Inside each pool, neurons excite each other, whereas between pools, neurons inhibit each other. The perceptual input is represented by an external excitatory field. Using methods from statistical mechanics, the high-dimensional network of neurons (microscopic level) is reduced to a two-dimensional stochastic process, describing the evolution of the mean neural activity per pool (macroscopic level). The IDM can be seen as an abstract, analytically tractable multiple attractor network model of information accumulation. In this article, the properties of the IDM are studied, the relations to existing models are discussed, and it is shown that the most important basic aspects of two-choice response time data can be reproduced. In addition, the IDM is shown to predict a variety of observed psychophysical relations such as Piéron's law, the van der Molen-Keuss effect, and Weber's law. Using Bayesian methods, the model is fitted to both simulated and real data, and its performance is compared to the Ratcliff diffusion model. (c) 2014 APA, all rights reserved.

Decision-making for foot-and-mouth disease control: Objectives matter

USGS Publications Warehouse

Probert, William J. M.; Shea, Katriona; Fonnesbeck, Christopher J.; Runge, Michael C.; Carpenter, Tim E.; Durr, Salome; Garner, M. Graeme; Harvey, Neil; Stevenson, Mark A.; Webb, Colleen T.; Werkman, Marleen; Tildesley, Michael J.; Ferrari, Matthew J.

2016-01-01

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Cost-Utility Analysis of Bariatric Surgery in Italy: Results of Decision-Analytic Modelling.

PubMed

Lucchese, Marcello; Borisenko, Oleg; Mantovani, Lorenzo Giovanni; Cortesi, Paolo Angelo; Cesana, Giancarlo; Adam, Daniel; Burdukova, Elisabeth; Lukyanov, Vasily; Di Lorenzo, Nicola

2017-01-01

To evaluate the cost-effectiveness of bariatric surgery in Italy from a third-party payer perspective over a medium-term (10 years) and a long-term (lifetime) horizon. A state-transition Markov model was developed, in which patients may experience surgery, post-surgery complications, diabetes mellitus type 2, cardiovascular diseases or die. Transition probabilities, costs, and utilities were obtained from the Italian and international literature. Three types of surgeries were considered: gastric bypass, sleeve gastrectomy, and adjustable gastric banding. A base-case analysis was performed for the population, the characteristics of which were obtained from surgery candidates in Italy. In the base-case analysis, over 10 years, bariatric surgery led to cost increment of EUR 2,661 and generated additional 1.1 quality-adjusted life years (QALYs). Over a lifetime, surgery led to savings of EUR 8,649, additional 0.5 life years and 3.2 QALYs. Bariatric surgery was cost-effective at 10 years with an incremental cost-effectiveness ratio of EUR 2,412/QALY and dominant over conservative management over a lifetime. In a comprehensive decision analytic model, a current mix of surgical methods for bariatric surgery was cost-effective at 10 years and cost-saving over the lifetime of the Italian patient cohort considered in this analysis. © 2017 The Author(s) Published by S. Karger GmbH, Freiburg.

Decision Making on Medical Innovations in a Changing Health Care Environment: Insights from Accountable Care Organizations and Payers on Personalized Medicine and Other Technologies.

PubMed

Trosman, Julia R; Weldon, Christine B; Douglas, Michael P; Deverka, Patricia A; Watkins, John B; Phillips, Kathryn A

2017-01-01

New payment and care organization approaches, such as those of accountable care organizations (ACOs), are reshaping accountability and shifting risk, as well as decision making, from payers to providers, within the Triple Aim context of health reform. The Triple Aim calls for improving experience of care, improving health of populations, and reducing health care costs. To understand how the transition to the ACO model impacts decision making on adoption and use of innovative technologies in the era of accelerating scientific advancement of personalized medicine and other innovations. We interviewed representatives from 10 private payers and 6 provider institutions involved in implementing the ACO model (i.e., ACOs) to understand changes, challenges, and facilitators of decision making on medical innovations, including personalized medicine. We used the framework approach of qualitative research for study design and thematic analysis. We found that representatives from the participating payer companies and ACOs perceive similar challenges to ACOs' decision making in terms of achieving a balance between the components of the Triple Aim-improving care experience, improving population health, and reducing costs. The challenges include the prevalence of cost over care quality considerations in ACOs' decisions and ACOs' insufficient analytical and technology assessment capacity to evaluate complex innovations such as personalized medicine. Decision-making facilitators included increased competition across ACOs and patients' interest in personalized medicine. As new payment models evolve, payers, ACOs, and other stakeholders should address challenges and leverage opportunities to arm ACOs with robust, consistent, rigorous, and transparent approaches to decision making on medical innovations. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Decision-Making on Medical Innovations in a Changing Healthcare Environment: Insights from Accountable Care Organizations and Payers on Personalized Medicine and Other Technologies

PubMed Central

Trosman, Julia R.; Weldon, Christine B.; Douglas, Michael P.; Deverka, Patricia A.; Watkins, John; Phillips, Kathryn A.

2016-01-01

Background New payment and care organization approaches, such as the Accountable Care Organization (ACO), are reshaping accountability and shifting risk, as well as decision-making, from payers to providers, under the Triple Aim of health reform. The Triple Aim calls for improving experience of care, improving health of populations and reducing healthcare costs. In the era of accelerating scientific advancement of personalized medicine and other innovations, it is critical to understand how the transition to the ACO model impacts decision-making on adoption and utilization of innovative technologies. Methods We interviewed representatives from ten private payers and six provider institutions involved in implementing the ACO model (i.e. ACOs) to understand changes, challenges and facilitators of decision-making on medical innovations, including personalized medicine. We used the framework approach of qualitative research for study design and thematic analysis. Results We found that representatives from the participating payer companies and ACOs perceive similar challenges to ACOs’ decision-making in terms of achieving a balance between the components of the Triple Aim – improving care experience, improving population health and reducing costs. The challenges include the prevalence of cost over care quality considerations in ACOs’ decisions and ACOs’ insufficient analytical and technology assessment capacity to evaluate complex innovations such as personalized medicine. Decision-making facilitators included increased competition across ACOs and patients’ interest in personalized medicine. Conclusions As new payment models evolve, payers, ACOs and other stakeholders should address challenges and leverage opportunities to arm ACOs with robust, consistent, rigorous and transparent approaches to decision-making on medical innovations. PMID:28212967

The PANTHER User Experience

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coram, Jamie L.; Morrow, James D.; Perkins, David Nikolaus

2015-09-01

This document describes the PANTHER R&D Application, a proof-of-concept user interface application developed under the PANTHER Grand Challenge LDRD. The purpose of the application is to explore interaction models for graph analytics, drive algorithmic improvements from an end-user point of view, and support demonstration of PANTHER technologies to potential customers. The R&D Application implements a graph-centric interaction model that exposes analysts to the algorithms contained within the GeoGraphy graph analytics library. Users define geospatial-temporal semantic graph queries by constructing search templates based on nodes, edges, and the constraints among them. Users then analyze the results of the queries using bothmore » geo-spatial and temporal visualizations. Development of this application has made user experience an explicit driver for project and algorithmic level decisions that will affect how analysts one day make use of PANTHER technologies.« less

Big data analytics to improve cardiovascular care: promise and challenges.

PubMed

Rumsfeld, John S; Joynt, Karen E; Maddox, Thomas M

2016-06-01

The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.

The role of analytical science in natural resource decision making

NASA Astrophysics Data System (ADS)

Miller, Alan

1993-09-01

There is a continuing debate about the proper role of analytical (positivist) science in natural resource decision making. Two diametrically opposed views are evident, arguing for and against a more extended role for scientific information. The debate takes on a different complexion if one recognizes that certain kinds of problem, referred to here as “wicked” or “trans-science” problems, may not be amenable to the analytical process. Indeed, the mistaken application of analytical methods to trans-science problems may not only be a waste of time and money but also serve to hinder policy development. Since many environmental issues are trans-science in nature, then it follows that alternatives to analytical science need to be developed. In this article, the issues involved in the debate are clarified by examining the impact of the use of analytical methods in a particular case, the spruce budworm controversy in New Brunswick. The article ends with some suggestions about a “holistic” approach to the problem.

Visual analytics in medical education: impacting analytical reasoning and decision making for quality improvement.

PubMed

Vaitsis, Christos; Nilsson, Gunnar; Zary, Nabil

2015-01-01

The medical curriculum is the main tool representing the entire undergraduate medical education. Due to its complexity and multilayered structure it is of limited use to teachers in medical education for quality improvement purposes. In this study we evaluated three visualizations of curriculum data from a pilot course, using teachers from an undergraduate medical program and applying visual analytics methods. We found that visual analytics can be used to positively impacting analytical reasoning and decision making in medical education through the realization of variables capable to enhance human perception and cognition on complex curriculum data. The positive results derived from our evaluation of a medical curriculum and in a small scale, signify the need to expand this method to an entire medical curriculum. As our approach sustains low levels of complexity it opens a new promising direction in medical education informatics research.

Workshop on Current Issues in Predictive Approaches to Intelligence and Security Analytics: Fostering the Creation of Decision Advantage through Model Integration and Evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.

2010-05-23

The increasing asymmetric nature of threats to the security, health and sustainable growth of our society requires that anticipatory reasoning become an everyday activity. Currently, the use of anticipatory reasoning is hindered by the lack of systematic methods for combining knowledge- and evidence-based models, integrating modeling algorithms, and assessing model validity, accuracy and utility. The workshop addresses these gaps with the intent of fostering the creation of a community of interest on model integration and evaluation that may serve as an aggregation point for existing efforts and a launch pad for new approaches.

Restoring and Managing Gulf of Mexico Fisheries: A Path Toward Creative Decision-Making

EPA Science Inventory

This chapter introduces decision analysis concepts with examples for managing fisheries. Decision analytic methods provide useful tools for structuring environmental management problems and separating technical judgments from preference judgments to better weigh the prospects fro...

Optimal management of colorectal liver metastases in older patients: a decision analysis

PubMed Central

Yang, Simon; Alibhai, Shabbir MH; Kennedy, Erin D; El-Sedfy, Abraham; Dixon, Matthew; Coburn, Natalie; Kiss, Alex; Law, Calvin HL

2014-01-01

Background Comparative trials evaluating management strategies for colorectal cancer liver metastases (CLM) are lacking, especially for older patients. This study developed a decision-analytic model to quantify outcomes associated with treatment strategies for CLM in older patients. Methods A Markov-decision model was built to examine the effect on life expectancy (LE) and quality-adjusted life expectancy (QALE) for best supportive care (BSC), systemic chemotherapy (SC), radiofrequency ablation (RFA) and hepatic resection (HR). The baseline patient cohort assumptions included healthy 70-year-old CLM patients after a primary cancer resection. Event and transition probabilities and utilities were derived from a literature review. Deterministic and probabilistic sensitivity analyses were performed on all study parameters. Results In base case analysis, BSC, SC, RFA and HR yielded LEs of 11.9, 23.1, 34.8 and 37.0 months, and QALEs of 7.8, 13.2, 22.0 and 25.0 months, respectively. Model results were sensitive to age, comorbidity, length of model simulation and utility after HR. Probabilistic sensitivity analysis showed increasing preference for RFA over HR with increasing patient age. Conclusions HR may be optimal for healthy 70-year-old patients with CLM. In older patients with comorbidities, RFA may provide better LE and QALE. Treatment decisions in older cancer patients should account for patient age, comorbidities, local expertise and individual values. PMID:24961482

Value of Earth Observations: Key principles and techniques of socioeconomic benefits analysis (Invited)

NASA Astrophysics Data System (ADS)

Friedl, L.; Macauley, M.; Bernknopf, R.

2013-12-01

Internationally, multiple organizations are placing greater emphasis on the societal benefits that governments, businesses, and NGOs can derive from applications of Earth-observing satellite observations, research, and models. A growing set of qualitative, anecdotal examples on the uses of Earth observations across a range of sectors can be complemented by the quantitative substantiation of the socioeconomic benefits. In turn, the expanding breadth of environmental data available and the awareness of their beneficial applications to inform decisions can support new products and services by companies, agencies, and civil society. There are, however, significant efforts needed to bridge the Earth sciences and social and economic sciences fields to build capacity, develop case studies, and refine analytic techniques in quantifying socioeconomic benefits from the use of Earth observations. Some government programs, such as the NASA Earth Science Division's Applied Sciences Program have initiated activities in recent years to quantify the socioeconomic benefits from applications of Earth observations research, and to develop multidisciplinary models for organizations' decision-making activities. A community of practice has conducted workshops, developed impact analysis reports, published a book, developed a primer, and pursued other activities to advance analytic methodologies and build capacity. This paper will present an overview of measuring socioeconomic impacts of Earth observations and how the measures can be translated into a value of Earth observation information. It will address key terms, techniques, principles and applications of socioeconomic impact analyses. It will also discuss activities to pursue a research agenda on analytic techniques, develop a body of knowledge, and promote broader skills and capabilities.

What if Learning Analytics Were Based on Learning Science?

ERIC Educational Resources Information Center

Marzouk, Zahia; Rakovic, Mladen; Liaqat, Amna; Vytasek, Jovita; Samadi, Donya; Stewart-Alonso, Jason; Ram, Ilana; Woloshen, Sonya; Winne, Philip H.; Nesbit, John C.

2016-01-01

Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students' decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning…

Incorporating Learning Analytics in the Classroom

ERIC Educational Resources Information Center

Thille, Candace; Zimmaro, Dawn

2017-01-01

This chapter describes an open learning analytics system focused on learning process measures and designed to engage instructors and students in an evidence-informed decision-making process to improve learning.

Decision Making in the Airplane

NASA Technical Reports Server (NTRS)

Orasanu, Judith; Shafto, Michael G. (Technical Monitor)

1995-01-01

The Importance of decision-making to safety in complex, dynamic environments like mission control centers, aviation, and offshore installations has been well established. NASA-ARC has a program of research dedicated to fostering safe and effective decision-making in the manned spaceflight environment. Because access to spaceflight is limited, environments with similar characteristics, including aviation and nuclear power plants, serve as analogs from which space-relevant data can be gathered and theories developed. Analyses of aviation accidents cite crew judgement and decision making as causes or contributing factors in over half of all accidents. Yet laboratory research on decision making has not proven especially helpful In improving the quality of decisions in these kinds of environments. One reason is that the traditional, analytic decision models are inappropriate to multi-dimensional, high-risk environments, and do not accurately describe what expert human decision makers do when they make decisions that have consequences. A new model of dynamic, naturalistic decision making is offered that may prove useful for improving decision making in complex, isolated, confined and high-risk environments. Based on analyses of crew performance in full-mission simulators and accident reports, features that define effective decision strategies in abnormal or emergency situations have been identified. These include accurate situation assessment (including time and risk assessment), appreciation of the complexity of the problem, sensitivity to constraints on the decision, timeliness of the response, and use of adequate information. More effective crews also manage their workload to provide themselves with time and resources to make good decisions. In brief, good decisions are appropriate to the demands of the situation. Effective crew decision making and overall performance are mediated by crew communication. Communication contributes to performance because it assures that all crew members have essential information, but it also regulates and coordinates crew actions and is the medium of collective thinking In response to a problem, This presentation will examine the relations between leadership, communication, decision making and overall crew performance. Implications of these findings for training will be discussed.

Analytical group decision making in natural resources: methodology and application

Treesearch

Daniel L. Schmoldt; David L. Peterson

2000-01-01

Group decision making is becoming increasingly important in natural resource management and associated scientific applications, because multiple values are treated coincidentally in time and space, multiple resource specialists are needed, and multiple stakeholders must be included in the decision process. Decades of social science research on decision making in groups...

Big Data Analytics in Chemical Engineering.

PubMed

Chiang, Leo; Lu, Bo; Castillo, Ivan

2017-06-07

Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.

PubMed

Durham, Erin-Elizabeth A; Yu, Xiaxia; Harrison, Robert W

2014-12-01

Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.

Use of the Analytic Hierarchy Process for Medication Decision-Making in Type 2 Diabetes

PubMed Central

Maruthur, Nisa M.; Joy, Susan M.; Dolan, James G.; Shihab, Hasan M.; Singh, Sonal

2015-01-01

Aim To investigate the feasibility and utility of the Analytic Hierarchy Process (AHP) for medication decision-making in type 2 diabetes. Methods We conducted an AHP with nine diabetes experts using structured interviews to rank add-on therapies (to metformin) for type 2 diabetes. During the AHP, participants compared treatment alternatives relative to eight outcomes (hemoglobin A1c-lowering and seven potential harms) and the relative importance of the different outcomes. The AHP model and instrument were pre-tested and pilot-tested prior to use. Results were discussed and an evaluation of the AHP was conducted during a group session. We conducted the quantitative analysis using Expert Choice software with the ideal mode to determine the priority of treatment alternatives. Results Participants judged exenatide to be the best add-on therapy followed by sitagliptin, sulfonylureas, and then pioglitazone. Maximizing benefit was judged 21% more important than minimizing harm. Minimizing severe hypoglycemia was judged to be the most important harm to avoid. Exenatide was the best overall alternative if the importance of minimizing harms was prioritized completely over maximizing benefits. Participants reported that the AHP improved transparency, consistency, and an understanding of others’ perspectives and agreed that the results reflected the views of the group. Conclusions The AHP is feasible and useful to make decisions about diabetes medications. Future studies which incorporate stakeholder preferences should evaluate other decision contexts, objectives, and treatments. PMID:26000636

Decision analysis with cumulative prospect theory.

PubMed

Bayoumi, A M; Redelmeier, D A

2000-01-01

Individuals sometimes express preferences that do not follow expected utility theory. Cumulative prospect theory adjusts for some phenomena by using decision weights rather than probabilities when analyzing a decision tree. The authors examined how probability transformations from cumulative prospect theory might alter a decision analysis of a prophylactic therapy in AIDS, eliciting utilities from patients with HIV infection (n = 75) and calculating expected outcomes using an established Markov model. They next focused on transformations of three sets of probabilities: 1) the probabilities used in calculating standard-gamble utility scores; 2) the probabilities of being in discrete Markov states; 3) the probabilities of transitioning between Markov states. The same prophylaxis strategy yielded the highest quality-adjusted survival under all transformations. For the average patient, prophylaxis appeared relatively less advantageous when standard-gamble utilities were transformed. Prophylaxis appeared relatively more advantageous when state probabilities were transformed and relatively less advantageous when transition probabilities were transformed. Transforming standard-gamble and transition probabilities simultaneously decreased the gain from prophylaxis by almost half. Sensitivity analysis indicated that even near-linear probability weighting transformations could substantially alter quality-adjusted survival estimates. The magnitude of benefit estimated in a decision-analytic model can change significantly after using cumulative prospect theory. Incorporating cumulative prospect theory into decision analysis can provide a form of sensitivity analysis and may help describe when people deviate from expected utility theory.

Implementing participatory decision making in forest planning.

PubMed

Ananda, Jayanath

2007-04-01

Forest policy decisions are often a source of debate, conflict, and tension in many countries. The debate over forest land-use decisions often hinges on disagreements about societal values related to forest resource use. Disagreements on social value positions are fought out repeatedly at local, regional, national, and international levels at an enormous social cost. Forest policy problems have some inherent characteristics that make them more difficult to deal with. On the one hand, forest policy decisions involve uncertainty, long time scales, and complex natural systems and processes. On the other hand, such decisions encompass social, political, and cultural systems that are evolving in response to forces such as globalization. Until recently, forest policy was heavily influenced by the scientific community and various economic models of optimal resource use. However, growing environmental awareness and acceptance of participatory democracy models in policy formulation have forced the public authorities to introduce new participatory mechanisms to manage forest resources. Most often, the efforts to include the public in policy formulation can be described using the lower rungs of Arnstein's public participation typology. This paper presents an approach that incorporates stakeholder preferences into forest land-use policy using the Analytic Hierarchy Process (AHP). An illustrative case of regional forest-policy formulation in Australia is used to demonstrate the approach. It is contended that applying the AHP in the policy process could considerably enhance the transparency of participatory process and public acceptance of policy decisions.

Accuracy of parameterized proton range models; A comparison

NASA Astrophysics Data System (ADS)

Pettersen, H. E. S.; Chaar, M.; Meric, I.; Odland, O. H.; Sølie, J. R.; Röhrich, D.

2018-03-01

An accurate calculation of proton ranges in phantoms or detector geometries is crucial for decision making in proton therapy and proton imaging. To this end, several parameterizations of the range-energy relationship exist, with different levels of complexity and accuracy. In this study we compare the accuracy of four different parameterizations models for proton range in water: Two analytical models derived from the Bethe equation, and two different interpolation schemes applied to range-energy tables. In conclusion, a spline interpolation scheme yields the highest reproduction accuracy, while the shape of the energy loss-curve is best reproduced with the differentiated Bragg-Kleeman equation.

Social Learning Networks: From Data Analytics to Active Sensing

DTIC Science & Technology

2017-10-13

time updating of user models that in turn dictate the learning path of each student . In particular, we have designed , implemented, and evaluated our...decision, unless so designated by other documentation. 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS (ES) U.S. Army Research Office P.O. Box...social network that exists between students , instructors, and modules of learning. Between 2015 and 2017, we completed a variety of data-driven

The Relationship Between Partial Contaminant Source Zone Remediation and Groundwater Plume Attenuation

NASA Astrophysics Data System (ADS)

Falta, R. W.

2004-05-01

Analytical solutions are developed that relate changes in the contaminant mass in a source area to the behavior of biologically reactive dissolved contaminant groundwater plumes. Based on data from field experiments, laboratory experiments, numerical streamtube models, and numerical multiphase flow models, the chemical discharge from a source region is assumed to be a nonlinear power function of the fraction of contaminant mass removed from the source zone. This function can approximately represent source zone mass discharge behavior over a wide range of site conditions ranging from simple homogeneous systems, to complex heterogeneous systems. A mass balance on the source zone with advective transport and first order decay leads to a nonlinear differential equation that is solved analytically to provide a prediction of the time-dependent contaminant mass discharge leaving the source zone. The solution for source zone mass discharge is coupled semi-analytically with a modified version of the Domenico (1987) analytical solution for three-dimensional reactive advective and dispersive transport in groundwater. The semi-analytical model then employs the BIOCHLOR (Aziz et al., 2000; Sun et al., 1999) transformations to model sequential first order parent-daughter biological decay reactions of chlorinated ethenes and ethanes in the groundwater plume. The resulting semi-analytic model thus allows for transient simulation of complex source zone behavior that is fully coupled to a dissolved contaminant plume undergoing sequential biological reactions. Analyses of several realistic scenarios show that substantial changes in the ground water plume can result from the partial removal of contaminant mass from the source zone. These results, however, are sensitive to the nature of the source mass reduction-source discharge reduction curve, and to the rates of degradation of the primary contaminant and its daughter products in the ground water plume. Aziz, C.E., C.J. Newell, J.R. Gonzales, P. Haas, T.P. Clement, and Y. Sun, 2000, BIOCHLOR Natural Attenuation Decision Support System User's Manual Version 1.0, US EPA Report EPA/600/R-00/008 Domenico, P.A., 1987, An analytical model for multidimensional transport of a decaying contaminant species, J. Hydrol., 91: 49-58. Sun, Y., J.N. Petersen, T.P. Clement, and R.S. Skeen, 1999, A new analytical solution for multi-species transport equations with serial and parallel reactions, Water Resour. Res., 35(1): 185-190.

Should cell-free DNA testing be used to target antenatal rhesus immune globulin administration?

PubMed

Ma, Kimberly K; Rodriguez, Maria I; Cheng, Yvonne W; Norton, Mary E; Caughey, Aaron B

2016-01-01

To compare the rates of alloimmunization with the use of cell-free DNA (cfDNA) screening to target antenatal rhesus immune globulin (RhIG) prenatally, versus routine administration of RhIG in rhesus D (RhD)-negative pregnant women in a theoretic cohort using a decision-analytic model. A decision-analytic model compared cfDNA testing to routine antenatal RhIG administration. The primary outcome was maternal sensitization to RhD antigen. Sensitivity and specificity of cfDNA testing were assumed to be 99.8% and 95.3%, respectively. Univariate and bivariate sensitivity analyses, Monte Carlo simulation, and threshold analyses were performed. In a cohort of 10,000 RhD-negative women, 22.6 sensitizations would occur with utilization of cfDNA, while 20 sensitizations would occur with routine RhIG. Only when the sensitivity of the cfDNA test reached 100%, the rate of sensitization was equal for both cfDNA and RhIG. Otherwise, routine RhIG minimized the rate of sensitization, especially given RhIG is readily available in the United States. Adoption of cfDNA testing would result in a 13.0% increase in sensitization among RhD-negative women in a theoretical cohort taking into account the ethnic diversity of the United States' population.

Training conservation practitioners to be better decision makers

USGS Publications Warehouse

Johnson, Fred A.; Eaton, Mitchell J.; Williams, James H.; Jensen, Gitte H.; Madsen, Jesper

2015-01-01

Traditional conservation curricula and training typically emphasizes only one part of systematic decision making (i.e., the science), at the expense of preparing conservation practitioners with critical skills in values-setting, working with decision makers and stakeholders, and effective problem framing. In this article we describe how the application of decision science is relevant to conservation problems and suggest how current and future conservation practitioners can be trained to be better decision makers. Though decision-analytic approaches vary considerably, they all involve: (1) properly formulating the decision problem; (2) specifying feasible alternative actions; and (3) selecting criteria for evaluating potential outcomes. Two approaches are available for providing training in decision science, with each serving different needs. Formal education is useful for providing simple, well-defined problems that allow demonstrations of the structure, axioms and general characteristics of a decision-analytic approach. In contrast, practical training can offer complex, realistic decision problems requiring more careful structuring and analysis than those used for formal training purposes. Ultimately, the kinds and degree of training necessary depend on the role conservation practitioners play in a decision-making process. Those attempting to facilitate decision-making processes will need advanced training in both technical aspects of decision science and in facilitation techniques, as well as opportunities to apprentice under decision analysts/consultants. Our primary goal should be an attempt to ingrain a discipline for applying clarity of thought to all decisions.

Integrated Data & Analysis in Support of Informed and Transparent Decision Making

NASA Astrophysics Data System (ADS)

Guivetchi, K.

2012-12-01

The California Water Plan includes a framework for improving water reliability, environmental stewardship, and economic stability through two initiatives - integrated regional water management to make better use of local water sources by integrating multiple aspects of managing water and related resources; and maintaining and improving statewide water management systems. The Water Plan promotes ways to develop a common approach for data standards and for understanding, evaluating, and improving regional and statewide water management systems, and for common ways to evaluate and select from alternative management strategies and projects. The California Water Plan acknowledges that planning for the future is uncertain and that change will continue to occur. It is not possible to know for certain how population growth, land use decisions, water demand patterns, environmental conditions, the climate, and many other factors that affect water use and supply may change by 2050. To anticipate change, our approach to water management and planning for the future needs to consider and quantify uncertainty, risk, and sustainability. There is a critical need for information sharing and information management to support over-arching and long-term water policy decisions that cross-cut multiple programs across many organizations and provide a common and transparent understanding of water problems and solutions. Achieving integrated water management with multiple benefits requires a transparent description of dynamic linkages between water supply, flood management, water quality, land use, environmental water, and many other factors. Water Plan Update 2013 will include an analytical roadmap for improving data, analytical tools, and decision-support to advance integrated water management at statewide and regional scales. It will include recommendations for linking collaborative processes with technical enhancements, providing effective analytical tools, and improving and sharing data and information. Specifically, this includes achieving better integration and consistency with other planning activities; obtaining consensus on quantitative deliverables; building a common conceptual understanding of the water management system; developing common schematics of the water management system; establishing modeling protocols and standards; and improving transparency and exchange of Water Plan information.

Enabling Data-Driven Methodologies Across the Data Lifecycle and Ecosystem

NASA Astrophysics Data System (ADS)

Doyle, R. J.; Crichton, D.

2017-12-01

NASA has unlocked unprecedented scientific knowledge through exploration of the Earth, our solar system, and the larger universe. NASA is generating enormous amounts of data that are challenging traditional approaches to capturing, managing, analyzing and ultimately gaining scientific understanding from science data. New architectures, capabilities and methodologies are needed to span the entire observing system, from spacecraft to archive, while integrating data-driven discovery and analytic capabilities. NASA data have a definable lifecycle, from remote collection point to validated accessibility in multiple archives. Data challenges must be addressed across this lifecycle, to capture opportunities and avoid decisions that may limit or compromise what is achievable once data arrives at the archive. Data triage may be necessary when the collection capacity of the sensor or instrument overwhelms data transport or storage capacity. By migrating computational and analytic capability to the point of data collection, informed decisions can be made about which data to keep; in some cases, to close observational decision loops onboard, to enable attending to unexpected or transient phenomena. Along a different dimension than the data lifecycle, scientists and other end-users must work across an increasingly complex data ecosystem, where the range of relevant data is rarely owned by a single institution. To operate effectively, scalable data architectures and community-owned information models become essential. NASA's Planetary Data System is having success with this approach. Finally, there is the difficult challenge of reproducibility and trust. While data provenance techniques will be part of the solution, future interactive analytics environments must support an ability to provide a basis for a result: relevant data source and algorithms, uncertainty tracking, etc., to assure scientific integrity and to enable confident decision making. Advances in data science offer opportunities to gain new insights from space missions and their vast data collections. We are working to innovate new architectures, exploit emerging technologies, develop new data-driven methodologies, and transfer them across disciplines, while working across the dual dimensions of the data lifecycle and the data ecosystem.

Assessing precision, bias and sigma-metrics of 53 measurands of the Alinity ci system.

PubMed

Westgard, Sten; Petrides, Victoria; Schneider, Sharon; Berman, Marvin; Herzogenrath, Jörg; Orzechowski, Anthony

2017-12-01

Assay performance is dependent on the accuracy and precision of a given method. These attributes can be combined into an analytical Sigma-metric, providing a simple value for laboratorians to use in evaluating a test method's capability to meet its analytical quality requirements. Sigma-metrics were determined for 37 clinical chemistry assays, 13 immunoassays, and 3 ICT methods on the Alinity ci system. Analytical Performance Specifications were defined for the assays, following a rationale of using CLIA goals first, then Ricos Desirable goals when CLIA did not regulate the method, and then other sources if the Ricos Desirable goal was unrealistic. A precision study was conducted at Abbott on each assay using the Alinity ci system following the CLSI EP05-A2 protocol. Bias was estimated following the CLSI EP09-A3 protocol using samples with concentrations spanning the assay's measuring interval tested in duplicate on the Alinity ci system and ARCHITECT c8000 and i2000 SR systems, where testing was also performed at Abbott. Using the regression model, the %bias was estimated at an important medical decisions point. Then the Sigma-metric was estimated for each assay and was plotted on a method decision chart. The Sigma-metric was calculated using the equation: Sigma-metric=(%TEa-|%bias|)/%CV. The Sigma-metrics and Normalized Method Decision charts demonstrate that a majority of the Alinity assays perform at least at five Sigma or higher, at or near critical medical decision levels. More than 90% of the assays performed at Five and Six Sigma. None performed below Three Sigma. Sigma-metrics plotted on Normalized Method Decision charts provide useful evaluations of performance. The majority of Alinity ci system assays had sigma values >5 and thus laboratories can expect excellent or world class performance. Laboratorians can use these tools as aids in choosing high-quality products, further contributing to the delivery of excellent quality healthcare for patients. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Decision Making in Action: Applying Research to Practice

NASA Technical Reports Server (NTRS)

Orasanu, Judith; Hart, Sandra G. (Technical Monitor)

1994-01-01

The importance of decision-making to safety in complex, dynamic environments like mission control centers, aviation, and offshore installations has been well established. NASA-ARC has a program of research dedicated to fostering safe and effective decision-making in the manned spaceflight environment: Because access to spaceflight is limited, environments with similar characteristics, including aviation and nuclear power plants, serve as analogs from which space-relevant data can be gathered and theories developed. Analyses of aviation accidents cite crew judgement and decision making as causes or contributing factors in over half of all accidents. Yet laboratory research on decision making has not proven especially helpful in improving the quality of decisions in these kinds of environments. One reason is that the traditional, analytic decision models are inappropriate to multi-dimensional, high-risk environments, and do not accurately describe what expert human decision makers do when they make decisions that have consequences. A new model of dynamic, naturalistic decision making is offered that may prove useful for improving decision making in complex, isolated, confined and high-risk environments. Based on analyses of crew performance in full-mission simulators and accident reports, features that define effective decision strategies in abnormal or emergency situations have been identified. These include accurate situation assessment (including time and risk assessment), appreciation of the complexity of the problem, sensitivity to constraints on the decision, timeliness of the response, and use of adequate information. More effective crews also manage their workload to provide themselves with time and resources to make good good decisions are appropriate to the demands of the situation. Effective crew decision making and overall performance are mediated by crew communication. Communication contributes to performance because it assures that all crew members have essential information, but it also regulates and coordinates crew actions and is the medium of collective thinking in response to a problem. This presentation will examine the relations between leadership, communication, decision making and overall crew performance. Implications of these findings for training will be discussed.

Land-use evaluation for sustainable construction in a protected area: A case of Sara mountain national park.

PubMed

Ristić, Vladica; Maksin, Marija; Nenković-Riznić, Marina; Basarić, Jelena

2018-01-15

The process of making decisions on sustainable development and construction begins in spatial and urban planning when defining the suitability of using land for sustainable construction in a protected area (PA) and its immediate and regional surroundings. The aim of this research is to propose and assess a model for evaluating land-use suitability for sustainable construction in a PA and its surroundings. The methodological approach of Multi-Criteria Decision Analysis was used in the formation of this model and adapted for the research; it was combined with the adapted Analytical hierarchy process and the Delphi process, and supported by a geographical information system (GIS) within the framework of ESRI ArcGIS software - Spatial analyst. The model is applied to the case study of Sara mountain National Park in Kosovo. The result of the model is a "map of integrated assessment of land-use suitability for sustainable construction in a PA for the natural factor". Copyright © 2017 Elsevier Ltd. All rights reserved.

Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

PubMed Central

Flanagan, Eleni; Siddiqui, Sauleh; Appelbaum, Jeff; Kasper, Edward K; Levin, Scott

2016-01-01

Objective To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs). Methods The forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients. Results The forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory. Discussion The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients’ risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature. Conclusion Data-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support. PMID:26342217

Technology Enhanced Analytics (TEA) in Higher Education

ERIC Educational Resources Information Center

Daniel, Ben Kei; Butson, Russell

2013-01-01

This paper examines the role of Big Data Analytics in addressing contemporary challenges associated with current changes in institutions of higher education. The paper first explores the potential of Big Data Analytics to support instructors, students and policy analysts to make better evidence based decisions. Secondly, the paper presents an…

Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network

PubMed Central

Abdullah, Radhwan Mohamed; Zukarnain, Zuriati Ahmad

2017-01-01

Transferring a huge amount of data between different network locations over the network links depends on the network’s traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model. PMID:28708067

Research implications of science-informed, value-based decision making.

PubMed

Dowie, Jack

2004-01-01

In 'Hard' science, scientists correctly operate as the 'guardians of certainty', using hypothesis testing formulations and value judgements about error rates and time discounting that make classical inferential methods appropriate. But these methods can neither generate most of the inputs needed by decision makers in their time frame, nor generate them in a form that allows them to be integrated into the decision in an analytically coherent and transparent way. The need for transparent accountability in public decision making under uncertainty and value conflict means the analytical coherence provided by the stochastic Bayesian decision analytic approach, drawing on the outputs of Bayesian science, is needed. If scientific researchers are to play the role they should be playing in informing value-based decision making, they need to see themselves also as 'guardians of uncertainty', ensuring that the best possible current posterior distributions on relevant parameters are made available for decision making, irrespective of the state of the certainty-seeking research. The paper distinguishes the actors employing different technologies in terms of the focus of the technology (knowledge, values, choice); the 'home base' mode of their activity on the cognitive continuum of varying analysis-to-intuition ratios; and the underlying value judgements of the activity (especially error loss functions and time discount rates). Those who propose any principle of decision making other than the banal 'Best Principle', including the 'Precautionary Principle', are properly interpreted as advocates seeking to have their own value judgements and preferences regarding mode location apply. The task for accountable decision makers, and their supporting technologists, is to determine the best course of action under the universal conditions of uncertainty and value difference/conflict.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.

PubMed

Taylor, R Andrew; Pare, Joseph R; Venkatesh, Arjun K; Mowafi, Hani; Melnick, Edward R; Fleischman, William; Hall, M Kennedy

2016-03-01

Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. © 2015 by the Society for Academic Emergency Medicine.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

PubMed Central

Taylor, R. Andrew; Pare, Joseph R.; Venkatesh, Arjun K.; Mowafi, Hani; Melnick, Edward R.; Fleischman, William; Hall, M. Kennedy

2018-01-01

Objectives Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data–driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. Methods This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics. Results There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons). Conclusions In this proof-of-concept study, a local big data–driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions. PMID:26679719

Using Predictive Analytics to Predict Power Outages from Severe Weather

NASA Astrophysics Data System (ADS)

Wanik, D. W.; Anagnostou, E. N.; Hartman, B.; Frediani, M. E.; Astitha, M.

2015-12-01

The distribution of reliable power is essential to businesses, public services, and our daily lives. With the growing abundance of data being collected and created by industry (i.e. outage data), government agencies (i.e. land cover), and academia (i.e. weather forecasts), we can begin to tackle problems that previously seemed too complex to solve. In this session, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. We will show a performance evaluation of outage predictive models built for Eversource Energy (formerly Connecticut Light & Power) for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to >15,000 outages). High resolution weather simulations (simulated with the Weather and Research Forecast Model) were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes.

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management.

PubMed

Oddo, Perry C; Lee, Ben S; Garner, Gregory G; Srikrishnan, Vivek; Reed, Patrick M; Forest, Chris E; Keller, Klaus

2017-09-05

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies. © 2017 Society for Risk Analysis.

Fertility treatment in women with polycystic ovary syndrome: a decision analysis of different oral ovulation induction agents

PubMed Central

Jungheim, Emily S.; Odibo, Anthony O.

2010-01-01

Study objective To compare different oral ovulation induction agents in treating infertile women with polycystic ovary syndrome Design Decision-analytic model comparing three treatment strategies using probability estimates derived from literature review and sensitivity analyses performed on the baseline assumptions Setting Outpatient reproductive medicine and gynecology practices Patients Infertile women with polycystic ovary syndrome Interventions Metformin, clomiphene citrate, or metformin with clomiphene citrate Main Outcome Measures Live birth Results Within the baseline assumptions, combination therapy with metformin and clomiphene citrate was the preferred therapy for achieving live birth in women with polycystic ovary syndrome. Sensitivity analysis revealed the model to be robust over a wide range of probabilities. Conclusions Combination therapy with metformin and clomiphene citrate should be considered as first-line treatment for infertile women with polycystic ovary syndrome PMID:20451181

Information analytics for healthcare service discovery.

PubMed

Sun, Lily; Yamin, Mohammad; Mushi, Cleopa; Liu, Kecheng; Alsaigh, Mohammed; Chen, Fabian

2014-01-01

The concept of being 'patient-centric' is a challenge to many existing healthcare service provision practices. This paper focuses on the issue of referrals, where multiple stakeholders, such as General Practitioners (GPs) and patients, are encouraged to make a consensual decision based on patients' needs. In this paper, we present an ontology-enabled healthcare service provision, which facilitates both patients and GPs in jointly deciding upon the referral decision. In the healthcare service provision model, we define three types of profiles which represent different stakeholders' requirements. This model also comprises a set of healthcare service discovery processes: articulating a service need, matching the need with the healthcare service offerings, and deciding on a best-fit service for acceptance. As a result, the healthcare service provision can carry out coherent analysis using personalised information and iterative processes that deal with requirements which change over time.

Testing a 1-D Analytical Salt Intrusion Model and the Predictive Equation in Malaysian Estuaries

NASA Astrophysics Data System (ADS)

Gisen, Jacqueline Isabella; Savenije, Hubert H. G.

2013-04-01

Little is known about the salt intrusion behaviour in Malaysian estuaries. Study on this topic sometimes requires large amounts of data especially if a 2-D or 3-D numerical models are used for analysis. In poor data environments, 1-D analytical models are more appropriate. For this reason, a fully analytical 1-D salt intrusion model, based on the theory of Savenije in 2005, was tested in three Malaysian estuaries (Bernam, Selangor and Muar) because it is simple and requires minimal data. In order to achieve that, site surveys were conducted in these estuaries during the dry season (June-August) at spring tide by moving boat technique. Data of cross-sections, water levels and salinity were collected, and then analysed with the salt intrusion model. This paper demonstrates a good fit between the simulated and observed salinity distribution for all three estuaries. Additionally, the calibrated Van der Burgh's coefficient K, Dispersion coefficient D0, and salt intrusion length L, for the estuaries also displayed a reasonable correlations with those calculated from the predictive equations. This indicates that not only is the salt intrusion model valid for the case studies in Malaysia but also the predictive model. Furthermore, the results from this study describe the current state of the estuaries with which the Malaysian water authority in Malaysia can make decisions on limiting water abstraction or dredging. Keywords: salt intrusion, Malaysian estuaries, discharge, predictive model, dispersion

Multicriteria decision analysis in ranking of analytical procedures for aldrin determination in water.

PubMed

Tobiszewski, Marek; Orłowski, Aleksander

2015-03-27

The study presents the possibility of multi-criteria decision analysis (MCDA) application when choosing analytical procedures with low environmental impact. A type of MCDA, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), was chosen as versatile tool that meets all the analytical chemists--decision makers requirements. Twenty five analytical procedures for aldrin determination in water samples (as an example) were selected as input alternatives to MCDA analysis. Nine different criteria describing the alternatives were chosen from different groups--metrological, economical and the most importantly--environmental impact. The weights for each criterion were obtained from questionnaires that were sent to experts, giving three different scenarios for MCDA results. The results of analysis show that PROMETHEE is very promising tool to choose the analytical procedure with respect to its greenness. The rankings for all three scenarios placed solid phase microextraction and liquid phase microextraction--based procedures high, while liquid-liquid extraction, solid phase extraction and stir bar sorptive extraction--based procedures were placed low in the ranking. The results show that although some of the experts do not intentionally choose green analytical chemistry procedures, their MCDA choice is in accordance with green chemistry principles. The PROMETHEE ranking results were compared with more widely accepted green analytical chemistry tools--NEMI and Eco-Scale. As PROMETHEE involved more different factors than NEMI, the assessment results were only weakly correlated. Oppositely, the results of Eco-Scale assessment were well-correlated as both methodologies involved similar criteria of assessment. Copyright © 2015 Elsevier B.V. All rights reserved.

Exploring the Integration of COSYSMO with a Model-Based Systems Engineering Methodology in Early Trade Space Analytics and Decisions

DTIC Science & Technology

2016-06-01

UAV system but had a larger scope. The publishing timeline for the second UAV model and this thesis ...r l o g i c a l ) t h a t can be used t o d e s c r i b e a p a r t o f t h e model . < / d e s c r i p t i o n > 7 <h idden> f a l s e < / h idden...July). Program manager e -tool kit: Hierarchy of models and simulations . [Online]. Available: https://acc.dau.mil/CommunityBrowser.aspx?id= 294530

Updating the Finite Element Model of the Aerostructures Test Wing Using Ground Vibration Test Data

NASA Technical Reports Server (NTRS)

Lung, Shun-Fat; Pak, Chan-Gi

2009-01-01

Improved and/or accelerated decision making is a crucial step during flutter certification processes. Unfortunately, most finite element structural dynamics models have uncertainties associated with model validity. Tuning the finite element model using measured data to minimize the model uncertainties is a challenging task in the area of structural dynamics. The model tuning process requires not only satisfactory correlations between analytical and experimental results, but also the retention of the mass and stiffness properties of the structures. Minimizing the difference between analytical and experimental results is a type of optimization problem. By utilizing the multidisciplinary design, analysis, and optimization (MDAO) tool in order to optimize the objective function and constraints; the mass properties, the natural frequencies, and the mode shapes can be matched to the target data to retain the mass matrix orthogonality. This approach has been applied to minimize the model uncertainties for the structural dynamics model of the aerostructures test wing (ATW), which was designed and tested at the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California). This study has shown that natural frequencies and corresponding mode shapes from the updated finite element model have excellent agreement with corresponding measured data.

Updating the Finite Element Model of the Aerostructures Test Wing using Ground Vibration Test Data

NASA Technical Reports Server (NTRS)

Lung, Shun-fat; Pak, Chan-gi

2009-01-01

Improved and/or accelerated decision making is a crucial step during flutter certification processes. Unfortunately, most finite element structural dynamics models have uncertainties associated with model validity. Tuning the finite element model using measured data to minimize the model uncertainties is a challenging task in the area of structural dynamics. The model tuning process requires not only satisfactory correlations between analytical and experimental results, but also the retention of the mass and stiffness properties of the structures. Minimizing the difference between analytical and experimental results is a type of optimization problem. By utilizing the multidisciplinary design, analysis, and optimization (MDAO) tool in order to optimize the objective function and constraints; the mass properties, the natural frequencies, and the mode shapes can be matched to the target data to retain the mass matrix orthogonality. This approach has been applied to minimize the model uncertainties for the structural dynamics model of the Aerostructures Test Wing (ATW), which was designed and tested at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center (DFRC) (Edwards, California). This study has shown that natural frequencies and corresponding mode shapes from the updated finite element model have excellent agreement with corresponding measured data.

A Semi-Analytical Method for Rapid Estimation of Near-Well Saturation, Temperature, Pressure and Stress in Non-Isothermal CO2 Injection

NASA Astrophysics Data System (ADS)

LaForce, T.; Ennis-King, J.; Paterson, L.

2015-12-01

Reservoir cooling near the wellbore is expected when fluids are injected into a reservoir or aquifer in CO2 storage, enhanced oil or gas recovery, enhanced geothermal systems, and water injection for disposal. Ignoring thermal effects near the well can lead to under-prediction of changes in reservoir pressure and stress due to competition between increased pressure and contraction of the rock in the cooled near-well region. In this work a previously developed semi-analytical model for immiscible, nonisothermal fluid injection is generalised to include partitioning of components between two phases. Advection-dominated radial flow is assumed so that the coupled two-phase flow and thermal conservation laws can be solved analytically. The temperature and saturation profiles are used to find the increase in reservoir pressure, tangential, and radial stress near the wellbore in a semi-analytical, forward-coupled model. Saturation, temperature, pressure, and stress profiles are found for parameters representative of several CO2 storage demonstration projects around the world. General results on maximum injection rates vs depth for common reservoir parameters are also presented. Prior to drilling an injection well there is often little information about the properties that will determine the injection rate that can be achieved without exceeding fracture pressure, yet injection rate and pressure are key parameters in well design and placement decisions. Analytical solutions to simplified models such as these can quickly provide order of magnitude estimates for flow and stress near the well based on a range of likely parameters.

Leasing vs. owning a medical office: an analytical model.

PubMed

Tolbert, Samuel H; Wood, Carol P

2007-01-01

Physicians often face a major financial dilemma: To lease or own their medical office. This article takes a set of typical assumptions for a real estate market and analyzes the capital costs, cash flow, and investment implications of the option of leasing a medical office versus owning a similar property. The paper analyzes the financial aspects of each option and the impact on net physician income and potential return-on-investment. A model for analysis is presented that can be used by practitioners who advise physicians in such decision-making.

THE FUTURE OF SUSTAINABLE MANAGEMENT APPROACHES AND REVITALIZATION TOOLS-ELECTRONIC (SMARTE): 2006-2010

EPA Science Inventory

SMARTe is being developed to give stakeholders information resources, analytical tools, communication strategies, and a decision analysis approach to be able to make better decisions regarding future uses of property. The development of the communication tools and decision analys...

Multi-criteria decision analysis for waste management in Saharawi refugee camps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garfi, M.; Tondelli, S.; Bonoli, A.

2009-10-15

The aim of this paper is to compare different waste management solutions in Saharawi refugee camps (Algeria) and to test the feasibility of a decision-making method developed to be applied in particular conditions in which environmental and social aspects must be considered. It is based on multi criteria analysis, and in particular on the analytic hierarchy process (AHP), a mathematical technique for multi-criteria decision making (Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York, USA; Saaty, T.L., 1990. How to Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research; Saaty, T.L., 1994. Decision Making for Leaders:more » The Analytic Hierarchy Process in a Complex World. RWS Publications, Pittsburgh, PA), and on participatory approach, focusing on local community's concerns. The research compares four different waste collection and management alternatives: waste collection by using three tipper trucks, disposal and burning in an open area; waste collection by using seven dumpers and disposal in a landfill; waste collection by using seven dumpers and three tipper trucks and disposal in a landfill; waste collection by using three tipper trucks and disposal in a landfill. The results show that the second and the third solutions provide better scenarios for waste management. Furthermore, the discussion of the results points out the multidisciplinarity of the approach, and the equilibrium between social, environmental and technical impacts. This is a very important aspect in a humanitarian and environmental project, confirming the appropriateness of the chosen method.« less

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

PubMed Central

Kessler, R.C.; van Loo, H.M.; Wardenaar, K.J.; Bossarte, R.M.; Brenner, L.A.; Ebert, D.D; de Jonge, P.; Nierenberg, A.A.; Rosellini, A.J.; Sampson, N.A.; Schoevers, R.A.; Wilcox, M.A.; Zaslavsky, A.M.

2016-01-01

Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists. PMID:26810628

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

PubMed

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Ebert, D D; de Jonge, P; Nierenberg, A A; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2017-02-01

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

NASA program decisions using reliability analysis.

NASA Technical Reports Server (NTRS)

Steinberg, A.

1972-01-01

NASA made use of the analytical outputs of reliability people to make management decisions on the Apollo program. Such decisions affected the amount of the incentive fees, how much acceptance testing was necessary, how to optimize development testing, whether to approve engineering changes, and certification of flight readiness. Examples of such analysis are discussed and related to programmatic decisions.-

Value of innovation for hematologic malignancies.

PubMed

Monia, Marchetti

2016-01-01

Several novel drugs are dramatically improving both lifespan and quality-of-life of patients with blood cancers. Prolonged disease duration and increased treatment costs for hematologic malignancies impose a relevant economic burden onto healthcare services, despite the low incidence of blood cancers. Therefore, an appropriate paradigm for valuing 'innovation' is urgently required in order to refine pricing and reimbursement decisions. Cost-per-QALY-gained is still the standard metric for assessing the 'incremental' value of new drugs; however, the high number of 'comparator' therapies and the huge variety of treatment sequences make plain two-treatment comparisons sub-optimal, while multiple-treatment and multiple-sequence comparisons require complex and less-transparent decision models. A repository of standard backbones for decision models might allow benchmarking and comparability among cost-effectiveness analyses; however, an international effort is required to build it up. Deontology recommends that hematologists act in optimizing healthcare resources while preserving patient-physician alliance, but clinical practice guidelines do not support doctors in balancing cost against clinical outcomes. Decision models of chronic blood cancers unexpectedly proved that cost might be an appropriate value for innovation if treatments avoided severe toxicity and further lines of treatments, despite the eventually long duration of treatment and the competing risk of death due to comorbidity and old age. The improved transparency of decision models allows sharing of relevant structural and analytic parameters (i.e., time horizon, comparator treatments, hierarchy of end-point, assumptions, source of data, sub-group analyses) by stakeholders, physicians and patients, making health economics a noble 'translator' of values for innovation.

Analytics4Action Evaluation Framework: A Review of Evidence-Based Learning Analytics Interventions at the Open University UK

ERIC Educational Resources Information Center

Rienties, Bart; Boroowa, Avinash; Cross, Simon; Kubiak, Chris; Mayles, Kevin; Murphy, Sam

2016-01-01

There is an urgent need to develop an evidence-based framework for learning analytics whereby stakeholders can manage, evaluate, and make decisions about which types of interventions work well and under which conditions. In this article, we will work towards developing a foundation of an Analytics4Action Evaluation Framework (A4AEF) that is…

Multi-analytical Approaches Informing the Risk of Sepsis

NASA Astrophysics Data System (ADS)

Gwadry-Sridhar, Femida; Lewden, Benoit; Mequanint, Selam; Bauer, Michael

Sepsis is a significant cause of mortality and morbidity and is often associated with increased hospital resource utilization, prolonged intensive care unit (ICU) and hospital stay. The economic burden associated with sepsis is huge. With advances in medicine, there are now aggressive goal oriented treatments that can be used to help these patients. If we were able to predict which patients may be at risk for sepsis we could start treatment early and potentially reduce the risk of mortality and morbidity. Analytic methods currently used in clinical research to determine the risk of a patient developing sepsis may be further enhanced by using multi-modal analytic methods that together could be used to provide greater precision. Researchers commonly use univariate and multivariate regressions to develop predictive models. We hypothesized that such models could be enhanced by using multiple analytic methods that together could be used to provide greater insight. In this paper, we analyze data about patients with and without sepsis using a decision tree approach and a cluster analysis approach. A comparison with a regression approach shows strong similarity among variables identified, though not an exact match. We compare the variables identified by the different approaches and draw conclusions about the respective predictive capabilities,while considering their clinical significance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szoka de Valladares, M.R.; Mack, S.

The DOE Hydrogen Program needs to develop criteria as part of a systematic evaluation process for proposal identification, evaluation and selection. The H Scan component of this process provides a framework in which a project proposer can fully describe their candidate technology system and its components. The H Scan complements traditional methods of capturing cost and technical information. It consists of a special set of survey forms designed to elicit information so expert reviewers can assess the proposal relative to DOE specified selection criteria. The Analytic Hierarchy Process (AHP) component of the decision process assembles the management defined evaluation andmore » selection criteria into a coherent multi-level decision construct by which projects can be evaluated in pair-wise comparisons. The AHP model will reflect management`s objectives and it will assist in the ranking of individual projects based on the extent to which each contributes to management`s objectives. This paper contains a detailed description of the products and activities associated with the planning and evaluation process: The objectives or criteria; the H Scan; and The Analytic Hierarchy Process (AHP).« less

Structured decision making as a framework for large-scale wildlife harvest management decisions

USGS Publications Warehouse

Robinson, Kelly F.; Fuller, Angela K.; Hurst, Jeremy E.; Swift, Bryan L.; Kirsch, Arthur; Farquhar, James F.; Decker, Daniel J.; Siemer, William F.

2016-01-01

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer (Odocoileus virginianus) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

Taming Data to Make Decisions: Using a Spatial Fuzzy Logic Decision Support Framework to Inform Conservation and Land Use Planning

NASA Astrophysics Data System (ADS)

Sheehan, T.; Baker, B.; Degagne, R. S.

2015-12-01

With the abundance of data sources, analytical methods, and computer models, land managers are faced with the overwhelming task of making sense of a profusion of data of wildly different types. Luckily, fuzzy logic provides a method to work with different types of data using language-based propositions such as "the landscape is undisturbed," and a simple set of logic constructs. Just as many surveys allow different levels of agreement with a proposition, fuzzy logic allows values reflecting different levels of truth for a proposition. Truth levels fall within a continuum ranging from Fully True to Fully False. Hence a fuzzy logic model produces continuous results. The Environmental Evaluation Modeling System (EEMS) is a platform-independent, tree-based, fuzzy logic modeling framework. An EEMS model provides a transparent definition of an evaluation model and is commonly developed as a collaborative effort among managers, scientists, and GIS experts. Managers specify a set of evaluative propositions used to characterize the landscape. Scientists, working with managers, formulate functions that convert raw data values into truth values for the propositions and produce a logic tree to combine results into a single metric used to guide decisions. Managers, scientists, and GIS experts then work together to implement and iteratively tune the logic model and produce final results. We present examples of two successful EEMS projects that provided managers with map-based results suitable for guiding decisions: sensitivity and climate change exposure in Utah and the Colorado Plateau modeled for the Bureau of Land Management; and terrestrial ecological intactness in the Mojave and Sonoran region of southern California modeled for the Desert Renewable Energy Conservation Plan.

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

USGS Publications Warehouse

Johnson, Fred A.; Williams, Byron K.; Nichols, James D.

2013-01-01

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Analytic network process model for sustainable lean and green manufacturing performance indicator

NASA Astrophysics Data System (ADS)

Aminuddin, Adam Shariff Adli; Nawawi, Mohd Kamal Mohd; Mohamed, Nik Mohd Zuki Nik

2014-09-01

Sustainable manufacturing is regarded as the most complex manufacturing paradigm to date as it holds the widest scope of requirements. In addition, its three major pillars of economic, environment and society though distinct, have some overlapping among each of its elements. Even though the concept of sustainability is not new, the development of the performance indicator still needs a lot of improvement due to its multifaceted nature, which requires integrated approach to solve the problem. This paper proposed the best combination of criteria en route a robust sustainable manufacturing performance indicator formation via Analytic Network Process (ANP). The integrated lean, green and sustainable ANP model can be used to comprehend the complex decision system of the sustainability assessment. The finding shows that green manufacturing is more sustainable than lean manufacturing. It also illustrates that procurement practice is the most important criteria in the sustainable manufacturing performance indicator.

The Hamming distance in the minority game

NASA Astrophysics Data System (ADS)

D'hulst, R.; Rodgers, G. J.

1999-08-01

We investigate different versions of the minority game, a toy model for agents buying and selling a commodity. The Hamming distance between the strategies used by agents to make decisions is introduced as an analytical tool to determine several properties of these models. The success rate of the agents in an adaptive version of the game is compared with the rate from a stochastic version. It is shown numerically and analytically that the adaptive process is inefficient, increasing the success rate of the unused strategies while decreasing the success rate of the strategies used by the agents. The agents do not do as well as if they were forced to use only one strategy permanently. A version of the game in which the agents strategies evolve is also analysed using the notion of distance. The agents evolve into a state in which they are all using one strategy, which is again the state that yields the maximum success rate.

Theoretical aspects and modelling of cellular decision making, cell killing and information-processing in photodynamic therapy of cancer.

PubMed

Gkigkitzis, Ioannis

2013-01-01

The aim of this report is to provide a mathematical model of the mechanism for making binary fate decisions about cell death or survival, during and after Photodynamic Therapy (PDT) treatment, and to supply the logical design for this decision mechanism as an application of rate distortion theory to the biochemical processing of information by the physical system of a cell. Based on system biology models of the molecular interactions involved in the PDT processes previously established, and regarding a cellular decision-making system as a noisy communication channel, we use rate distortion theory to design a time dependent Blahut-Arimoto algorithm where the input is a stimulus vector composed of the time dependent concentrations of three PDT related cell death signaling molecules and the output is a cell fate decision. The molecular concentrations are determined by a group of rate equations. The basic steps are: initialize the probability of the cell fate decision, compute the conditional probability distribution that minimizes the mutual information between input and output, compute the cell probability of cell fate decision that minimizes the mutual information and repeat the last two steps until the probabilities converge. Advance to the next discrete time point and repeat the process. Based on the model from communication theory described in this work, and assuming that the activation of the death signal processing occurs when any of the molecular stimulants increases higher than a predefined threshold (50% of the maximum concentrations), for 1800s of treatment, the cell undergoes necrosis within the first 30 minutes with probability range 90.0%-99.99% and in the case of repair/survival, it goes through apoptosis within 3-4 hours with probability range 90.00%-99.00%. Although, there is no experimental validation of the model at this moment, it reproduces some patterns of survival ratios of predicted experimental data. Analytical modeling based on cell death signaling molecules has been shown to be an independent and useful tool for prediction of cell surviving response to PDT. The model can be adjusted to provide important insights for cellular response to other treatments such as hyperthermia, and diseases such as neurodegeneration.

Hierarchical analysis of bridge decision makers : the role of new technology adoption in the timber bridge market : special project fiscal year 1992

DOT National Transportation Integrated Search

1995-08-01

Bridge design engineers and local highway officials make bridge replacement decisions across the : United States. The Analytical Hierarchy Process was used to characterize the bridge material selection : decision of these individuals. State Departmen...

Towards Secure and Trustworthy Cyberspace: Social Media Analytics on Hacker Communities

ERIC Educational Resources Information Center

Li, Weifeng

2017-01-01

Social media analytics is a critical research area spawned by the increasing availability of rich and abundant online user-generated content. So far, social media analytics has had a profound impact on organizational decision making in many aspects, including product and service design, market segmentation, customer relationship management, and…

Of mental models, assumptions and heuristics: The case of acids and acid strength

NASA Astrophysics Data System (ADS)

McClary, Lakeisha Michelle

This study explored what cognitive resources (i.e., units of knowledge necessary to learn) first-semester organic chemistry students used to make decisions about acid strength and how those resources guided the prediction, explanation and justification of trends in acid strength. We were specifically interested in the identifying and characterizing the mental models, assumptions and heuristics that students relied upon to make their decisions, in most cases under time constraints. The views about acids and acid strength were investigated for twenty undergraduate students. Data sources for this study included written responses and individual interviews. The data was analyzed using a qualitative methodology to answer five research questions. Data analysis regarding these research questions was based on existing theoretical frameworks: problem representation (Chi, Feltovich & Glaser, 1981), mental models (Johnson-Laird, 1983); intuitive assumptions (Talanquer, 2006), and heuristics (Evans, 2008). These frameworks were combined to develop the framework from which our data were analyzed. Results indicated that first-semester organic chemistry students' use of cognitive resources was complex and dependent on their understanding of the behavior of acids. Expressed mental models were generated using prior knowledge and assumptions about acids and acid strength; these models were then employed to make decisions. Explicit and implicit features of the compounds in each task mediated participants' attention, which triggered the use of a very limited number of heuristics, or shortcut reasoning strategies. Many students, however, were able to apply more effortful analytic reasoning, though correct trends were predicted infrequently. Most students continued to use their mental models, assumptions and heuristics to explain a given trend in acid strength and to justify their predicted trends, but the tasks influenced a few students to shift from one model to another model. An emergent finding from this project was that the problem representation greatly influenced students' ability to make correct predictions in acid strength. Many students, however, were able to apply more effortful analytic reasoning, though correct trends were predicted infrequently. Most students continued to use their mental models, assumptions and heuristics to explain a given trend in acid strength and to justify their predicted trends, but the tasks influenced a few students to shift from one model to another model. An emergent finding from this project was that the problem representation greatly influenced students' ability to make correct predictions in acid strength.

An integrative model for in-silico clinical-genomics discovery science.

PubMed

Lussier, Yves A; Sarkar, Indra Nell; Cantor, Michael

2002-01-01

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

Does probability guided hysteroscopy reduce costs in women investigated for postmenopausal bleeding?

PubMed

Breijer, M C; van Hanegem, N; Visser, N C M; Verheijen, R H M; Mol, B W J; Pijnenborg, J M A; Opmeer, B C; Timmermans, A

2015-01-01

To evaluate whether a model to predict a failed endometrial biopsy in women with postmenopausal bleeding (PMB) and a thickened endometrium can reduce costs without compromising diagnostic accuracy. Model based cost-minimization analysis. A decision analytic model was designed to compare two diagnostic strategies for women with PMB: (I) attempting office endometrial biopsy and performing outpatient hysteroscopy after failed biopsy and (II) predicted probability of a failed endometrial biopsy based on patient characteristics to guide the decision for endometrial biopsy or immediate hysteroscopy. Robustness of assumptions regarding costs was evaluated in sensitivity analyses. Costs for the different strategies. At different cut-offs for the predicted probability of failure of an endometrial biopsy, strategy I was generally less expensive than strategy II. The costs for strategy I were always € 460; the costs for strategy II varied between € 457 and € 475. At a 65% cut-off, a possible saving of € 3 per woman could be achieved. Individualizing the decision to perform an endometrial biopsy or immediate hysteroscopy in women presenting with postmenopausal bleeding based on patient characteristics does not increase the efficiency of the diagnostic work-up.

Economics of influenza vaccine administration timing for children.

PubMed

Lee, Bruce Y; Tai, Julie H Y; Bailey, Rachel R; Smith, Kenneth J; Nowalk, Andrew J

2010-03-01

To determine how much should be invested each year to encourage and operationalize the administration of influenza vaccine to children before November and how late the vaccine should be offered each year. Monte Carlo decision analytic computer simulation models. The children's influenza vaccination timing model quantified the incremental economic value of vaccinating a child earlier in the influenza season and the incremental cost of delaying vaccination. The children's monthly influenza vaccination decision model evaluated the cost-effectiveness of vaccinating versus not vaccinating for every month of the influenza season. Getting children vaccinated by the end of October rather than when they are currently getting vaccinated could save society between $6.4 million and $9.2 million plus 653 and 926 quality-adjusted life-years (QALYs) and third-party payers between $4.1 million and $6.1 million plus 647 to 942 QALYs each year. Decision makers may want to continue offering influenza vaccination to children at least through the end of December. Vaccinating with trivalent inactivated virus vaccine was more cost-effective than vaccinating with live attenuated influenza vaccine for every month. Policymakers could invest up to $6 million to $9 million a year to get children vaccinated in September or October without expending any net costs.

Big data and visual analytics in anaesthesia and health care.

PubMed

Simpao, A F; Ahumada, L M; Rehman, M A

2015-09-01

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences. © The Author 2015. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Applying the Wildland Fire Decision Support System (WFDSS) to support risk-informed decision making: The Gold Pan Fire, Bitterroot National Forest, Montana, USA

Treesearch

Erin K. Noonan-Wright; Tonja S. Opperman

2015-01-01

In response to federal wildfire policy changes, risk-informed decision-making by way of improved decision support, is increasingly becoming a component of managing wildfires. As fire incidents escalate in size and complexity, the Wildland Fire Decision Support System (WFDSS) provides support with different analytical tools as fire conditions change. We demonstrate the...

The Global War on Terrorism: Analytical Support, Tools and Metrics of Assessment. MORS Workshop

DTIC Science & Technology

2005-08-11

is the matter of intelligence, as COL(P) Keller pointed out, we need to spend less time in the intelligence cycle on managing information and...models, decision aids: "named things " * Methodologies: potentially useful things "* Resources: databases, people, books? * Meta-data on tools * Develop a...experience. Only one member (Mr. Garry Greco) had served on the Joint Intelligence Task Force for Counter Terrorism. Although Gary heavily participated

Modeling rheumatoid arthritis using different techniques - a review of model construction and results.

PubMed

Scholz, Stefan; Mittendorf, Thomas

2014-12-01

Rheumatoid arthritis (RA) is a chronic, inflammatory disease with severe effects on the functional ability of patients. Due to the prevalence of 0.5 to 1.0 percent in western countries, new treatment options are a major concern for decision makers with regard to their budget impact. In this context, cost-effectiveness analyses are a helpful tool to evaluate new treatment options for reimbursement schemes. To analyze and compare decision analytic modeling techniques and to explore their use in RA with regard to their advantages and shortcomings. A systematic literature review was conducted in PubMED and 58 studies reporting health economics decision models were analyzed with regard to the modeling technique used. From the 58 reviewed publications, we found 13 reporting decision tree-analysis, 25 (cohort) Markov models, 13 publications on individual sampling methods (ISM) and seven discrete event simulations (DES). Thereby 26 studies were identified as presenting independently developed models and 32 models as adoptions. The modeling techniques used were found to differ in their complexity and in the number of treatment options compared. Methodological features are presented in the article and a comprehensive overview of the cost-effectiveness estimates is given in Additional files 1 and 2. When compared to the other modeling techniques, ISM and DES have advantages in the coverage of patient heterogeneity and, additionally, DES is capable to model more complex treatment sequences and competing risks in RA-patients. Nevertheless, the availability of sufficient data is necessary to avoid assumptions in ISM and DES exercises, thereby enabling biased results. Due to the different settings, time frames and interventions in the reviewed publications, no direct comparison of modeling techniques was applicable. The results from other indications suggest that incremental cost-effective ratios (ICERs) do not differ significantly between Markov and DES models, but DES is able to report more outcome parameters. Given a sufficient data supply, DES is the modeling technique of choice when modeling cost-effectiveness in RA. Otherwise transparency on the data inputs is crucial for valid results and to inform decision makers about possible biases. With regard to ICERs, Markov models might provide similar estimates as more advanced modeling techniques.

Validation of a DICE Simulation Against a Discrete Event Simulation Implemented Entirely in Code.

PubMed

Möller, Jörgen; Davis, Sarah; Stevenson, Matt; Caro, J Jaime

2017-10-01

Modeling is an essential tool for health technology assessment, and various techniques for conceptualizing and implementing such models have been described. Recently, a new method has been proposed-the discretely integrated condition event or DICE simulation-that enables frequently employed approaches to be specified using a common, simple structure that can be entirely contained and executed within widely available spreadsheet software. To assess if a DICE simulation provides equivalent results to an existing discrete event simulation, a comparison was undertaken. A model of osteoporosis and its management programmed entirely in Visual Basic for Applications and made public by the National Institute for Health and Care Excellence (NICE) Decision Support Unit was downloaded and used to guide construction of its DICE version in Microsoft Excel ® . The DICE model was then run using the same inputs and settings, and the results were compared. The DICE version produced results that are nearly identical to the original ones, with differences that would not affect the decision direction of the incremental cost-effectiveness ratios (<1% discrepancy), despite the stochastic nature of the models. The main limitation of the simple DICE version is its slow execution speed. DICE simulation did not alter the results and, thus, should provide a valid way to design and implement decision-analytic models without requiring specialized software or custom programming. Additional efforts need to be made to speed up execution.

Mobility Data Analytics Center.

DOT National Transportation Integrated Search

2016-01-01

Mobility Data Analytics Center aims at building a centralized data engine to efficiently manipulate : large-scale data for smart decision making. Integrating and learning the massive data are the key to : the data engine. The ultimate goal of underst...

Personality, Cognitive Style, Motivation, and Aptitude Predict Systematic Trends in Analytic Forecasting Behavior.

PubMed

Poore, Joshua C; Forlines, Clifton L; Miller, Sarah M; Regan, John R; Irvine, John M

2014-12-01

The decision sciences are increasingly challenged to advance methods for modeling analysts, accounting for both analytic strengths and weaknesses, to improve inferences taken from increasingly large and complex sources of data. We examine whether psychometric measures-personality, cognitive style, motivated cognition-predict analytic performance and whether psychometric measures are competitive with aptitude measures (i.e., SAT scores) as analyst sample selection criteria. A heterogeneous, national sample of 927 participants completed an extensive battery of psychometric measures and aptitude tests and was asked 129 geopolitical forecasting questions over the course of 1 year. Factor analysis reveals four dimensions among psychometric measures; dimensions characterized by differently motivated "top-down" cognitive styles predicted distinctive patterns in aptitude and forecasting behavior. These dimensions were not better predictors of forecasting accuracy than aptitude measures. However, multiple regression and mediation analysis reveals that these dimensions influenced forecasting accuracy primarily through bias in forecasting confidence. We also found that these facets were competitive with aptitude tests as forecast sampling criteria designed to mitigate biases in forecasting confidence while maximizing accuracy. These findings inform the understanding of individual difference dimensions at the intersection of analytic aptitude and demonstrate that they wield predictive power in applied, analytic domains.

Personality, Cognitive Style, Motivation, and Aptitude Predict Systematic Trends in Analytic Forecasting Behavior

PubMed Central

Forlines, Clifton L.; Miller, Sarah M.; Regan, John R.; Irvine, John M.

2014-01-01

The decision sciences are increasingly challenged to advance methods for modeling analysts, accounting for both analytic strengths and weaknesses, to improve inferences taken from increasingly large and complex sources of data. We examine whether psychometric measures—personality, cognitive style, motivated cognition—predict analytic performance and whether psychometric measures are competitive with aptitude measures (i.e., SAT scores) as analyst sample selection criteria. A heterogeneous, national sample of 927 participants completed an extensive battery of psychometric measures and aptitude tests and was asked 129 geopolitical forecasting questions over the course of 1 year. Factor analysis reveals four dimensions among psychometric measures; dimensions characterized by differently motivated “top-down” cognitive styles predicted distinctive patterns in aptitude and forecasting behavior. These dimensions were not better predictors of forecasting accuracy than aptitude measures. However, multiple regression and mediation analysis reveals that these dimensions influenced forecasting accuracy primarily through bias in forecasting confidence. We also found that these facets were competitive with aptitude tests as forecast sampling criteria designed to mitigate biases in forecasting confidence while maximizing accuracy. These findings inform the understanding of individual difference dimensions at the intersection of analytic aptitude and demonstrate that they wield predictive power in applied, analytic domains. PMID:25983670

A data mining system for providing analytical information on brain tumors to public health decision makers.

PubMed

Santos, R S; Malheiros, S M F; Cavalheiro, S; de Oliveira, J M Parente

2013-03-01

Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Malignant brain neoplasms are among the most devastating and incurable forms of cancer, and their treatment may be excessively complex and costly. Public health decision makers require significant amounts of analytical information to manage public treatment programs for these patients. Data mining, a technology that is used to produce analytically useful information, has been employed successfully with medical data. However, the large-scale adoption of this technique has been limited thus far because it is difficult to use, especially for non-expert users. One way to facilitate data mining by non-expert users is to automate the process. Our aim is to present an automated data mining system that allows public health decision makers to access analytical information regarding brain tumors. The emphasis in this study is the use of ontology in an automated data mining process. The non-experts who tried the system obtained useful information about the treatment of brain tumors. These results suggest that future work should be conducted in this area. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Non-monetary valuation using Multi-Criteria Decision Analysis: Sensitivity of additive aggregation methods to scaling and compensation assumptions

EPA Science Inventory

Analytical methods for Multi-Criteria Decision Analysis (MCDA) support the non-monetary valuation of ecosystem services for environmental decision making. Many published case studies transform ecosystem service outcomes into a common metric and aggregate the outcomes to set land ...

Past developments and future directions for the AHP in natural resources

Treesearch

Daniel L. Schmoldt; G.A. Mendoza; Jyrki Kangas

2001-01-01

The analytic hierarchy process (AHP) possesses certain characteristics that make it a useful tool for natural resource decision making. The AHP’s capabilities include: participatory decision making, problem structuring and alternative development, group facilitation, consensus building, fairness, qualitative and quantitative information, conflict resolution, decision...

Identification of drought in Dhalai river watershed using MCDM and ANN models

NASA Astrophysics Data System (ADS)

Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

2017-03-01

An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

Decision-making processes for the uptake and implementation of family-based therapy by eating disorder treatment teams: a qualitative study.

PubMed

Kimber, Melissa; Couturier, Jennifer; Jack, Susan; Niccols, Alison; Van Blyderveen, Sherry; McVey, Gail

2014-01-01

To explore the decision-making processes involved in the uptake and implementation of evidence-based treatments (EBTs), namely, family-based treatment (FBT), among therapists and their administrators within publically funded eating disorder treatment programs in Ontario, Canada. Fundamental qualitative description guided sampling, data collection, and analytic decisions. Forty therapists and 11 administrators belonging to a network of clinicians treating eating disorders completed an in-depth interview regarding the decision-making processes involved in EBT uptake and implementation within their organizations. Content analysis and the constant comparative technique were used to analyze interview transcripts, with 20% of the data independently double-coded by a second coder. Therapists and their administrators identified the importance of an inclusive change culture in evidence-based practice (EBP) decision-making. Each group indicated reluctance to make EBP decisions in isolation from the other. Additionally, participants identified seven stages of decision-making involved in EBT adoption, beginning with exposure to the EBT model and ending with evaluating the impact of the EBT on patient outcomes. Support for a stage-based decision-making process was in participants' indication that the stages were needed to demonstrate that they considered the costs and benefits of making a practice change. Participants indicated that EBTs endorsed by the Provincial Network for Eating Disorders or the Academy for Eating Disorders would more likely be adopted. Future work should focus on integrating the important decision-making processes identified in this study with known implementation models to increase the use of low-cost and effective treatments, such as FBT, within eating disorder treatment programs. Copyright © 2013 Wiley Periodicals, Inc.

Logic-Based Retrieval: Technology for Content-Oriented and Analytical Querying of Patent Data

NASA Astrophysics Data System (ADS)

Klampanos, Iraklis Angelos; Wu, Hengzhi; Roelleke, Thomas; Azzam, Hany

Patent searching is a complex retrieval task. An initial document search is only the starting point of a chain of searches and decisions that need to be made by patent searchers. Keyword-based retrieval is adequate for document searching, but it is not suitable for modelling comprehensive retrieval strategies. DB-like and logical approaches are the state-of-the-art techniques to model strategies, reasoning and decision making. In this paper we present the application of logical retrieval to patent searching. The two grand challenges are expressiveness and scalability, where high degree of expressiveness usually means a loss in scalability. In this paper we report how to maintain scalability while offering the expressiveness of logical retrieval required for solving patent search tasks. We present logical retrieval background, and how to model data-source selection and results' fusion. Moreover, we demonstrate the modelling of a retrieval strategy, a technique by which patent professionals are able to express, store and exchange their strategies and rationales when searching patents or when making decisions. An overview of the architecture and technical details complement the paper, while the evaluation reports preliminary results on how query processing times can be guaranteed, and how quality is affected by trading off responsiveness.

Developing a conceptual model for the application of patient and public involvement in the healthcare system in Iran.

PubMed

Azmal, Mohammad; Sari, Ali Akbari; Foroushani, Abbas Rahimi; Ahmadi, Batoul

2016-06-01

Patient and public involvement is engaging patients, providers, community representatives, and the public in healthcare planning and decision-making. The purpose of this study was to develop a model for the application of patient and public involvement in decision making in the Iranian healthcare system. A mixed qualitative-quantitative approach was used to develop a conceptual model. Thirty three key informants were purposely recruited in the qualitative stage, and 420 people (patients and their companions) were included in a protocol study that was implemented in five steps: 1) Identifying antecedents, consequences, and variables associated with the patient and the publics' involvement in healthcare decision making through a comprehensive literature review; 2) Determining the main variables in the context of Iran's health system using conceptual framework analysis; 3) Prioritizing and weighting variables by Shannon entropy; 4) designing and validating a tool for patient and public involvement in healthcare decision making; and 5) Providing a conceptual model of patient and the public involvement in planning and developing healthcare using structural equation modeling. We used various software programs, including SPSS (17), Max QDA (10), EXCEL, and LISREL. Content analysis, Shannon entropy, and descriptive and analytic statistics were used to analyze the data. In this study, seven antecedents variable, five dimensions of involvement, and six consequences were identified. These variables were used to design a valid tool. A logical model was derived that explained the logical relationships between antecedent and consequent variables and the dimensions of patient and public involvement as well. Given the specific context of the political, social, and innovative environments in Iran, it was necessary to design a model that would be compatible with these features. It can improve the quality of care and promote the patient and the public satisfaction with healthcare and legitimate the representative of people they served for. This model can provide a practical guide for managers and policy makers to involve people in making the decisions that influence their lives.

Trends & Controversies: Sociocultural Predictive Analytics and Terrorism Deterrence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; McGrath, Liam R.

2011-08-12

The use of predictive analytics to model terrorist rhetoric is highly instrumental in developing a strategy to deter terrorism. Traditional (e.g. Cold-War) deterrence methods are ineffective with terrorist groups such as al Qaida. Terrorists typically regard the prospect of death or loss of property as acceptable consequences of their struggle. Deterrence by threat of punishment is therefore fruitless. On the other hand, isolating terrorists from the community that may sympathize with their cause can have a decisive deterring outcome. Without the moral backing of a supportive audience, terrorism cannot be successfully framed as a justifiable political strategy and recruiting ismore » curtailed. Ultimately, terrorism deterrence is more effectively enforced by exerting influence to neutralize the communicative reach of terrorists.« less

Maternal Household Decision-Making Autonomy and Adolescent Education in Honduras.

PubMed

Hendrick, C Emily; Marteleto, Leticia

2017-06-01

Maternal decision-making autonomy has been linked to positive outcomes for children's health and well-being early in life in low- and middle-income countries throughout the world. However, there is a dearth of research examining if and how maternal autonomy continues to influence children's outcomes into adolescence and whether it impacts other domains of children's lives beyond health, such as their education. The goal of this study was to determine whether high maternal decision-making was associated with school enrollment for secondary school-aged youth in Honduras. Further, we aimed to assess whether the relationships between maternal autonomy and school enrollment varied by adolescents' environmental contexts and individual characteristics such as gender. Our analytical sample included 6,579 adolescents ages 12-16 living with their mothers from the Honduran Demographic and Health Survey (DHS) 2011-12. We used stepwise logistic regression models to investigate the association between maternal household decision-making autonomy and adolescents' school enrollment. Our findings suggest that adolescents, especially girls, benefit from their mothers' high decision-making autonomy. Findings suggest that maternal decision-making autonomy promotes adolescents' school enrollment above and beyond other maternal, household, and regional influences.

Collaborative Decision Model on Stockpile Material of a Traditional Market Infrastructure using Value-Based HBU

NASA Astrophysics Data System (ADS)

Utomo, C.; Rahmawati, Y.; Pararta, D. L.; Ariesta, A.

2017-11-01

Readiness of infrastructure establishment is needed in the early phase of real estate development. To meet the needs of retail property in the form of traditional markets, the Government prepares to build a new 1300 units. Traditional market development requires infrastructure development. One of it is the preparation of sand material embankment as much as ± 200,000 m3. With a distance of 30 km, sand material can be delivered to the project site by dump trucks that can only be operated by 2 trip per day. The material is managed by using stockpile method. Decision of stockpile location requires multi person and multi criteria in a collaborative environment. The highest and the best use (HBU) criteria was used to construct a value-based decision hierarchy. Decision makers from five stakeholders analyzed the best of three locations by giving their own preference of development cost and HBU function. Analytical Hierarchy Process (AHP) based on satisfying options and cooperative game was applied for agreement options and coalition formation on collaborative decision. The result indicates that not all solutions become a possible location for the stockpile material. It shows the ‘best fit’ options process for all decision makers.

Maternal Household Decision-Making Autonomy and Adolescent Education in Honduras

PubMed Central

Hendrick, C. Emily; Marteleto, Leticia

2017-01-01

Maternal decision-making autonomy has been linked to positive outcomes for children’s health and well-being early in life in low- and middle-income countries throughout the world. However, there is a dearth of research examining if and how maternal autonomy continues to influence children’s outcomes into adolescence and whether it impacts other domains of children’s lives beyond health, such as their education. The goal of this study was to determine whether high maternal decision-making was associated with school enrollment for secondary school-aged youth in Honduras. Further, we aimed to assess whether the relationships between maternal autonomy and school enrollment varied by adolescents’ environmental contexts and individual characteristics such as gender. Our analytical sample included 6,579 adolescents ages 12–16 living with their mothers from the Honduran Demographic and Health Survey (DHS) 2011–12. We used stepwise logistic regression models to investigate the association between maternal household decision-making autonomy and adolescents’ school enrollment. Our findings suggest that adolescents, especially girls, benefit from their mothers’ high decision-making autonomy. Findings suggest that maternal decision-making autonomy promotes adolescents’ school enrollment above and beyond other maternal, household, and regional influences. PMID:29075048

Integrating uncertainty into public energy research and development decisions

NASA Astrophysics Data System (ADS)

Anadón, Laura Díaz; Baker, Erin; Bosetti, Valentina

2017-05-01

Public energy research and development (R&D) is recognized as a key policy tool for transforming the world's energy system in a cost-effective way. However, managing the uncertainty surrounding technological change is a critical challenge for designing robust and cost-effective energy policies. The design of such policies is particularly important if countries are going to both meet the ambitious greenhouse-gas emissions reductions goals set by the Paris Agreement and achieve the required harmonization with the broader set of objectives dictated by the Sustainable Development Goals. The complexity of informing energy technology policy requires, and is producing, a growing collaboration between different academic disciplines and practitioners. Three analytical components have emerged to support the integration of technological uncertainty into energy policy: expert elicitations, integrated assessment models, and decision frameworks. Here we review efforts to incorporate all three approaches to facilitate public energy R&D decision-making under uncertainty. We highlight emerging insights that are robust across elicitations, models, and frameworks, relating to the allocation of public R&D investments, and identify gaps and challenges that remain.

Does [-2]Pro-Prostate Specific Antigen Meet the Criteria to Justify Its Inclusion in the Clinical Decision-Making Process?

PubMed

Sanchis-Bonet, Angeles; Barrionuevo-González, Marta; Bajo-Chueca, Ana; Morales-Palacios, Nelson; Sanchez-Chapado, Manuel

2018-01-01

To assess whether [-2]pro-prostate-specific antigen (p2PSA) meets the criteria to justify its inclusion in a predictive model of prostate cancer (PCa) diagnosis and in the clinical decision-making process. A total 172 men with total prostate-specific antigen of 2-10 ng/mL underwent measurement of free PSA and p2PSA before prostate biopsy in an observational and prospective study. From these measurements, the Prostate Health Index (PHI) was calculated. Clinical and analytical predictive models were created incorporating PHI. Of 172 men, 72 (42%) were diagnosed with PCa, 33 (46%) of whom were found to be with high-grade disease. PHI score was the most predictive of biopsy outcomes in terms of discriminative ability (area under the curve = 0.79), with an added gain in predictive accuracy of 17%. All the models that incorporated PHI worked better in terms of calibration close to 45° on the slope. In the decision curve analysis, at a threshold probability of 40% we could prevent 82 biopsies, missing only 16 tumors and 5 high-grade tumors. PHI score is a more discriminant biomarker, has superior calibration and superior net benefit, and provides a higher rate of avoided biopsies; thus, it can be useful for aiding in making a more informed decision for each patient. © 2018 S. Karger AG, Basel.

Health informatics and analytics - building a program to integrate business analytics across clinical and administrative disciplines.

PubMed

Tremblay, Monica Chiarini; Deckard, Gloria J; Klein, Richard

2016-07-01

Health care organizations must develop integrated health information systems to respond to the numerous government mandates driving the movement toward reimbursement models emphasizing value-based and accountable care. Success in this transition requires integrated data analytics, supported by the combination of health informatics, interoperability, business process design, and advanced decision support tools. This case study presents the development of a master's level cross- and multidisciplinary informatics program offered through a business school. The program provides students from diverse backgrounds with the knowledge, leadership, and practical application skills of health informatics, information systems, and data analytics that bridge the interests of clinical and nonclinical professionals. This case presents the actions taken and challenges encountered in navigating intra-university politics, specifying curriculum, recruiting the requisite interdisciplinary faculty, innovating the educational format, managing students with diverse educational and professional backgrounds, and balancing multiple accreditation agencies. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Investigating a self-scoring interview simulation for learning and assessment in the medical consultation.

PubMed

Bruen, Catherine; Kreiter, Clarence; Wade, Vincent; Pawlikowska, Teresa

2017-01-01

Experience with simulated patients supports undergraduate learning of medical consultation skills. Adaptive simulations are being introduced into this environment. The authors investigate whether it can underpin valid and reliable assessment by conducting a generalizability analysis using IT data analytics from the interaction of medical students (in psychiatry) with adaptive simulations to explore the feasibility of adaptive simulations for supporting automated learning and assessment. The generalizability (G) study was focused on two clinically relevant variables: clinical decision points and communication skills. While the G study on the communication skills score yielded low levels of true score variance, the results produced by the decision points, indicating clinical decision-making and confirming user knowledge of the process of the Calgary-Cambridge model of consultation, produced reliability levels similar to what might be expected with rater-based scoring. The findings indicate that adaptive simulations have potential as a teaching and assessment tool for medical consultations.

Item response theory analysis of the Lichtenberg Financial Decision Screening Scale.

PubMed

Teresi, Jeanne A; Ocepek-Welikson, Katja; Lichtenberg, Peter A

2017-01-01

The focus of these analyses was to examine the psychometric properties of the Lichtenberg Financial Decision Screening Scale (LFDSS). The purpose of the screen was to evaluate the decisional abilities and vulnerability to exploitation of older adults. Adults aged 60 and over were interviewed by social, legal, financial, or health services professionals who underwent in-person training on the administration and scoring of the scale. Professionals provided a rating of the decision-making abilities of the older adult. The analytic sample included 213 individuals with an average age of 76.9 (SD = 10.1). The majority (57%) were female. Data were analyzed using item response theory (IRT) methodology. The results supported the unidimensionality of the item set. Several IRT models were tested. Ten ordinal and binary items evidenced a slightly higher reliability estimate (0.85) than other versions and better coverage in terms of the range of reliable measurement across the continuum of financial incapacity.

Student Attitudes toward Learning Analytics in Higher Education: "The Fitbit Version of the Learning World".

PubMed

Roberts, Lynne D; Howell, Joel A; Seaman, Kristen; Gibson, David C

2016-01-01

Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups ( N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, "Uninformed and Uncertain," represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; "Help or Hindrance to Learning," "More than a Number," and "Impeding Independence"; represented students' perceptions of the likely impact of learning analytics on their learning. "Driving Inequality" and "Where Will it Stop?" represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how "personal" vs. "collective" purposes or principles can intersect with "uniform" vs. "autonomous" activity. The findings highlight the need the need to engage students in the decision making process about learning analytics.

A ricin forensic profiling approach based on a complex set of biomarkers.

PubMed

Fredriksson, Sten-Åke; Wunschel, David S; Lindström, Susanne Wiklund; Nilsson, Calle; Wahl, Karen; Åstot, Crister

2018-08-15

A forensic method for the retrospective determination of preparation methods used for illicit ricin toxin production was developed. The method was based on a complex set of biomarkers, including carbohydrates, fatty acids, seed storage proteins, in combination with data on ricin and Ricinus communis agglutinin. The analyses were performed on samples prepared from four castor bean plant (R. communis) cultivars by four different sample preparation methods (PM1-PM4) ranging from simple disintegration of the castor beans to multi-step preparation methods including different protein precipitation methods. Comprehensive analytical data was collected by use of a range of analytical methods and robust orthogonal partial least squares-discriminant analysis- models (OPLS-DA) were constructed based on the calibration set. By the use of a decision tree and two OPLS-DA models, the sample preparation methods of test set samples were determined. The model statistics of the two models were good and a 100% rate of correct predictions of the test set was achieved. Copyright © 2018 Elsevier B.V. All rights reserved.

An Overview of Learning Analytics

ERIC Educational Resources Information Center

Zilvinskis, John; Willis, James, III; Borden, Victor M. H.

2017-01-01

The purpose of this chapter is to provide administrators and faculty with an understanding of learning analytics and its relationship to existing roles and functions so better institutional decisions can be made about investments and activities related to these technologies.

Collaborative decision-analytic framework to maximize resilience of tidal marshes to climate change

USGS Publications Warehouse

Thorne, Karen M.; Mattsson, Brady J.; Takekawa, John Y.; Cummings, Jonathan; Crouse, Debby; Block, Giselle; Bloom, Valary; Gerhart, Matt; Goldbeck, Steve; Huning, Beth; Sloop, Christina; Stewart, Mendel; Taylor, Karen; Valoppi, Laura

2015-01-01

Decision makers that are responsible for stewardship of natural resources face many challenges, which are complicated by uncertainty about impacts from climate change, expanding human development, and intensifying land uses. A systematic process for evaluating the social and ecological risks, trade-offs, and cobenefits associated with future changes is critical to maximize resilience and conserve ecosystem services. This is particularly true in coastal areas where human populations and landscape conversion are increasing, and where intensifying storms and sea-level rise pose unprecedented threats to coastal ecosystems. We applied collaborative decision analysis with a diverse team of stakeholders who preserve, manage, or restore tidal marshes across the San Francisco Bay estuary, California, USA, as a case study. Specifically, we followed a structured decision-making approach, and we using expert judgment developed alternative management strategies to increase the capacity and adaptability to manage tidal marsh resilience while considering uncertainties through 2050. Because sea-level rise projections are relatively confident to 2050, we focused on uncertainties regarding intensity and frequency of storms and funding. Elicitation methods allowed us to make predictions in the absence of fully compatible models and to assess short- and long-term trade-offs. Specifically we addressed two questions. (1) Can collaborative decision analysis lead to consensus among a diverse set of decision makers responsible for environmental stewardship and faced with uncertainties about climate change, funding, and stakeholder values? (2) What is an optimal strategy for the conservation of tidal marshes, and what strategy is robust to the aforementioned uncertainties? We found that when taking this approach, consensus was reached among the stakeholders about the best management strategies to maintain tidal marsh integrity. A Bayesian decision network revealed that a strategy considering sea-level rise and storms explicitly in wetland restoration planning and designs was optimal, and it was robust to uncertainties about management effectiveness and budgets. We found that strategies that avoided explicitly accounting for future climate change had the lowest expected performance based on input from the team. Our decision-analytic framework is sufficiently general to offer an adaptable template, which can be modified for use in other areas that include a diverse and engaged stakeholder group.

Learning Analytics: Insights into the Natural Learning Behavior of Our Students

ERIC Educational Resources Information Center

Becker, Bernd

2013-01-01

The migration from traditional classrooms to online learning environments is in full effect. In the midst of these changes, a new approach to learning analytics needs to be considered. Learning analytics refers to the process of collecting and studying usage data in order to make instructional decisions that will support student success. In…

Enhanced Adaptive Management: Integrating Decision Analysis, Scenario Analysis and Environmental Modeling for the Everglades

PubMed Central

Convertino, Matteo; Foran, Christy M.; Keisler, Jeffrey M.; Scarlett, Lynn; LoSchiavo, Andy; Kiker, Gregory A.; Linkov, Igor

2013-01-01

We propose to enhance existing adaptive management efforts with a decision-analytical approach that can guide the initial selection of robust restoration alternative plans and inform the need to adjust these alternatives in the course of action based on continuously acquired monitoring information and changing stakeholder values. We demonstrate an application of enhanced adaptive management for a wetland restoration case study inspired by the Florida Everglades restoration effort. We find that alternatives designed to reconstruct the pre-drainage flow may have a positive ecological impact, but may also have high operational costs and only marginally contribute to meeting other objectives such as reduction of flooding. Enhanced adaptive management allows managers to guide investment in ecosystem modeling and monitoring efforts through scenario and value of information analyses to support optimal restoration strategies in the face of uncertain and changing information. PMID:24113217

The four principles: Can they be measured and do they predict ethical decision making?

PubMed Central

2012-01-01

Background The four principles of Beauchamp and Childress - autonomy, non-maleficence, beneficence and justice - have been extremely influential in the field of medical ethics, and are fundamental for understanding the current approach to ethical assessment in health care. This study tests whether these principles can be quantitatively measured on an individual level, and then subsequently if they are used in the decision making process when individuals are faced with ethical dilemmas. Methods The Analytic Hierarchy Process was used as a tool for the measurement of the principles. Four scenarios, which involved conflicts between the medical ethical principles, were presented to participants who then made judgments about the ethicality of the action in the scenario, and their intentions to act in the same manner if they were in the situation. Results Individual preferences for these medical ethical principles can be measured using the Analytic Hierarchy Process. This technique provides a useful tool in which to highlight individual medical ethical values. On average, individuals have a significant preference for non-maleficence over the other principles, however, and perhaps counter-intuitively, this preference does not seem to relate to applied ethical judgements in specific ethical dilemmas. Conclusions People state they value these medical ethical principles but they do not actually seem to use them directly in the decision making process. The reasons for this are explained through the lack of a behavioural model to account for the relevant situational factors not captured by the principles. The limitations of the principles in predicting ethical decision making are discussed. PMID:22606995

The four principles: can they be measured and do they predict ethical decision making?

PubMed

Page, Katie

2012-05-20

The four principles of Beauchamp and Childress--autonomy, non-maleficence, beneficence and justice--have been extremely influential in the field of medical ethics, and are fundamental for understanding the current approach to ethical assessment in health care. This study tests whether these principles can be quantitatively measured on an individual level, and then subsequently if they are used in the decision making process when individuals are faced with ethical dilemmas. The Analytic Hierarchy Process was used as a tool for the measurement of the principles. Four scenarios, which involved conflicts between the medical ethical principles, were presented to participants who then made judgments about the ethicality of the action in the scenario, and their intentions to act in the same manner if they were in the situation. Individual preferences for these medical ethical principles can be measured using the Analytic Hierarchy Process. This technique provides a useful tool in which to highlight individual medical ethical values. On average, individuals have a significant preference for non-maleficence over the other principles, however, and perhaps counter-intuitively, this preference does not seem to relate to applied ethical judgements in specific ethical dilemmas. People state they value these medical ethical principles but they do not actually seem to use them directly in the decision making process. The reasons for this are explained through the lack of a behavioural model to account for the relevant situational factors not captured by the principles. The limitations of the principles in predicting ethical decision making are discussed.

Early decision-analytic modeling - a case study on vascular closure devices.

PubMed

Brandes, Alina; Sinner, Moritz F; Kääb, Stefan; Rogowski, Wolf H

2015-10-27

As economic considerations become more important in healthcare reimbursement, decisions about the further development of medical innovations need to take into account not only medical need and potential clinical effectiveness, but also cost-effectiveness. Already early in the innovation process economic evaluations can support decisions on development in specific indications or patient groups by anticipating future reimbursement and implementation decisions. One potential concept for early assessment is value-based pricing. The objective is to assess the feasibility of value-based pricing and product design for a hypothetical vascular closure device in the pre-clinical stage which aims at decreasing bleeding events. A deterministic decision-analytic model was developed to estimate the cost-effectiveness of established vascular closure devices from the perspective of the Statutory Health Insurance system. To identify early benchmarks for pricing and product design, three strategies of determining the product's value are explored: 1) savings from complications avoided by the new device; 2) valuation of the avoided complications based on an assumed willingness-to-pay-threshold (the efficiency frontier approach); 3) value associated with modifying the care pathways within which the device would be applied. Use of established vascular closure devices is dominated by manual compression. The hypothetical vascular closure device reduces overall complication rates at higher costs than manual compression. Maximum cost savings of only about €4 per catheterization could be realized by applying the hypothetical device. Extrapolation of an efficiency frontier is only possible for one subgroup where vascular closure devices are not a dominated strategy. Modifying care in terms of same-day discharge of patients treated with vascular closure devices could result in cost savings of €400-600 per catheterization. It was partially feasible to calculate value-based prices for the novel closure device which can be used to inform product design. However, modifying the care pathway may generate much more value from the payers' perspective than modifying the device per se. Manufacturers should thus explore the feasibility of combining reimbursement of their product with arrangements that make same-day discharge attractive also for hospitals. Due to the early nature of the product, the results are afflicted with substantial uncertainty.

Knowledge Engineering as a Component of the Curriculum for Medical Cybernetists.

PubMed

Karas, Sergey; Konev, Arthur

2017-01-01

According to a new state educational standard, students who have chosen medical cybernetics as their major must develop a knowledge engineering competency. Previously, in the course "Clinical cybernetics" while practicing project-based learning students were designing automated workstations for medical personnel using client-server technology. The purpose of the article is to give insight into the project of a new educational module "Knowledge engineering". Students will acquire expert knowledge by holding interviews and conducting surveys, and then they will formalize it. After that, students will form declarative expert knowledge in a network model and analyze the knowledge graph. Expert decision making methods will be applied in software on the basis of a production model of knowledge. Project implementation will result not only in the development of analytical competencies among students, but also creation of a practically useful expert system based on student models to support medical decisions. Nowadays, this module is being tested in the educational process.

Application of the predicted heat strain model in development of localized, threshold-based heat stress management guidelines for the construction industry.

PubMed

Rowlinson, Steve; Jia, Yunyan Andrea

2014-04-01

Existing heat stress risk management guidelines recommended by international standards are not practical for the construction industry which needs site supervision staff to make instant managerial decisions to mitigate heat risks. The ability of the predicted heat strain (PHS) model [ISO 7933 (2004). Ergonomics of the thermal environment analytical determination and interpretation of heat stress using calculation of the predicted heat strain. Geneva: International Standard Organisation] to predict maximum allowable exposure time (D lim) has now enabled development of localized, action-triggering and threshold-based guidelines for implementation by lay frontline staff on construction sites. This article presents a protocol for development of two heat stress management tools by applying the PHS model to its full potential. One of the tools is developed to facilitate managerial decisions on an optimized work-rest regimen for paced work. The other tool is developed to enable workers' self-regulation during self-paced work.

Professional responsibility and decision-making in the context of a disease-focused model of nursing care: The difficulties experienced by Spanish nurses.

PubMed

Rodrigo, Olga; Caïs, Jordi; Monforte-Royo, Cristina

2017-10-01

When, in 1977, nurse education in Spain was transferred to universities a more patient-centred, the Anglo-American philosophy of care was introduced into a context in which nurses had traditionally prioritised their technical skills. This paper examines the characteristics of the nurse's professional role in Spain, where the model of nursing practice has historically placed them in a position akin to that of physician assistants. The study design was qualitative and used the method of analytic induction. Participants were selected by means of theoretical sampling and then underwent in-depth interviews. The resulting material was analysed using an approach based on the principles of grounded theory. Strategies were applied to ensure the credibility, transferability, dependability and confirmability of the findings. The main conclusion is that nurses in Spain continue to work within a disease-focused model of care, making it difficult for them to take responsibility for decision-making. © 2017 John Wiley & Sons Ltd.

Implementing Participatory Decision Making in Forest Planning

NASA Astrophysics Data System (ADS)

Ananda, Jayanath

2007-04-01

Forest policy decisions are often a source of debate, conflict, and tension in many countries. The debate over forest land-use decisions often hinges on disagreements about societal values related to forest resource use. Disagreements on social value positions are fought out repeatedly at local, regional, national, and international levels at an enormous social cost. Forest policy problems have some inherent characteristics that make them more difficult to deal with. On the one hand, forest policy decisions involve uncertainty, long time scales, and complex natural systems and processes. On the other hand, such decisions encompass social, political, and cultural systems that are evolving in response to forces such as globalization. Until recently, forest policy was heavily influenced by the scientific community and various economic models of optimal resource use. However, growing environmental awareness and acceptance of participatory democracy models in policy formulation have forced the public authorities to introduce new participatory mechanisms to manage forest resources. Most often, the efforts to include the public in policy formulation can be described using the lower rungs of Arnstein’s public participation typology. This paper presents an approach that incorporates stakeholder preferences into forest land-use policy using the Analytic Hierarchy Process (AHP). An illustrative case of regional forest-policy formulation in Australia is used to demonstrate the approach. It is contended that applying the AHP in the policy process could considerably enhance the transparency of participatory process and public acceptance of policy decisions.

The Development of a Strategic Prioritisation Method for Green Supply Chain Initiatives.

PubMed

Masoumik, S Maryam; Abdul-Rashid, Salwa Hanim; Olugu, Ezutah Udoncy

2015-01-01

To maintain a competitive position, companies are increasingly required to integrate their proactive environmental strategies into their business strategies. The shift from reactive and compliance-based to proactive and strategic environmental management has driven companies to consider the strategic factors while identifying the areas in which they should focus their green initiatives. In previous studies little attention was given to providing the managers with a basis from which they could strategically prioritise these green initiatives across their companies' supply chains. Considering this lacuna in the literature, we present a decision-making method for prioritising green supply chain initiatives aligned with the preferred green strategies alternatives for the manufacturing companies. To develop this method, the study considered a position between determinism and the voluntarism orientation of environmental management involving both external pressures and internal competitive drivers and key resources as decision factors. This decision-making method was developed using the analytic network process (ANP) technique. The elements of the decision model were derived from the literature. The causal relationships among the multiple decision variables were validated based on the results of structural equation modelling (SEM) using a dataset collected from a survey of the ISO 14001-certified manufacturers in Malaysia. A portion of the relative weights required for computation in ANP was also calculated using the SEM results. A case study is presented to demonstrate the applicability of the method.

The Development of a Strategic Prioritisation Method for Green Supply Chain Initiatives

PubMed Central

Masoumik, S. Maryam; Abdul-Rashid, Salwa Hanim; Olugu, Ezutah Udoncy

2015-01-01

To maintain a competitive position, companies are increasingly required to integrate their proactive environmental strategies into their business strategies. The shift from reactive and compliance-based to proactive and strategic environmental management has driven companies to consider the strategic factors while identifying the areas in which they should focus their green initiatives. In previous studies little attention was given to providing the managers with a basis from which they could strategically prioritise these green initiatives across their companies’ supply chains. Considering this lacuna in the literature, we present a decision-making method for prioritising green supply chain initiatives aligned with the preferred green strategies alternatives for the manufacturing companies. To develop this method, the study considered a position between determinism and the voluntarism orientation of environmental management involving both external pressures and internal competitive drivers and key resources as decision factors. This decision-making method was developed using the analytic network process (ANP) technique. The elements of the decision model were derived from the literature. The causal relationships among the multiple decision variables were validated based on the results of structural equation modelling (SEM) using a dataset collected from a survey of the ISO 14001-certified manufacturers in Malaysia. A portion of the relative weights required for computation in ANP was also calculated using the SEM results. A case study is presented to demonstrate the applicability of the method. PMID:26618353

Lessons of War: Turning Data Into Decisions.

PubMed

Forsberg, Jonathan A; Potter, Benjamin K; Wagner, Matthew B; Vickers, Andrew; Dente, Christopher J; Kirk, Allan D; Elster, Eric A

2015-09-01

Recent conflicts in Afghanistan and Iraq produced a substantial number of critically wounded service-members. We collected biomarker and clinical information from 73 patients who sustained 116 life-threatening combat wounds, and sought to determine if the data could be used to predict the likelihood of wound failure. From each patient, we collected clinical information, serum, wound effluent, and tissue prior to and at each surgical débridement. Inflammatory cytokines were quantified in both the serum and effluent, as were gene expression targets. The primary outcome was successful wound healing. Computer intensive methods were used to derive prognostic models that were internally validated using target shuffling and cross-validation methods. A second cohort of eighteen critically injured civilian patients was evaluated to determine if similar inflammatory responses were observed. The best-performing models enhanced clinical observation with biomarker data from the serum and wound effluent, an indicator that systemic inflammatory conditions contribute to local wound failure. A Random Forest model containing ten variables demonstrated the highest accuracy (AUC 0.79). Decision Curve Analysis indicated that the use of this model would improve clinical outcomes and reduce unnecessary surgical procedures. Civilian trauma patients demonstrated similar inflammatory responses and an equivalent wound failure rate, indicating that the model may be generalizable to civilian settings. Using advanced analytics, we successfully codified clinical and biomarker data from combat patients into a potentially generalizable decision support tool. Analysis of inflammatory data from critically ill patients with acute injury may inform decision-making to improve clinical outcomes and reduce healthcare costs. United States Department of Defense Health Programs.

High Technology Service Value Maximization through an MCDM-Based Innovative e-Business Model

NASA Astrophysics Data System (ADS)

Huang, Chi-Yo; Tzeng, Gwo-Hshiung; Ho, Wen-Rong; Chuang, Hsiu-Tyan; Lue, Yeou-Feng

The emergence of the Internet has changed the high technology marketing channels thoroughly in the past decade while E-commerce has already become one of the most efficient channels which high technology firms may skip the intermediaries and reach end customers directly. However, defining appropriate e-business models for commercializing new high technology products or services through Internet are not that easy. To overcome the above mentioned problems, a novel analytic framework based on the concept of high technology customers’ competence set expansion by leveraging high technology service firms’ capabilities and resources as well as novel multiple criteria decision making (MCDM) techniques, will be proposed in order to define an appropriate e-business model. An empirical example study of a silicon intellectual property (SIP) commercialization e-business model based on MCDM techniques will be provided for verifying the effectiveness of this novel analytic framework. The analysis successful assisted a Taiwanese IC design service firm to define an e-business model for maximizing its customer’s SIP transactions. In the future, the novel MCDM framework can be applied successful to novel business model definitions in the high technology industry.

Analytic Procedures For Designing and Evaluating Decision Aids.

DTIC Science & Technology

1980-04-01

the taxonomy of decision charateristics . Chapter 5 applies the taxonomies to the information processing functions needed for AAW decisions, and...rationality emphasizes the extent to which organizations and other social institutions consist of individuals who pursue individual objectives by means of...adaptive rationality is always wrong or naive; most of us know persons that seem to be naturally good decision-makers. There is no logic that guarantees

The influence of discrete emotions on judgement and decision-making: a meta-analytic review.

PubMed

Angie, Amanda D; Connelly, Shane; Waples, Ethan P; Kligyte, Vykinta

2011-12-01

During the past three decades, researchers interested in emotions and cognition have attempted to understand the relationship that affect and emotions have with cognitive outcomes such as judgement and decision-making. Recent research has revealed the importance of examining more discrete emotions, showing that same-valence emotions (e.g., anger and fear) differentially impact judgement and decision-making outcomes. Narrative reviews of the literature (Lerner & Tiedens, 2006 ; Pham, 2007 ) have identified some under-researched topics, but provide a limited synthesis of findings. The purpose of this study was to review the research examining the influence of discrete emotions on judgement and decision-making outcomes and provide an assessment of the observed effects using a meta-analytic approach. Results, overall, show that discrete emotions have moderate to large effects on judgement and decision-making outcomes. However, moderator analyses revealed differential effects for study-design characteristics and emotion-manipulation characteristics by emotion type. Implications are discussed.

Sustainable energy planning decision using the intuitionistic fuzzy analytic hierarchy process: choosing energy technology in Malaysia

NASA Astrophysics Data System (ADS)

Abdullah, Lazim; Najib, Liana

2016-04-01

Energy consumption for developing countries is sharply increasing due to the higher economic growth due to industrialisation along with population growth and urbanisation. The increasing demand of energy leads to global energy crisis. Selecting the best energy technology and conservation requires both quantitative and qualitative evaluation criteria. The fuzzy set-based approach is one of the well-known theories to handle fuzziness, uncertainty in decision-making and vagueness of information. This paper proposes a new method of intuitionistic fuzzy analytic hierarchy process (IF-AHP) to deal with the uncertainty in decision-making. The new IF-AHP is applied to establish a preference in the sustainable energy planning decision-making problem. Three decision-makers attached with Malaysian government agencies were interviewed to provide linguistic judgement prior to analysing with the new IF-AHP. Nuclear energy has been decided as the best alternative in energy planning which provides the highest weight among all the seven alternatives.

Decision-making under risk conditions is susceptible to interference by a secondary executive task.

PubMed

Starcke, Katrin; Pawlikowski, Mirko; Wolf, Oliver T; Altstötter-Gleich, Christine; Brand, Matthias

2011-05-01

Recent research suggests two ways of making decisions: an intuitive and an analytical one. The current study examines whether a secondary executive task interferes with advantageous decision-making in the Game of Dice Task (GDT), a decision-making task with explicit and stable rules that taps executive functioning. One group of participants performed the original GDT solely, two groups performed either the GDT and a 1-back or a 2-back working memory task as a secondary task simultaneously. Results show that the group which performed the GDT and the secondary task with high executive load (2-back) decided less advantageously than the group which did not perform a secondary executive task. These findings give further evidence for the view that decision-making under risky conditions taps into the rational-analytical system which acts in a serial and not parallel way as performance on the GDT is disturbed by a parallel task that also requires executive resources.

Linking climate change and fish conservation efforts using spatially explicit decision support tools

Treesearch

Douglas P. Peterson; Seth J. Wenger; Bruce E. Rieman; Daniel J. Isaak

2013-01-01

Fisheries professionals are increasingly tasked with incorporating climate change projections into their decisions. Here we demonstrate how a structured decision framework, coupled with analytical tools and spatial data sets, can help integrate climate and biological information to evaluate management alternatives. We present examples that link downscaled climate...

Beyond privacy and exposure: ethical issues within citizen-facing analytics.

PubMed

Grindrod, Peter

2016-12-28

We discuss the governing forces for analytics, especially concerning citizens' behaviours and their transactions, that depend on which of three spheres of operation an institution is in (corporate, public sector/government and academic). We argue that aspirations and missions also differ by sphere even as digital spaces have drawn these spheres ever closer together. We propose that citizens' expectations and implicit permissions for any exploitation of their data require the perception of a fair balance of benefits, which should be transparent (accessible to citizens) and justifiable. We point out that within the corporate sphere most analytics does not concern identity, targeted marketing nor any direct interference with individual citizens; but instead it supports strategic decision-making, where the data are effectively anonymous. With the three spheres we discuss the nature of models deployed in analytics, including 'black-box' modelling uncheckable by a human mind, and the need to track the provenance and workings or models. We also examine the recent evolution of personal data, where some behaviours, or tokens, identifying individuals (unique and yet non-random) are partially and jointly owned by other individuals that are themselves connected. We consider the ability of heavily and lightly regulated sectors to increase access or to stifle innovation. We also call for clear and inclusive definitions of 'data science and analytics', avoiding the narrow claims of those in technical sub-sectors or sub-themes. Finally, we examine some examples of unethical and abusive practices. We argue for an ethical responsibility to be placed upon professional data scientists to avoid abuses in the future.This article is part of the themed issue 'The ethical impact of data science'. © 2016 The Author(s).

Rotorcraft Diagnostics

NASA Technical Reports Server (NTRS)

Haste, Deepak; Azam, Mohammad; Ghoshal, Sudipto; Monte, James

2012-01-01

Health management (HM) in any engineering systems requires adequate understanding about the system s functioning; a sufficient amount of monitored data; the capability to extract, analyze, and collate information; and the capability to combine understanding and information for HM-related estimation and decision-making. Rotorcraft systems are, in general, highly complex. Obtaining adequate understanding about functioning of such systems is quite difficult, because of the proprietary (restricted access) nature of their designs and dynamic models. Development of an EIM (exact inverse map) solution for rotorcraft requires a process that can overcome the abovementioned difficulties and maximally utilize monitored information for HM facilitation via employing advanced analytic techniques. The goal was to develop a versatile HM solution for rotorcraft for facilitation of the Condition Based Maintenance Plus (CBM+) capabilities. The effort was geared towards developing analytic and reasoning techniques, and proving the ability to embed the required capabilities on a rotorcraft platform, paving the way for implementing the solution on an aircraft-level system for consolidation and reporting. The solution for rotorcraft can he used offboard or embedded directly onto a rotorcraft system. The envisioned solution utilizes available monitored and archived data for real-time fault detection and identification, failure precursor identification, and offline fault detection and diagnostics, health condition forecasting, optimal guided troubleshooting, and maintenance decision support. A variant of the onboard version is a self-contained hardware and software (HW+SW) package that can be embedded on rotorcraft systems. The HM solution comprises components that gather/ingest data and information, perform information/feature extraction, analyze information in conjunction with the dependency/diagnostic model of the target system, facilitate optimal guided troubleshooting, and offer decision support for optimal maintenance.