Data and Decision Making: Same Organization, Different Perceptions; Different Organizations, Different Perceptions

ERIC Educational Resources Information Center

Maxwell, Nan L.; Rotz, Dana; Garcia, Christina

2016-01-01

This study examines the perceptions of data-driven decision making (DDDM) activities and culture in organizations driven by a social mission. Analysis of survey information from multiple stakeholders in each of eight social enterprises highlights the wide divergence in views of DDDM. Within an organization, managerial and nonmanagerial staff…

Creating a System for Data-Driven Decision-Making: Applying the Principal-Agent Framework

ERIC Educational Resources Information Center

Wohlstetter, Priscilla; Datnow, Amanda; Park, Vicki

2008-01-01

The purpose of this article is to improve our understanding of data-driven decision-making strategies that are initiated at the district or system level. We apply principal-agent theory to the analysis of qualitative data gathered in a case study of 4 urban school systems. Our findings suggest educators at the school level need not only systemic…

Teacher Talk about Student Ability and Achievement in the Era of Data-Driven Decision Making

ERIC Educational Resources Information Center

Datnow, Amanda; Choi, Bailey; Park, Vicki; St. John, Elise

2018-01-01

Background: Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students' needs. However, most prior research has not examined how the…

Supporting Informed Decision Making - Center for Research Strategy

Cancer.gov

CRS conducts portfolio analyses and collects data on scientific topics, funding mechanisms, and investigator characteristics to help NCI leadership make data-driven decisions about the scientific research enterprise.

Systemic Data-Based Decision Making: A Systems Approach for Using Data in Schools

ERIC Educational Resources Information Center

Walser, Tamara M.

2009-01-01

No Child Left Behind has increased data collection and reporting, the development of data systems, and interest in using data for decision-making in schools and classrooms. Ends-driven decision making has become common educational practice, where the ends justify the means at all costs, and short-term results trump longer-term outcomes and the…

How Instructional Coaches Support Data-Driven Decision Making: Policy Implementation and Effects in Florida Middle Schools

ERIC Educational Resources Information Center

Marsh, Julie A.; McCombs, Jennifer Sloan; Martorell, Francisco

2010-01-01

This article examines the convergence of two popular school improvement policies: instructional coaching and data-driven decision making (DDDM). Drawing on a mixed methods study of a statewide reading coach program in Florida middle schools, the article examines how coaches support DDDM and how this support relates to student and teacher outcomes.…

Social Capital in Data-Driven Community College Reform

ERIC Educational Resources Information Center

Kerrigan, Monica Reid

2015-01-01

The current rhetoric around using data to improve community college student outcomes with only limited research on data-driven decision-making (DDDM) within postsecondary education compels a more comprehensive understanding of colleges' capacity for using data to inform decisions. Based on an analysis of faculty and administrators' perceptions and…

Data key to quest for quality.

PubMed

Chang, Florence S; Nielsen, Jon; Macias, Charles

2013-11-01

Late-binding data warehousing reduces the time it takes to obtain data needed to make crucial decisions. Late binding refers to when and how tightly data from the source applications are bound to the rules and vocabularies that make it useful. In some cases, data can be seen in real time. In historically paper-driven environments where data-driven decisions may be a new concept, buy-in from clinicians, physicians, and hospital leaders is key to success in using data to improve outcomes.

Data-Driven Decision Making and Its Effects on Leadership Practices and Student Achievement in K-5 Public Elementary Schools in California

ERIC Educational Resources Information Center

Ceja, Rafael, Jr.

2012-01-01

The enactment of the NCLB Act of 2001 and its legislative mandates for accountability testing throughout the nation brought to the forefront the issue of data-driven decision making. This emphasis on improving education has been spurred due to the alleged failure of the public school system. As a result, the role of administrators has evolved to…

Teachers' Experiences with the Data-Driven Decision Making Process in Increasing Students' Reading Achievement in a Title I Elementary Public School

ERIC Educational Resources Information Center

Atkinson, Linton

2015-01-01

This paper is a research dissertation based on a qualitative case study conducted on Teachers' Experiences within a Data-Driven Decision Making (DDDM) process. The study site was a Title I elementary school in a large school district in Central Florida. Background information is given in relation to the need for research that was conducted on the…

Data to Decisions: Creating a Culture of Model-Driven Drug Discovery.

PubMed

Brown, Frank K; Kopti, Farida; Chang, Charlie Zhenyu; Johnson, Scott A; Glick, Meir; Waller, Chris L

2017-09-01

Merck & Co., Inc., Kenilworth, NJ, USA, is undergoing a transformation in the way that it prosecutes R&D programs. Through the adoption of a "model-driven" culture, enhanced R&D productivity is anticipated, both in the form of decreased attrition at each stage of the process and by providing a rational framework for understanding and learning from the data generated along the way. This new approach focuses on the concept of a "Design Cycle" that makes use of all the data possible, internally and externally, to drive decision-making. These data can take the form of bioactivity, 3D structures, genomics, pathway, PK/PD, safety data, etc. Synthesis of high-quality data into models utilizing both well-established and cutting-edge methods has been shown to yield high confidence predictions to prioritize decision-making and efficiently reposition resources within R&D. The goal is to design an adaptive research operating plan that uses both modeled data and experiments, rather than just testing, to drive project decision-making. To support this emerging culture, an ambitious information management (IT) program has been initiated to implement a harmonized platform to facilitate the construction of cross-domain workflows to enable data-driven decision-making and the construction and validation of predictive models. These goals are achieved through depositing model-ready data, agile persona-driven access to data, a unified cross-domain predictive model lifecycle management platform, and support for flexible scientist-developed workflows that simplify data manipulation and consume model services. The end-to-end nature of the platform, in turn, not only supports but also drives the culture change by enabling scientists to apply predictive sciences throughout their work and over the lifetime of a project. This shift in mindset for both scientists and IT was driven by an early impactful demonstration of the potential benefits of the platform, in which expert-level early discovery predictive models were made available from familiar desktop tools, such as ChemDraw. This was built using a workflow-driven service-oriented architecture (SOA) on top of the rigorous registration of all underlying model entities.

Data: Mining with a Mission- Data-Driven Decision Making Is the Buzz Phrase of Choice for the New Decade. but Once We've Got the Information, How Do We Use It to Yield Results?

ERIC Educational Resources Information Center

Salpeter, Judy

2004-01-01

For some districts, the current obsession with data grows out of the need to comply with No Child Left Behind and additional accountability-related mandates. For others, it dates way back before the phrase "data-driven decision making" rolled so frequently off the tongues of educators. In either case, there is no denying that an integral…

ENABLING SMART MANUFACTURING TECHNOLOGIES FOR DECISION-MAKING SUPPORT

PubMed Central

Helu, Moneer; Libes, Don; Lubell, Joshua; Lyons, Kevin; Morris, KC

2017-01-01

Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies. PMID:28649678

A geospatial framework for dynamic route planning using congestion prediction in transportation systems.

DOT National Transportation Integrated Search

2011-01-01

The goal this research is to develop an end-to-end data-driven system, dubbed TransDec : (short for Transportation Decision-Making), to enable decision-making queries in : transportation systems with dynamic, real-time and historical data. With Trans...

Demystifying Data: Designing and Implementing Data-Driven Systems and Practices for Continuous Quality Improvement

ERIC Educational Resources Information Center

Krugly, Andrew; Stein, Amanda; Centeno, Maribel G.

2014-01-01

Data-based decision making should be the driving force in any early care and education setting. Data usage compels early childhood practitioners and leaders to make decisions on the basis of more than just professional instinct. This article explores why early childhood schools should be using data for continuous quality improvement at various…

Data for Renewable Energy Planning, Policy, and Investment

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

Cox, Sarah L

Reliable, robust, and validated data are critical for informed planning, policy development, and investment in the clean energy sector. The Renewable Energy (RE) Explorer was developed to support data-driven renewable energy analysis that can inform key renewable energy decisions globally. This document presents the types of geospatial and other data at the core of renewable energy analysis and decision making. Individual data sets used to inform decisions vary in relation to spatial and temporal resolution, quality, and overall usefulness. From Data to Decisions, a complementary geospatial data and analysis decision guide, provides an in-depth view of these and other considerationsmore » to enable data-driven planning, policymaking, and investment. Data support a wide variety of renewable energy analyses and decisions, including technical and economic potential assessment, renewable energy zone analysis, grid integration, risk and resiliency identification, electrification, and distributed solar photovoltaic potential. This fact sheet provides information on the types of data that are important for renewable energy decision making using the RE Data Explorer or similar types of geospatial analysis tools.« less

Replicating the Relationship between Teachers' Data Use and Student Achievement: The Urban Data Study and the Data Dashboard Usage Study

ERIC Educational Resources Information Center

Faria, Ann-Marie; Greenberg, Ariela; Meakin, John; Bichay, Krystal; Heppen, Jessica

2014-01-01

Educators have long used test scores to make educational decisions, but only within the last decade has the availability of data been systematic (Abelman, Elmore, Even, Kenyon, & Marshall, 1999). In recent years, interest has spiked in data-driven decision making in education (Marsh, Pane, & Hamilton, 2006). With technological advances and…

Data-driven freeway performance evaluation framework for project prioritization and decision making.

DOT National Transportation Integrated Search

2017-01-01

This report describes methods that potentially can be incorporated into the performance monitoring and planning processes for freeway performance evaluation and decision making. Reliability analysis was conducted on the selected I-15 corridor by empl...

Data-driven freeway performance evaluation framework for project prioritization and decision making.

DOT National Transportation Integrated Search

2015-03-01

This report describes methods that potentially can be incorporated into the performance monitoring and planning : processes for freeway performance evaluation and decision making. Reliability analysis is conducted on the selected : I-15 corridor by e...

The Correlation of Data-Driven Instruction to Teacher-Effectiveness, Teacher-Collaboration, and Teacher-Satisfaction

ERIC Educational Resources Information Center

Shaw, Rhonda R.

2017-01-01

Education reform is inevitable; however, the journey of reform must ensure that educators are equipped to meet the diverse needs of all children within the classrooms throughout. Data-driven decision making is going to be the driving force for making that happen. This mixed model research was designed to show how implementing data-driven…

The Call for Data-Driven Decision Making in the Midwest's Schools: NCREL's Response.

ERIC Educational Resources Information Center

Cromey, Allison; van der Ploeg, Arie; Masini, Blase

This report describes the efforts of the North Central Regional Educational Laboratory (NCREL) during the last several years to respond to direct requests from educational stakeholders to help integrate data into their decision-making processes related to school improvement. In some cases, NCREL cooperated in the development of educational…

Balancing Data, Time, and Expectations: The Complex Decision-­Making Environment of Enrollment Management

ERIC Educational Resources Information Center

Johnson, Adam W.

2016-01-01

As a growing entity within higher education organizational structures, enrollment managers (EMs) are primarily tasked with projecting, recruiting, and retaining the student population of their campuses. Enrollment managers are expected by institutional presidents as well as through industry standards to make data-driven planning decisions to reach…

Balancing Data, Time, and Expectations: The Complex Decision-Making Environment of Enrollment Management

ERIC Educational Resources Information Center

Johnson, Adam W.

2013-01-01

As a growing entity within higher education organizational structures, enrollment managers (EMs) are primarily tasked with projecting, recruiting, and retaining the student population of their campuses. Enrollment managers are expected by institutional presidents as well as through industry standards to make data-driven planning decisions to reach…

The impact of data integrity on decision making in early lead discovery

NASA Astrophysics Data System (ADS)

Beck, Bernd; Seeliger, Daniel; Kriegl, Jan M.

2015-09-01

Data driven decision making is a key element of today's pharmaceutical research, including early drug discovery. It comprises questions like which target to pursue, which chemical series to pursue, which compound to make next, or which compound to select for advanced profiling and promotion to pre-clinical development. In the following paper we will exemplify how data integrity, i.e. the context data is generated in and auxiliary information that is provided for individual result records, can influence decision making in early lead discovery programs. In addition we will describe some approaches which we pursue at Boehringer Ingelheim to reduce the risk for getting misguided.

How Business Intelligence and Social Interaction Amplify Organizational Cognition

ERIC Educational Resources Information Center

Penn, Stephen Paul

2012-01-01

This systematic literature review of current scholarship on business intelligence, individual decision-making behavior, strategy as practice, and strategic planning offers a roadmap for firms seeking to increase their competitive advantage through data driven decision-making. Planning, deciding, and using information is a single phenomenon where…

Data-Driven Planning: Using Assessment in Strategic Planning

ERIC Educational Resources Information Center

Bresciani, Marilee J.

2010-01-01

Data-driven planning or evidence-based decision making represents nothing new in its concept. For years, business leaders have claimed they have implemented planning informed by data that have been strategically and systematically gathered. Within higher education and student affairs, there may be less evidence of the actual practice of…

Proof in the Pattern: Librarians Follow the Corporate Sector toward More Data-Driven Management

ERIC Educational Resources Information Center

Nicholson, Scott

2006-01-01

As demands on libraries continue to grow, outpacing budget increases, more librarians are forced to make difficult decisions about what materials and services stay and go. Charles R. McClure has written that many librarians use an "adhocracy" method to make these decisions, relying on no data or simple aggregates in determining a course of action.…

Data-Driven Decision Making in Community Colleges: An Integrative Model for Institutional Effectiveness

ERIC Educational Resources Information Center

Callery, Claude Adam

2012-01-01

This qualitative study identified the best practices utilized by community colleges to achieve systemic and cultural agreement in support of the integration of institutional effectiveness measures (key performance indicators) to inform decision making. In addition, the study identifies the relevant motives, organizational structure, and processes…

Data-Driven Districts.

ERIC Educational Resources Information Center

LaFee, Scott

2002-01-01

Describes the use of data-driven decision-making in four school districts: Plainfield Public Schools, Plainfield, New Jersey; Palo Alto Unified School District, Palo Alto, California; Francis Howell School District in eastern Missouri, northwest of St. Louis; and Rio Rancho Public Schools, near Albuquerque, New Mexico. Includes interviews with the…

Wisdom within: unlocking the potential of big data for nursing regulators.

PubMed

Blumer, L; Giblin, C; Lemermeyer, G; Kwan, J A

2017-03-01

This paper explores the potential for incorporating big data in nursing regulators' decision-making and policy development. Big data, commonly described as the extensive volume of information that individuals and agencies generate daily, is a concept familiar to the business community but is only beginning to be explored by the public sector. Using insights gained from a recent research project, the College and Association of Registered Nurses of Alberta, in Canada is creating an organizational culture of data-driven decision-making throughout its regulatory and professional functions. The goal is to enable the organization to respond quickly and profoundly to nursing issues in a rapidly changing healthcare environment. The evidence includes a review of the Learning from Experience: Improving the Process of Internationally Educated Nurses' Applications for Registration (LFE) research project (2011-2016), combined with a literature review on data-driven decision-making within nursing and healthcare settings, and the incorporation of big data in the private and public sectors, primarily in North America. This paper discusses experience and, more broadly, how data can enhance the rigour and integrity of nursing and health policy. Nursing regulatory bodies have access to extensive data, and the opportunity to use these data to inform decision-making and policy development by investing in how it is captured, analysed and incorporated into decision-making processes. Understanding and using big data is a critical part of developing relevant, sound and credible policy. Rigorous collection and analysis of big data supports the integrity of the evidence used by nurse regulators in developing nursing and health policy. © 2016 International Council of Nurses.

Moral dilemmas and abortion decision-making: Lessons learnt from abortion research in England and Wales.

PubMed

Hoggart, Lesley

2018-05-21

This paper scrutinises the concepts of moral reasoning and personal reasoning, problematising the binary model by looking at young women's pregnancy decision-making. Data from two UK empirical studies are subjected to theoretically driven qualitative secondary analysis, and illustrative cases show how complex decision-making is characterised by an intertwining of the personal and the moral, and is thus best understood by drawing on moral relativism.

Toward a more data-driven supervision of collegiate counseling centers.

PubMed

Varlotta, Lori E

2012-01-01

Hearing the national call for higher education accountability, the author of this tripartite article urges university administrators to move towards a more data-driven approach to counseling center supervision. Toward that end, the author first examines a key factor--perceived increase in student pathology--that appears to shape budget and staffing decisions in many university centers. Second, she reviews the emerging but conflicting research of clinician-scholars who are trying to empirically verify or refute that perception; their conflicting results suggest that no study alone should be used as the "final word" in evidence-based decision-making. Third, the author delineates the campus-specific data that should be gathered to guide staffing and budgeting decisions on each campus. She concludes by reminding readers that data-driven decisions can and should foster high-quality care that is concurrently efficient, effective, and in sync with the needs of a particular university and student body.

A Collaborative Data Chat: Teaching Summative Assessment Data Use in Pre-Service Teacher Education

ERIC Educational Resources Information Center

Piro, Jody S.; Dunlap, Karen; Shutt, Tammy

2014-01-01

As the quality of educational outputs has been problematized, accountability systems have driven reform based upon summative assessment data. These policies impact the ways that educators use data within schools and subsequently, how teacher education programs may adjust their curricula to teach data-driven decision-making to inform instruction.…

Data-Driven Decision-Making: It's a Catch-Up Game

ERIC Educational Resources Information Center

Briggs, Linda L.

2006-01-01

Having an abundance of data residing in individual silos across campus, but little decision-ready information, is a typical scenario at many institutions. One problem is that the terms "data warehousing" and "business intelligence" refer to very different things, although the two often go hand-in-hand. "Data…

Investigating the Decision-Making of Response to Intervention (RtI) Teams within the School Setting

ERIC Educational Resources Information Center

Thur, Scott M.

2015-01-01

The purpose of this study was to measure decision-making influences within RtI teams. The study examined the factors that influence school personnel involved in three areas of RtI: determining which RtI measures and tools teams select and implement (i.e. Measures and Tools), evaluating the data-driven decisions that are made based on the…

Data-Driven Decision Making: The "Other" Data

ERIC Educational Resources Information Center

Villano, Matt

2007-01-01

Data is a daily reality for school systems. Between standardized tests and tools from companies that offer data warehousing services, educators and district superintendents alike are up to their eyeballs in facts and figures about student performance that they can use as the basis for curricular decisions. Still, there is more to assessment than…

Data-Driven Decision Making in Out-of-School Time Programs. Part 6 in a Series on Implementing Evidence-Based Practices in Out-of-School Time Programs: The Role of Organization-Level Activities. Research-to-Results Brief. Publication #2009-34

ERIC Educational Resources Information Center

Bandy, Tawana; Burkhauser, Mary; Metz, Allison J. R.

2009-01-01

Although many program managers look to data to inform decision-making and manage their programs, high-quality program data may not always be available. Yet such data are necessary for effective program implementation. The use of high-quality data facilitates program management, reduces reliance on anecdotal information, and ensures that data are…

Community College Alchemists: Turning Data into Information.

ERIC Educational Resources Information Center

Johnston, George H.; Kristovich, Sharon A. R.

2000-01-01

Examines how one community college has developed a national Bellwether Award-winning data-driven decision-making process that uses its institutional research staff to make the transition from data to information. Characterizes some of the data and resulting information that might be useful to department chairs. Identifies issues and concerns that…

An integrated, ethically driven environmental model of clinical decision making in emergency settings.

PubMed

Wolf, Lisa

2013-02-01

To explore the relationship between multiple variables within a model of critical thinking and moral reasoning. A quantitative descriptive correlational design using a purposive sample of 200 emergency nurses. Measured variables were accuracy in clinical decision-making, moral reasoning, perceived care environment, and demographics. Analysis was by bivariate correlation using Pearson's product-moment correlation coefficients, chi square and multiple linear regression analysis. The elements as identified in the integrated ethically-driven environmental model of clinical decision-making (IEDEM-CD) corrected depict moral reasoning and environment of care as factors significantly affecting accuracy in decision-making. The integrated, ethically driven environmental model of clinical decision making is a framework useful for predicting clinical decision making accuracy for emergency nurses in practice, with further implications in education, research and policy. A diagnostic and therapeutic framework for identifying and remediating individual and environmental challenges to accurate clinical decision making. © 2012, The Author. International Journal of Nursing Knowledge © 2012, NANDA International.

Hands-On Learning: A Problem-Based Approach to Teaching Microsoft Excel

ERIC Educational Resources Information Center

Slayter, Erik; Higgins, Lindsey M.

2018-01-01

The development of a student's ability to make data-driven decisions has become a focus in higher education (Schield 1999; Stephenson and Caravello 2007). Data literacy, the ability to understand and use data to effectively inform decisions, is a fundamental component of information competence (Mandinach and Gummer 2013; Stephenson and Caravello,…

Making Instructional Decisions Based on Data: What, How, and Why

ERIC Educational Resources Information Center

Mokhtari, Kouider; Rosemary, Catherine A.; Edwards, Patricia A.

2007-01-01

A carefully coordinated literacy assessment and instruction framework implemented school-wide can support school teams in making sense of various types of data for instructional planning. Instruction that is data based and goal driven sets the stage for continuous reading and writing improvement. (Contains 2 figures.)

Concept of operations for knowledge discovery from Big Data across enterprise data warehouses

NASA Astrophysics Data System (ADS)

Sukumar, Sreenivas R.; Olama, Mohammed M.; McNair, Allen W.; Nutaro, James J.

2013-05-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

City Connects Prompts Data-Driven Action in Community Schools in the Bronx

ERIC Educational Resources Information Center

Haywoode, Alyssa

2018-01-01

Community schools have a long history of helping students succeed in school by addressing the problems they face outside of school. But without specific data on students and the full range of their needs, community schools cannot be as effective as they would like to be. Driven by the desire to make more data-informed decisions, the Children's Aid…

Big Data & Learning Analytics: A Potential Way to Optimize eLearning Technological Tools

ERIC Educational Resources Information Center

García, Olga Arranz; Secades, Vidal Alonso

2013-01-01

In the information age, one of the most influential institutions is education. The recent emergence of MOOCS [Massively Open Online Courses] is a sample of the new expectations that are offered to university students. Basing decisions on data and evidence seems obvious, and indeed, research indicates that data-driven decision-making improves…

Charting Success: Data Use and Student Achievement in Urban Schools

ERIC Educational Resources Information Center

Faria, Ann-Marie; Heppen, Jessica; Li, Yibing; Stachel, Suzanne; Jones, Wehmah; Sawyer, Katherine; Thomsen, Kerri; Kutner, Melissa; Miser, David; Lewis, Sharon; Casserly, Michael; Simon, Candace; Uzzell, Renata; Corcoran, Amanda; Palacios, Moses

2012-01-01

In recent years, interest has spiked in data-driven decision making in education--that is, using various types of student data to inform decisions in schools and classrooms. In October 2008, the Council of the Great City Schools and American Institutes for Research (AIR) launched a project funded by The Bill & Melinda Gates Foundation that focused…

Building a Data Warehouse.

ERIC Educational Resources Information Center

Levine, Elliott

2002-01-01

Describes how to build a data warehouse, using the Schools Interoperability Framework (www.sifinfo.org), that supports data-driven decision making and complies with the Freedom of Information Act. Provides several suggestions for building and maintaining a data warehouse. (PKP)

The Will and the Way of Data Use.

ERIC Educational Resources Information Center

Alwin, Lance

2002-01-01

Superintendent of Antigo Unified School District, Antigo, Wisconsin, explains the use of school-level and community-level data to build support for bond and budget referenda. Describes the benefits of data-driven decision-making. (PKP)

Federal Policy to Local Level Decision-Making: Data Driven Education Planning in Nigeria

ERIC Educational Resources Information Center

Iyengar, Radhika; Mahal, Angelique R.; Felicia, Ukaegbu-Nnamchi Ifeyinwa; Aliyu, Balaraba; Karim, Alia

2015-01-01

This article discusses the implementation of local level education data-driven planning as implemented by the Office of the Senior Special Assistant to the President of Nigeria on the Millennium Development Goals (OSSAP-MDGs) in partnership with The Earth Institute, Columbia University. It focuses on the design and implementation of the…

Data-Driven Decision-Making: Data Pioneers

ERIC Educational Resources Information Center

Briggs, Linda L.

2006-01-01

Everyone on your campus needs information, and if your institution is like most schools, you have plenty of it to share. But which types of data warehousing and business intelligence systems you choose, and how accessible, usable, and meaningful those tools make all of that information, remain the big questions for many technologists and…

The use of control charts by laypeople and hospital decision-makers for guiding decision making.

PubMed

Schmidtke, K A; Watson, D G; Vlaev, I

2017-07-01

Graphs presenting healthcare data are increasingly available to support laypeople and hospital staff's decision making. When making these decisions, hospital staff should consider the role of chance-that is, random variation. Given random variation, decision-makers must distinguish signals (sometimes called special-cause data) from noise (common-cause data). Unfortunately, many graphs do not facilitate the statistical reasoning necessary to make such distinctions. Control charts are a less commonly used type of graph that support statistical thinking by including reference lines that separate data more likely to be signals from those more likely to be noise. The current work demonstrates for whom (laypeople and hospital staff) and when (treatment and investigative decisions) control charts strengthen data-driven decision making. We present two experiments that compare people's use of control and non-control charts to make decisions between hospitals (funnel charts vs. league tables) and to monitor changes across time (run charts with control lines vs. run charts without control lines). As expected, participants more accurately identified the outlying data using a control chart than using a non-control chart, but their ability to then apply that information to more complicated questions (e.g., where should I go for treatment?, and should I investigate?) was limited. The discussion highlights some common concerns about using control charts in hospital settings.

A genetically mediated bias in decision making driven by failure of amygdala control.

PubMed

Roiser, Jonathan P; de Martino, Benedetto; Tan, Geoffrey C Y; Kumaran, Dharshan; Seymour, Ben; Wood, Nicholas W; Dolan, Raymond J

2009-05-06

Genetic variation at the serotonin transporter-linked polymorphic region (5-HTTLPR) is associated with altered amygdala reactivity and lack of prefrontal regulatory control. Similar regions mediate decision-making biases driven by contextual cues and ambiguity, for example the "framing effect." We hypothesized that individuals hemozygous for the short (s) allele at the 5-HTTLPR would be more susceptible to framing. Participants, selected as homozygous for either the long (la) or s allele, performed a decision-making task where they made choices between receiving an amount of money for certain and taking a gamble. A strong bias was evident toward choosing the certain option when the option was phrased in terms of gains and toward gambling when the decision was phrased in terms of losses (the frame effect). Critically, this bias was significantly greater in the ss group compared with the lala group. In simultaneously acquired functional magnetic resonance imaging data, the ss group showed greater amygdala during choices made in accord, compared with those made counter to the frame, an effect not seen in the lala group. These differences were also mirrored by differences in anterior cingulate-amygdala coupling between the genotype groups during decision making. Specifically, lala participants showed increased coupling during choices made counter to, relative to those made in accord with, the frame, with no such effect evident in ss participants. These data suggest that genetically mediated differences in prefrontal-amygdala interactions underpin interindividual differences in economic decision making.

Charting Success: Data Use and Student Achievement in Urban Schools. Executive Summary

ERIC Educational Resources Information Center

Faria, Ann-Marie; Heppen, Jessica; Li, Yibing; Stachel, Suzanne; Jones, Wehmah; Sawyer, Katherine; Thomsen, Kerri; Kutner, Melissa; Miser, David; Lewis, Sharon; Casserly, Michael; Simon, Candace; Uzzell, Renata; Corcoran, Amanda; Palacios, Moses

2012-01-01

In recent years, interest has spiked in data-driven decision making in education--that is, using various types of student data to inform decisions in schools and classrooms. In October 2008, the Council of the Great City Schools and American Institutes for Research (AIR) launched a project funded by The Bill & Melinda Gates Foundation that focused…

Getting Started with Data Warehousing: The First in a Series on How to Manage Data Efficiently

ERIC Educational Resources Information Center

Mills, Lane B.

2008-01-01

These days, "data-driven decision making" is on every school district's buzzword bingo game board. Accountability pressures and lean budgets make translating data into information a major focus of school systems that are trying to improve district outcomes in all areas. As such, data warehousing has become an essential district tool. Historically…

Decision-making in Swiss home-like childbirth: A grounded theory study.

PubMed

Meyer, Yvonne; Frank, Franziska; Schläppy Muntwyler, Franziska; Fleming, Valerie; Pehlke-Milde, Jessica

2017-12-01

Decision-making in midwifery, including a claim for shared decision-making between midwives and women, is of major significance for the health of mother and child. Midwives have little information about how to share decision-making responsibilities with women, especially when complications arise during birth. To increase understanding of decision-making in complex home-like birth settings by exploring midwives' and women's perspectives and to develop a dynamic model integrating participatory processes for making shared decisions. The study, based on grounded theory methodology, analysed 20 interviews of midwives and 20 women who had experienced complications in home-like births. The central phenomenon that arose from the data was "defining/redefining decision as a joint commitment to healthy childbirth". The sub-indicators that make up this phenomenon were safety, responsibility, mutual and personal commitments. These sub-indicators were also identified to influence temporal conditions of decision-making and to apply different strategies for shared decision-making. Women adopted strategies such as delegating a decision, making the midwife's decision her own, challenging a decision or taking a decision driven by the dynamics of childbirth. Midwives employed strategies such as remaining indecisive, approving a woman's decision, making an informed decision or taking the necessary decision. To respond to recommendations for shared responsibility for care, midwives need to strengthen their shared decision-making skills. The visual model of decision-making in childbirth derived from the data provides a framework for transferring clinical reasoning into practice. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Keeping Teachers in the Center: A Framework of Data-Driven Decision-Making

ERIC Educational Resources Information Center

Light, Daniel; Wexler, Dara H.; Heinze, Juliette

2004-01-01

The Education Development Center's Center for Children and Technology (CCT) conducted a three year study of a large-scale data reporting system, developed by the Grow Network for New York City's Department of Education. This paper presents a framework based on two years of research exploring the intersection of decision-support technologies,…

Using Data Mining to Identify Actionable Information: Breaking New Ground in Data-Driven Decision Making

ERIC Educational Resources Information Center

Streifer, Philip A.; Schumann, Jeffrey A.

2005-01-01

The implementation of No Child Left Behind (NCLB) presents important challenges for schools across the nation to identify problems that lead to poor performance. Yet schools must intervene with instructional programs that can make a difference and evaluate the effectiveness of such programs. New advances in artificial intelligence (AI) data-mining…

In Light of the Limitations of Data-Driven Decision Making

ERIC Educational Resources Information Center

Loeb, Susanna

2012-01-01

Students' experiences and the opportunities they have to learn rest on the quality of education decisions made in each classroom, in each school, in each district, and in each state, federal legislature, and department of education. The role of research and scholarship more broadly in education finance and policy is to inform these decisions for…

Concept of Operations for Collaboration and Discovery from Big Data Across Enterprise Data Warehouses

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

Olama, Mohammed M; Nutaro, James J; Sukumar, Sreenivas R

2013-01-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Optionsmore » that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.« less

Visualization-based decision support for value-driven system design

NASA Astrophysics Data System (ADS)

Tibor, Elliott

In the past 50 years, the military, communication, and transportation systems that permeate our world, have grown exponentially in size and complexity. The development and production of these systems has seen ballooning costs and increased risk. This is particularly critical for the aerospace industry. The inability to deal with growing system complexity is a crippling force in the advancement of engineered systems. Value-Driven Design represents a paradigm shift in the field of design engineering that has potential to help counteract this trend. The philosophy of Value-Driven Design places the desires of the stakeholder at the forefront of the design process to capture true preferences and reveal system alternatives that were never previously thought possible. Modern aerospace engineering design problems are large, complex, and involve multiple levels of decision-making. To find the best design, the decision-maker is often required to analyze hundreds or thousands of combinations of design variables and attributes. Visualization can be used to support these decisions, by communicating large amounts of data in a meaningful way. Understanding the design space, the subsystem relationships, and the design uncertainties is vital to the advancement of Value-Driven Design as an accepted process for the development of more effective, efficient, robust, and elegant aerospace systems. This research investigates the use of multi-dimensional data visualization tools to support decision-making under uncertainty during the Value-Driven Design process. A satellite design system comprising a satellite, ground station, and launch vehicle is used to demonstrate effectiveness of new visualization methods to aid in decision support during complex aerospace system design. These methods are used to facilitate the exploration of the feasible design space by representing the value impact of system attribute changes and comparing the results of multi-objective optimization formulations with a Value-Driven Design formulation. The visualization methods are also used to assist in the decomposition of a value function, by representing attribute sensitivities to aid with trade-off studies. Lastly, visualization is used to enable greater understanding of the subsystem relationships, by displaying derivative-based couplings, and the design uncertainties, through implementation of utility theory. The use of these visualization methods is shown to enhance the decision-making capabilities of the designer by granting them a more holistic view of the complex design space.

Patterns of reasoning and decision making about condom use by urban college students.

PubMed

Patel, V L; Gutnik, L A; Yoskowitz, N A; O'sullivan, L F; Kaufman, D R

2006-11-01

HIV infection rates are rapidly increasing among young heterosexuals, making it increasingly important to understand how these individuals make decisions regarding risk in sexual encounters. Our objective in this study was to characterize young adults' safer sex behaviour and associate this behaviour with patterns of reasoning, using cognitive, information processing methods to understand the process of sexual risk taking. Sixty urban college students from NYC maintained diaries for two weeks and then were interviewed regarding lifetime condom use and sexual history. Using cognitive analysis, we characterized four patterns of condom use behaviour: consistent condom use (35.0%), inconsistent condom use (16.7%), shifting from consistent to inconsistent condom use (35.0%), and shifting from inconsistent to consistent condom use (13.3%). Directionality of reasoning (i.e. data-driven and hypothesis-driven reasoning) was analysed in the explanations provided for condom use decisions. The consistent and inconsistent patterns of condom use were associated with data-driven heuristic reasoning, where behaviour becomes automated and is associated with a high level of confidence in one's judgment. In the other two patterns, the shift in behaviour was due to a significant event that caused a change in type of reasoning to explanation-based reasoning, reflecting feelings of uncertainty and willingness to evaluate their decisions. We discuss these results within the framework of identifying potentially high-risk groups (e.g. heterosexual young adults) as well as intervention strategies for risk reduction. Further, our findings not only identify different patterns of condom use behaviour, but our investigation of the cognitive process of decision-making characterizes the conditions under which such behaviour and reasoning change.

Information, Please.

ERIC Educational Resources Information Center

Hardy, Lawrence

2003-01-01

Requirements of the No Child Left Behind Act present school districts with a massive lesson in data-driven decision-making. Technology companies offer data-management tools that organize student information from state tests. Offers districts advice in choosing a technology provider. (MLF)

Prevention of and Early Intervention for Students with Emotional and Behavioral Disorders: Systems to Support Data-Based Decision Making

ERIC Educational Resources Information Center

Lewis, Timothy J.; Mitchell, Barbara S.

2012-01-01

Students with emotional and behavioral disorders are at great risk for long-term negative outcomes. Researchers and practitioners alike acknowledge the need for evidence-based, preventive, and early intervention strategies. Accordingly, in this chapter an expanded view of prevention is presented as a series of data driven decisions to guide…

Data Analysis and Data-Driven Decision-Making Strategies Implemented by Elementary Teachers in Selected Exited Program Improvement Safe Harbor Schools in Southern California

ERIC Educational Resources Information Center

Senger, Karen

2012-01-01

Purpose: The purposes of this study were to investigate and describe how elementary teachers in exited Program Improvement-Safe Harbor schools acquire student achievement data through assessments, the strategies and reflections utilized to make sense of the data to improve student achievement, ensure curriculum and instructional goals are aligned,…

Data Systems and Reports as Active Participants in Data Interpretation

ERIC Educational Resources Information Center

Rankin, Jenny Grant

2016-01-01

Most data-informed decision-making in education is undermined by flawed interpretations. Educator-driven interventions to improve data use are beneficial but not omnipotent, as data misunderstandings persist at schools and school districts commended for ideal data use support. Meanwhile, most data systems and reports display figures without…

Georgia concrete pavement performance and longevity.

DOT National Transportation Integrated Search

2012-02-01

The Georgia Department of Transportation (GDOT) has effectively utilized its pavement management system (PMS) to make informed, data-driven pavement maintenance decisions, including project selection, project prioritization, and funding allocation. C...

Data-driven medicinal chemistry in the era of big data.

PubMed

Lusher, Scott J; McGuire, Ross; van Schaik, René C; Nicholson, C David; de Vlieg, Jacob

2014-07-01

Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Stream traffic data archival, querying, and analysis with TransDec.

DOT National Transportation Integrated Search

2011-01-01

The goal of research was to extend the traffic data analysis of the TransDec (short for : Transportation Decision-Making) system, which was developed under METRANS 09-26 : research grant. The TransDec system is a real-data driven system to support de...

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.

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education.

PubMed

Hervatis, Vasilis; Loe, Alan; Barman, Linda; O'Donoghue, John; Zary, Nabil

2015-10-06

Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators' decision making. A deductive case study approach was applied to develop the conceptual model. The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach.

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education

PubMed Central

Loe, Alan; Barman, Linda; O'Donoghue, John; Zary, Nabil

2015-01-01

Background Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. Objective The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. Methods A deductive case study approach was applied to develop the conceptual model. Results The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. Conclusions The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach. PMID:27731840

Using Data to Promote Equity

ERIC Educational Resources Information Center

Shum, Brenda

2016-01-01

Data plays a starring role in promoting educational equity, and data-driven decision making begins with good state policies. With the recent passage of the Every Student Succeeds Act (ESSA) and a proposed federal rule to address racial disproportionality in special education, states will shoulder increased responsibility for eliminating…

A Systemic View of Implementing Data Literacy in Educator Preparation

ERIC Educational Resources Information Center

Mandinach, Ellen B.; Gummer, Edith S.

2013-01-01

Data-driven decision making has become increasingly important in education. Policymakers require educators to use data to inform practice. Although the policy emphasis is growing, what has not increased is attention to building human capacity around data use. Educators need to gain data literacy skills to inform practice. Although some…

Simulation of California's Major Reservoirs Outflow Using Data Mining Technique

NASA Astrophysics Data System (ADS)

Yang, T.; Gao, X.; Sorooshian, S.

2014-12-01

The reservoir's outflow is controlled by reservoir operators, which is different from the upstream inflow. The outflow is more important than the reservoir's inflow for the downstream water users. In order to simulate the complicated reservoir operation and extract the outflow decision making patterns for California's 12 major reservoirs, we build a data-driven, computer-based ("artificial intelligent") reservoir decision making tool, using decision regression and classification tree approach. This is a well-developed statistical and graphical modeling methodology in the field of data mining. A shuffled cross validation approach is also employed to extract the outflow decision making patterns and rules based on the selected decision variables (inflow amount, precipitation, timing, water type year etc.). To show the accuracy of the model, a verification study is carried out comparing the model-generated outflow decisions ("artificial intelligent" decisions) with that made by reservoir operators (human decisions). The simulation results show that the machine-generated outflow decisions are very similar to the real reservoir operators' decisions. This conclusion is based on statistical evaluations using the Nash-Sutcliffe test. The proposed model is able to detect the most influential variables and their weights when the reservoir operators make an outflow decision. While the proposed approach was firstly applied and tested on California's 12 major reservoirs, the method is universally adaptable to other reservoir systems.

Action Research and Differentiating Reading Instruction in Mississippi: Fourth-Grade Students' Reading Success

ERIC Educational Resources Information Center

Mims, Wyn, M.; Lockley, Jeannie

2017-01-01

A fourth-grade teacher utilized action research in order to make data-driven decisions about reading interventions with her students. The teacher decided on a broad intervention, which was differentiating reading instruction, implemented differentiated instruction, collected data and continuously adjusted interventions based on monitoring data.…

How Data Use for Accountability Undermines Equitable Science Education

ERIC Educational Resources Information Center

Braaten, Melissa; Bradford, Chris; Kirchgasler, Kathryn L.; Barocas, Sadie Fox

2017-01-01

Purpose: When school leaders advance strategic plans focused on improving educational equity through data-driven decision making, how do policies-as-practiced unfold in the daily work of science teachers? The paper aims to discuss this issue. Design/methodology/approach: This ethnographic study examines how data-centric accountability and…

Local data will help Michigan make better safety investment decisions : research spotlight.

DOT National Transportation Integrated Search

2016-07-01

MDOT staff are aiming to use data-driven processes and practices from the AASHTO Highway Safety Manual (HSM) to estimate the safety impacts of various crash reduction strategies and highway design alternatives, such as adding a median or varying the ...

From Population Databases to Research and Informed Health Decisions and Policy.

PubMed

Machluf, Yossy; Tal, Orna; Navon, Amir; Chaiter, Yoram

2017-01-01

In the era of big data, the medical community is inspired to maximize the utilization and processing of the rapidly expanding medical datasets for clinical-related and policy-driven research. This requires a medical database that can be aggregated, interpreted, and integrated at both the individual and population levels. Policymakers seek data as a lever for wise, evidence-based decision-making and information-driven policy. Yet, bridging the gap between data collection, research, and policymaking, is a major challenge. To bridge this gap, we propose a four-step model: (A) creating a conjoined task force of all relevant parties to declare a national program to promote collaborations; (B) promoting a national digital records project, or at least a network of synchronized and integrated databases, in an accessible transparent manner; (C) creating an interoperative national research environment to enable the analysis of the organized and integrated data and to generate evidence; and (D) utilizing the evidence to improve decision-making, to support a wisely chosen national policy. For the latter purpose, we also developed a novel multidimensional set of criteria to illuminate insights and estimate the risk for future morbidity based on current medical conditions. Used by policymakers, providers of health plans, caregivers, and health organizations, we presume this model will assist transforming evidence generation to support the design of health policy and programs, as well as improved decision-making about health and health care, at all levels: individual, communal, organizational, and national.

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities.

PubMed

Ramanujan, Devarajan; Bernstein, William Z; Chandrasegaran, Senthil K; Ramani, Karthik

2017-01-01

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages-design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

The Principal's Mind-Set for Data

ERIC Educational Resources Information Center

Fox, Dennis

2013-01-01

Is there a school leader anywhere who hasn't been directed, or at least encouraged, to "analyze the data" and practice what has been termed "data-driven decision-making"? Today's principal is expected to be able to skillfully collect, organize, analyze, interpret and use a variety of data in order to improve instruction, services and programs for…

Clinical and regulatory considerations in pharmacogenetic testing.

PubMed

Schuck, Robert N; Marek, Elizabeth; Rogers, Hobart; Pacanowski, Michael

2016-12-01

Both regulatory science and clinical practice rely on best available scientific data to guide decision-making. However, changes in clinical practice may be driven by numerous other factors such as cost. In this review, we reexamine noteworthy examples where pharmacogenetic testing information was added to drug labeling to explore how the available evidence, potential public health impact, and predictive utility of each pharmacogenetic biomarker impacts clinical uptake. Advances in the field of pharmacogenetics have led to new discoveries about the genetic basis for variability in drug response. The Food and Drug Administration recognizes the value of pharmacogenetic testing strategies and has been proactive about incorporating pharmacogenetic information into the labeling of both new drugs and drugs already on the market. Although some examples have readily translated to routine clinical practice, clinical uptake of genetic testing for many drugs has been limited. Both regulatory science and clinical practice rely on data-driven approaches to guide decision making; however, additional factors are also important in clinical practice that do not impact regulatory decision making, and these considerations may result in heterogeneity in clinical uptake of pharmacogenetic testing. Copyright © 2016 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Decision Making Under Objective Risk Conditions-a Review of Cognitive and Emotional Correlates, Strategies, Feedback Processing, and External Influences.

PubMed

Schiebener, Johannes; Brand, Matthias

2015-06-01

While making decisions under objective risk conditions, the probabilities of the consequences of the available options are either provided or calculable. Brand et al. (Neural Networks 19:1266-1276, 2006) introduced a model describing the neuro-cognitive processes involved in such decisions. In this model, executive functions associated with activity in the fronto-striatal loop are important for developing and applying decision-making strategies, and for verifying, adapting, or revising strategies according to feedback. Emotional rewards and punishments learned from such feedback accompany these processes. In this literature review, we found support for the role of executive functions, but also found evidence for the importance of further cognitive abilities in decision making. Moreover, in addition to reflective processing (driven by cognition), decisions can be guided by impulsive processing (driven by anticipation of emotional reward and punishment). Reflective and impulsive processing may interact during decision making, affecting the evaluation of available options, as both processes are affected by feedback. Decision-making processes are furthermore modulated by individual attributes (e.g., age), and external influences (e.g., stressors). Accordingly, we suggest a revised model of decision making under objective risk conditions.

The Myth of the Rational Decision Maker: A Framework for Applying and Enhancing Heuristic and Intuitive Decision Making by School Leaders

ERIC Educational Resources Information Center

Davis, Stephen H.

2004-01-01

This article takes a critical look at administrative decision making in schools and the extent to which complex decisions conform to normative models and common expectations of rationality. An alternative framework for administrative decision making is presented that is informed, but not driven, by theories of rationality. The framework assumes…

Central Office Supports for Data-Driven Talent Management Decisions: Evidence from the Implementation of New Systems for Measuring Teacher Effectiveness

ERIC Educational Resources Information Center

Grissom, Jason A.; Rubin, Mollie; Neumerski, Christine M.; Cannata, Marisa; Drake, Timothy A.; Goldring, Ellen; Schuermann, Patrick

2017-01-01

School districts increasingly push school leaders to utilize multiple measures of teacher effectiveness, such as observation ratings or value-added scores, in making talent management decisions, including teacher hiring, assignment, support, and retention, but we know little about the local conditions that promote or impede these processes. We…

The Role of Human Expertise in Enhancing Data Mining

ERIC Educational Resources Information Center

Kaddouri, Abdelaaziz

2011-01-01

Current data mining (DM) technology is not domain-specific and therefore rarely generates reliable, business actionable knowledge that can be used to improve the effectiveness of the decision-making process in the banking industry. DM is mainly an autonomous, data-driven process with little focus on domain expertise, constraints, or requirements…

"Scrubbing" Data for D3M

ERIC Educational Resources Information Center

Mercurius, Neil

2005-01-01

Data-driven decision-making (D3M) appears to be the new buzz phrase for this century, the information age. On the education front, teachers and administrators are engaging in data-centered dialog in grade-level meetings, lounges, hallways, and classrooms as they brainstorm toward closing the gap in student achievement. Clearly, such discussion…

Toward Data-Driven Education Systems: Insights into Using Information to Measure Results and Manage Change

ERIC Educational Resources Information Center

Custer, Samantha; King, Elizabeth M.; Atinc, Tamar Manuelyan; Read, Lindsay; Sethi, Tanya

2018-01-01

Governments, organizations, and companies are generating copious amounts of data and analysis to support education decision-making around the world. While continued investments in data creation and management are necessary, the ultimate value of information is not in its "production," but its "use." Herein lies one of the…

Coherent District Reform: A Case Study of Two California School Districts

ERIC Educational Resources Information Center

Ezzani, Miriam

2015-01-01

The purpose of this paper is to enhance our understanding of districts that are implementing sustainable professional learning in data-driven decision-making (DDDM) to improve student achievement. The data-informed leadership framework, comprised of leadership practices that acknowledge the complexities that play into data use, guided the inquiry.…

A Crystal Ball for Student Achievement

ERIC Educational Resources Information Center

Pascopella, Angela

2012-01-01

Predicting the future is now in the hands of K12 administrators. While for years districts have collected thousands of pieces of student data, educators have been using them only for data-driven decision-making or formative assessments, which give a "rear-view" perspective only. Now, using predictive analysis--the pulling together of data over…

Statistical Literacy: Data Tell a Story

ERIC Educational Resources Information Center

Sole, Marla A.

2016-01-01

Every day, students collect, organize, and analyze data to make decisions. In this data-driven world, people need to assess how much trust they can place in summary statistics. The results of every survey and the safety of every drug that undergoes a clinical trial depend on the correct application of appropriate statistics. Recognizing the…

Application of process monitoring to anomaly detection in nuclear material processing systems via system-centric event interpretation of data from multiple sensors of varying reliability

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

Garcia, Humberto E.; Simpson, Michael F.; Lin, Wen-Chiao

In this paper, we apply an advanced safeguards approach and associated methods for process monitoring to a hypothetical nuclear material processing system. The assessment regarding the state of the processing facility is conducted at a systemcentric level formulated in a hybrid framework. This utilizes architecture for integrating both time- and event-driven data and analysis for decision making. While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis. By integrating process- and operation-related information and methodologiesmore » within a unified framework, the task of anomaly detection is greatly improved. This is because decision-making can benefit from not only known time-series relationships among measured signals but also from known event sequence relationships among generated events. This available knowledge at both time series and discrete event layers can then be effectively used to synthesize observation solutions that optimally balance sensor and data processing requirements. The application of the proposed approach is then implemented on an illustrative monitored system based on pyroprocessing and results are discussed.« less

Notification Event Architecture for Traveler Screening: Predictive Traveler Screening Using Event Driven Business Process Management

ERIC Educational Resources Information Center

Lynch, John Kenneth

2013-01-01

Using an exploratory model of the 9/11 terrorists, this research investigates the linkages between Event Driven Business Process Management (edBPM) and decision making. Although the literature on the role of technology in efficient and effective decision making is extensive, research has yet to quantify the benefit of using edBPM to aid the…

Assess. Instruct. Repeat.

ERIC Educational Resources Information Center

Demski, Jennifer

2009-01-01

Response to Intervention, or RTI, is a framework for using data to establish the nature and degree of the help a student needs, and then applying strategies targeting those areas. It is a carefully drawn, systematic form of data-driven decision-making that establishes multiple stages of interventions for varying degrees of problems. Though some…

Health assessment and risk mitigation of railroad networks exposed to natural hazards using commercial remote sensing and spatial information technologies.

DOT National Transportation Integrated Search

2017-05-31

The overarching goal of this project was to integrate data from commercial remote sensing and spatial information (CRS&SI) technologies to create a novel data-driven decision making framework that empowers the railroad industry to monitor, assess, an...

Using GIS Tools and Environmental Scanning to Forecast Industry Workforce Needs

ERIC Educational Resources Information Center

Gaertner, Elaine; Fleming, Kevin; Marquez, Michelle

2009-01-01

The Centers of Excellence (COE) provide regional workforce data on high growth, high demand industries and occupations for use by community colleges in program planning and resource enhancement. This article discusses the environmental scanning research methodology and its application to data-driven decision making in community college program…

Data Driven Decision-Making in the Era of Accountability: Fostering Faculty Data Cultures for Learning

ERIC Educational Resources Information Center

Hora, Matthew T.; Bouwma-Gearhart, Jana; Park, Hyoung Joon

2017-01-01

In this article the authors report findings from a practice-based study that examines the cultural practices of data use among 59 science and engineering faculty from three large, public research universities. In this exploratory study they documented how faculty use teaching-related data "in the wild" using interviews and classroom…

Integrating Information & Communications Technologies into the Classroom

ERIC Educational Resources Information Center

Tomei, Lawrence, Ed.

2007-01-01

"Integrating Information & Communications Technologies Into the Classroom" examines topics critical to business, computer science, and information technology education, such as: school improvement and reform, standards-based technology education programs, data-driven decision making, and strategic technology education planning. This book also…

Incorporating Science into Decision-Making

USGS Publications Warehouse

Karl, Herman A.; Turner, Christine E.

2003-01-01

Alan Leshner's Editorial “Public engagement with science” (14 Feb., p. 977) highlights a conundrum: Why is science often ignored in important societal decisions, even as the call for decisions based on sound science escalates? One reason is that decision-making is often driven by a variety of nonscientific, adversarial, and stakeholder dynamics

Ethical and Appropriate Data Use Requires Data Literacy

ERIC Educational Resources Information Center

Mandinach, Ellen B.; Parton, Brennan M.; Gummer, Edith S.; Anderson, Rachel

2015-01-01

Data use should be a continuous, integrated part of practice, a tool that is used all the time. Good teachers have been doing data-driven decision making all along, it just has not been recognized by that term. But there is more work to be done to ensure that educators know how to continuously, effectively, and ethically use data; that is, to help…

The data life cycle applied to our own data.

PubMed

Goben, Abigail; Raszewski, Rebecca

2015-01-01

Increased demand for data-driven decision making is driving the need for librarians to be facile with the data life cycle. This case study follows the migration of reference desk statistics from handwritten to digital format. This shift presented two opportunities: first, the availability of a nonsensitive data set to improve the librarians' understanding of data-management and statistical analysis skills, and second, the use of analytics to directly inform staffing decisions and departmental strategic goals. By working through each step of the data life cycle, library faculty explored data gathering, storage, sharing, and analysis questions.

Strategic Framing: How Leaders Craft the Meaning of Data Use for Equity and Learning

ERIC Educational Resources Information Center

Park, Vicki; Daly, Alan J.; Guerra, Alison Wishard

2013-01-01

Although there is an emerging body of research that examines data-driven decision making (DDDM) in schools, little attention has been paid to how local leaders strategically frame sensemaking around data use. This exploratory case examines how district and school leaders consciously framed the implementation of DDDM in one urban high school.…

Affordances and Constraints in the Context of Teacher Collaboration for the Purpose of Data Use

ERIC Educational Resources Information Center

Datnow, Amanda; Park, Vicki; Kennedy-Lewis, Brianna

2013-01-01

Purpose: An increasing number of schools and districts across the US are requiring teachers to collaborate for the purpose of data-driven decision making. Research suggests that both data use and teacher collaboration are important ingredients in the school improvement process. Existing studies also reveal the complexities of teacher collaboration…

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

ERIC Educational Resources Information Center

Johnson, LeAnne D.

2017-01-01

Data-driven decision making is central to improving success of children. Actualizing the use of data is challenging when addressing the social, emotional, and behavioral needs of children across different types of early childhood programs (i.e., early childhood special education, early childhood family education, Head Start, and childcare).…

The Evolution of Big Data and Learning Analytics in American Higher Education

ERIC Educational Resources Information Center

Picciano, Anthony G.

2012-01-01

Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is…

Involving Teachers in Data-Driven Decision Making: Using Computer Data Systems to Support Teacher Inquiry and Reflection

ERIC Educational Resources Information Center

Wayman, Jeffrey C.

2005-01-01

Accountability mandates such as No Child Left Behind (NCLB) have drawn attention to the practical use of student data for school improvement. Nevertheless, schools may struggle with these mandates because student data are often stored in forms that are difficult to access, manipulate, and interpret. Such access barriers additionally preclude the…

Student decision making in large group discussion

NASA Astrophysics Data System (ADS)

Kustusch, Mary Bridget; Ptak, Corey; Sayre, Eleanor C.; Franklin, Scott V.

2015-04-01

It is increasingly common in physics classes for students to work together to solve problems and perform laboratory experiments. When students work together, they need to negotiate the roles and decision making within the group. We examine how a large group of students negotiates authority as part of their two week summer College Readiness Program at Rochester Institute of Technology. The program is designed to develop metacognitive skills in first generation and Deaf and hard-of-hearing (DHH) STEM undergraduates through cooperative group work, laboratory experimentation, and explicit reflection exercises. On the first full day of the program, the students collaboratively developed a sign for the word ``metacognition'' for which there is not a sign in American Sign Language. This presentation will focus on three aspects of the ensuing discussion: (1) how the instructor communicated expectations about decision making; (2) how the instructor promoted student-driven decision making rather than instructor-driven policy; and (3) one student's shifts in decision making behavior. We conclude by discussing implications of this research for activity-based physics instruction.

Big(ger) Data as Better Data in Open Distance Learning

ERIC Educational Resources Information Center

Prinsloo, Paul; Archer, Elizabeth; Barnes, Glen; Chetty, Yuraisha; van Zyl, Dion

2015-01-01

In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously…

Knowledge Management and the Academy

ERIC Educational Resources Information Center

Cain, Timothy J.; Branin, Joseph J.; Sherman, W. Michael

2008-01-01

Universities and colleges generate extraordinary quantities of knowledge and innovation, but in many ways the academy struggles to keep pace with the digital revolution. Growing pressures are reshaping how universities must do business--students expecting enhanced access and support, administrators eager to make data-driven strategic decisions,…

Deciding for Future Selves Reduces Loss Aversion

PubMed Central

Cheng, Qiqi; He, Guibing

2017-01-01

In this paper, we present an incentivized experiment to investigate the degree of loss aversion when people make decisions for their current selves and future selves under risk. We find that when participants make decisions for their future selves, they are less loss averse compared to when they make decisions for their current selves. This finding is consistent with the interpretation of loss aversion as a bias in decision-making driven by emotions, which are reduced when making decisions for future selves. Our findings endorsed the external validity of previous studies on the impact of emotion on loss aversion in a real world decision-making environment. PMID:28979234

Deciding for Future Selves Reduces Loss Aversion.

PubMed

Cheng, Qiqi; He, Guibing

2017-01-01

In this paper, we present an incentivized experiment to investigate the degree of loss aversion when people make decisions for their current selves and future selves under risk. We find that when participants make decisions for their future selves, they are less loss averse compared to when they make decisions for their current selves. This finding is consistent with the interpretation of loss aversion as a bias in decision-making driven by emotions, which are reduced when making decisions for future selves. Our findings endorsed the external validity of previous studies on the impact of emotion on loss aversion in a real world decision-making environment.

The Role of the Lateral Intraparietal Area in (the Study of) Decision Making.

PubMed

Huk, Alexander C; Katz, Leor N; Yates, Jacob L

2017-07-25

Over the past two decades, neurophysiological responses in the lateral intraparietal area (LIP) have received extensive study for insight into decision making. In a parallel manner, inferred cognitive processes have enriched interpretations of LIP activity. Because of this bidirectional interplay between physiology and cognition, LIP has served as fertile ground for developing quantitative models that link neural activity with decision making. These models stand as some of the most important frameworks for linking brain and mind, and they are now mature enough to be evaluated in finer detail and integrated with other lines of investigation of LIP function. Here, we focus on the relationship between LIP responses and known sensory and motor events in perceptual decision-making tasks, as assessed by correlative and causal methods. The resulting sensorimotor-focused approach offers an account of LIP activity as a multiplexed amalgam of sensory, cognitive, and motor-related activity, with a complex and often indirect relationship to decision processes. Our data-driven focus on multiplexing (and de-multiplexing) of various response components can complement decision-focused models and provides more detailed insight into how neural signals might relate to cognitive processes such as decision making.

Visualization and Analysis for Near-Real-Time Decision Making in Distributed Workflows

DOE PAGES

Pugmire, David; Kress, James; Choi, Jong; ...

2016-08-04

Data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. Data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. Here, this paper discusses initial research into visualization and analysis of distributed datamore » workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.« less

Dysfunctional decision-making in pathological gambling: pattern specificity and the role of impulsivity.

PubMed

Kräplin, Anja; Dshemuchadse, Maja; Behrendt, Silke; Scherbaum, Stefan; Goschke, Thomas; Bühringer, Gerhard

2014-03-30

Dysfunctional decision-making in individuals with pathological gambling (PGs) may result from dominating reward-driven processes, indicated by higher impulsivity. In the current study we examined (1) if PGs show specific decision-making impairments related to dominating reward-driven processes rather than to strategic planning deficits and (2) whether these impairments are related to impulsivity. Nineteen PGs according to DSM-IV and 19 matched control subjects undertook the Cambridge Gambling Task (CGT) to assess decision-making. The delay discounting paradigm (DDP) as well as the UPPS Impulsive Behavior Scale (measuring urgency, premeditation, perseverance and sensation seeking) were administered as multidimensional measures of impulsivity. Results revealed that (1) PGs exhibited higher risk seeking and an immediate reward focus in the CGT and, in contrast, comparable strategic planning to the control group. (2) Decision-making impairments were related to more severe delay discounting and, specifically, to increased urgency and less premeditation. Our findings suggest (1) the necessity to disentangle decision-making components in order to improve etiological models of PGs, and (2) that urgency and premeditation are specifically related to disadvantageous decision-making and should be tackled in intervention strategies focusing on emotion tolerance and control strategies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

How Leaders Can Support Teachers with Data-Driven Decision Making: A Framework for Understanding Capacity Building

ERIC Educational Resources Information Center

Marsh, Julie A.; Farrell, Caitlin C.

2015-01-01

As accountability systems have increased demands for evidence of student learning, the use of data in education has become more prevalent in many countries. Although school and administrative leaders are recognizing the need to provide support to teachers on how to interpret and respond to data, there is little theoretically sound research on…

Optimal data systems: the future of clinical predictions and decision support.

PubMed

Celi, Leo A; Csete, Marie; Stone, David

2014-10-01

The purpose of the review is to describe the evolving concept and role of data as it relates to clinical predictions and decision-making. Critical care medicine is, as an especially data-rich specialty, becoming acutely cognizant not only of its historic deficits in data utilization but also of its enormous potential for capturing, mining, and leveraging such data into well-designed decision support modalities as well as the formulation of robust best practices. Modern electronic medical records create an opportunity to design complete and functional data systems that can support clinical care to a degree never seen before. Such systems are often referred to as 'data-driven,' but a better term is 'optimal data systems' (ODS). Here we discuss basic features of an ODS and its benefits, including the potential to transform clinical prediction and decision support.

Building Capacity for Data-Driven Decision Making in African HIV Testing Programs: Field Perspectives on Data Use Workshops.

PubMed

Courtenay-Quirk, Cari; Spindler, Hilary; Leidich, Aimee; Bachanas, Pam

2016-12-01

Strategic, high quality HIV testing services (HTS) delivery is an essential step towards reaching the end of AIDS by 2030. We conducted HTS Data Use workshops in five African countries to increase data use for strategic program decision-making. Feedback was collected on the extent to which workshop skills and tools were applied in practice and to identify future capacity-building needs. We later conducted six semistructured phone interviews with workshop planning teams and sent a web-based survey to 92 past participants. The HTS Data Use workshops provided accessible tools that were readily learned by most respondents. While most respondents reported increased confidence in interpreting data and frequency of using such tools over time, planning team representatives indicated ongoing needs for more automated tools that can function across data systems. To achieve ambitious global HIV/AIDS targets, national decision makers may continue to seek tools and skill-building opportunities to monitor programs and identify opportunities to refine strategies.

Knowledge management in healthcare: towards 'knowledge-driven' decision-support services.

PubMed

Abidi, S S

2001-09-01

In this paper, we highlight the involvement of Knowledge Management in a healthcare enterprise. We argue that the 'knowledge quotient' of a healthcare enterprise can be enhanced by procuring diverse facets of knowledge from the seemingly placid healthcare data repositories, and subsequently operationalising the procured knowledge to derive a suite of Strategic Healthcare Decision-Support Services that can impact strategic decision-making, planning and management of the healthcare enterprise. In this paper, we firstly present a reference Knowledge Management environment-a Healthcare Enterprise Memory-with the functionality to acquire, share and operationalise the various modalities of healthcare knowledge. Next, we present the functional and architectural specification of a Strategic Healthcare Decision-Support Services Info-structure, which effectuates a synergy between knowledge procurement (vis-à-vis Data Mining) and knowledge operationalisation (vis-à-vis Knowledge Management) techniques to generate a suite of strategic knowledge-driven decision-support services. In conclusion, we argue that the proposed Healthcare Enterprise Memory is an attempt to rethink the possible sources of leverage to improve healthcare delivery, hereby providing a valuable strategic planning and management resource to healthcare policy makers.

Measuring Conditions and Consequences of Tracking in the High School Curriculum

ERIC Educational Resources Information Center

Archbald, Doug; Keleher, Julia

2008-01-01

Despite a decade of advocacy and advances in technology, data driven decision making remains an elusive vision for most high schools. This article identifies key data systems design needs and presents methods for monitoring, managing, and improving programs. Because of its continuing salience, we focus on the issue of tracking (ability grouping).…

Understanding the Voice of the Customer: Practical, Data-Driven Planning and Decision Making for Access Services

ERIC Educational Resources Information Center

Huff-Eibl, Robyn; Miller-Wells, John; Begay, Wendy

2014-01-01

This article describes the process and role frontline access and public service staff play in needs assessment and evaluation of user services, specifically in understanding the voice of the customer. Information includes how the University of Arizona Libraries have incorporated daily data collection into the strategic planning process, resources…

A Framework for Understanding Community Colleges' Organizational Capacity for Data Use: A Convergent Parallel Mixed Methods Study

ERIC Educational Resources Information Center

Kerrigan, Monica Reid

2014-01-01

This convergent parallel design mixed methods case study of four community colleges explores the relationship between organizational capacity and implementation of data-driven decision making (DDDM). The article also illustrates purposive sampling using replication logic for cross-case analysis and the strengths and weaknesses of quantitizing…

Teacher Use of Data to Guide Instructional Practice in Elementary Schools

ERIC Educational Resources Information Center

Burrows, Debra C.

2011-01-01

This descriptive study focused on the degree to which data-driven decision making as envisioned by the NCLB legislation was actually occurring in the elementary schools studied. A multi-stage random sample of six Pennsylvania school districts out of 19 located within the service area of Pennsylvania Intermediate Unit #17, one of 29 regional…

An Investigation of Charter Schools' School Leader and Teacher Level of Assessment Literacy

ERIC Educational Resources Information Center

Pfeiffer-Hoens, Mareen

2017-01-01

Assessment of student performance is one of the most critical responsibilities of school leaders and teachers. Teachers and school leaders must acquire an understanding of assessment literacy for utilizing data to make sound data-driven decisions. The purpose of this descriptive study was to investigate the levels of assessment literacy among…

Quantum ensembles of quantum classifiers.

PubMed

Schuld, Maria; Petruccione, Francesco

2018-02-09

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

Informed Decision Making: Assessment of the Quality of Physician Communication about Prostate Cancer Diagnosis and Treatment.

PubMed

Holmes-Rovner, Margaret; Montgomery, Jeffrey S; Rovner, David R; Scherer, Laura D; Whitfield, Jesse; Kahn, Valerie C; Merkle, Edgar C; Ubel, Peter A; Fagerlin, Angela

2015-11-01

Little is known about how physicians present diagnosis and treatment planning in routine practice in preference-sensitive treatment decisions. We evaluated completeness and quality of informed decision making in localized prostate cancer post biopsy encounters. We analyzed audio-recorded office visits of 252 men with presumed localized prostate cancer (Gleason 6 and Gleason 7 scores) who were seeing 45 physicians at 4 Veterans Affairs Medical Centers. Data were collected between September 2008 and May 2012 in a trial of 2 decision aids (DAs). Braddock's previously validated Informed Decision Making (IDM) system was used to measure quality. Latent variable models for ordinal data examined the relationship of IDM score to treatment received. Mean IDM score showed modest quality (7.61±2.45 out of 18) and high variability. Treatment choice and risks and benefits were discussed in approximately 95% of encounters. However, in more than one-third of encounters, physicians provided a partial set of treatment options and omitted surveillance as a choice. Informing quality was greater in patients treated with surveillance (β = 1.1, p = .04). Gleason score (7 vs 6) and lower age were often cited as reasons to exclude surveillance. Patient preferences were elicited in the majority of cases, but not used to guide treatment planning. Encounter time was modestly correlated with IDM score (r = 0.237, p = .01). DA type was not associated with IDM score. Physicians informed patients of options and risks and benefits, but infrequently engaged patients in core shared decision-making processes. Despite patients having received DAs, physicians rarely provided an opportunity for preference-driven decision making. More attention to the underused patient decision-making and engagement elements could result in improved shared decision making. © The Author(s) 2015.

Building an Evidence-Driven Child Welfare Workforce: A University–Agency Partnership

PubMed Central

Lery, Bridgette; Wiegmann, Wendy; Berrick, Jill Duerr

2016-01-01

The federal government increasingly expects child welfare systems to be more responsive to the needs of their local populations, connect strategies to results, and use continuous quality improvement (CQI) to accomplish these goals. A method for improving decision making, CQI relies on an inflow of high-quality data, up-to-date research evidence, and a robust organizational structure and climate that supports the deliberate use of evidence for decision making. This article describes an effort to build and support these essential system components through one public-private child welfare agency–university partnership. PMID:27429534

Odyssey Reading. What Works Clearinghouse Intervention Report

ERIC Educational Resources Information Center

What Works Clearinghouse, 2012

2012-01-01

"Odyssey Reading," published by CompassLearning[R], is a web-based K-12 reading/language arts program designed to allow for instructional differentiation and data-driven decision making. The online program includes electronic curricula and materials for individual or small-group work, assessments aligned with state curriculum standards,…

Examining Construct Validity of the Quantitative Literacy VALUE Rubric in College-Level STEM Assignments

ERIC Educational Resources Information Center

Gray, Julie S.; Brown, Melissa A.; Connolly, John P.

2017-01-01

Data-driven decision making is increasingly viewed as essential in a globally competitive society. Initiatives to augment standardized testing with performance-based assessment have increased as educators progressively respond to mandates for authentic measurement of student attainment. To meet this challenge, multidisciplinary rubrics were…

Accountability for Results: The Realities of Data-Driven Decision Making

ERIC Educational Resources Information Center

McCaw, Donna; Watkins, Sandra

2007-01-01

The format of this book addresses the most salient questions administrators, school board members, and community stakeholders need to ask to ensure academic and fiscal accountability, providing definitions, background information and the current research. Readers will be provided with sufficient knowledge to effectively question the financial…

The Impact of Data-Driven Decision Making on Educational Practice in Louisiana Schools

ERIC Educational Resources Information Center

James-Maxie, Dana

2012-01-01

Using data to improve educational practice in schools has become a popular reform strategy that has grown as a result of the No Child Left Behind Act of 2001. Districts and schools across the United States are under a great deal of pressure to collect and analyze data in hopes of identifying student weaknesses to implement corrective action plans…

Decisional strategy determines whether frame influences treatment preferences for medical decisions.

PubMed

Woodhead, Erin L; Lynch, Elizabeth B; Edelstein, Barry A

2011-06-01

Decision makers are influenced by the frame of information such that preferences vary depending on whether survival or mortality data are presented. Research is inconsistent as to whether and how age impacts framing effects. This paper presents two studies that used qualitative analyses of think-aloud protocols to understand how the type of information used in the decision making process varies by frame and age. In Study 1, 40 older adults, age 65 to 89, and 40 younger adults, age 18 to 24, responded to a hypothetical lung cancer scenario in a within-subject design. Participants received both a survival and mortality frame. Qualitative analyses revealed that two main decisional strategies were used by all participants: one strategy reflected a data-driven decisional process, whereas the other reflected an experience-driven process. Age predicted decisional strategy, with older adults less likely to use a data-driven strategy. Frame interacted with strategy to predict treatment choice; only those using a data-driven strategy demonstrated framing effects. In Study 2, 61 older adults, age 65 to 98, and 63 younger adults, age 18 to 30, responded to the same scenarios as in Study 1 in a between-subject design. The results of Study 1 were replicated, with age significantly predicting decisional strategy and frame interacting with strategy to predict treatment choice. Findings suggest that framing effects may be more related to decisional strategy than to age. (c) 2011 APA, all rights reserved.

A Mixed Methods Bounded Case Study: Data-Driven Decision Making within Professional Learning Communities for Response to Intervention

ERIC Educational Resources Information Center

Rodriguez, Gabriel R.

2017-01-01

A growing number of schools are implementing PLCs to address school improvement, staff engage with data to identify student needs and determine instructional interventions. This is a starting point for engaging in the iterative process of learning for the teach in order to increase student learning (Hord & Sommers, 2008). The iterative process…

An Academic Innovation: The Executive Ph.D. in Urban Higher Education at a Historically Black University

ERIC Educational Resources Information Center

Stevenson, Joseph Martin; Payne, Alfredda Hunt

2016-01-01

This chapter describes how data analysis and data-driven decision making were critical for designing, developing, and assessing a new academic program. The authors--one, the program's founder; the other, an alumna--begin by highlighting some of the elements in the program's incubation and, subsequently, describe some of the components for data…

Conditions for Effective Data Use to Improve Schools: Recommendations for School Leaders

ERIC Educational Resources Information Center

Lange, Christine; Range, Bret; Welsh, Kate

2012-01-01

Although data driven-decision making has been the mantra of school reform for the last 10 years, school leaders benefit from frequent discussions in how to engage teachers in the process. As a result, the purpose of this paper is to apply Reeves' (2004) framework concerning Antecedents of Excellence in creating a school culture that routinely uses…

Capacity Enablers and Barriers for Learning Analytics: Implications for Policy and Practice

ERIC Educational Resources Information Center

Wolf, Mary Ann; Jones, Rachel; Hall, Sara; Wise, Bob

2014-01-01

The field of learning analytics is being discussed in many circles as an emerging concept in education. In many districts and states, the core philosophy behind learning analytics is not entirely new; for more than a decade, discussions of data-driven decision making and the use of data to drive instruction have been common. Still, the U.S.…

Age-related differences in advantageous decision making are associated with distinct differences in functional community structure.

PubMed

Moussa, Malaak Nasser; Wesley, Michael J; Porrino, Linda J; Hayasaka, Satoru; Bechara, Antoine; Burdette, Jonathan H; Laurienti, Paul J

2014-04-01

Human decision making is dependent on not only the function of several brain regions but also their synergistic interaction. The specific function of brain areas within the ventromedial prefrontal cortex has long been studied in an effort to understand choice evaluation and decision making. These data specifically focus on whole-brain functional interconnectivity using the principles of network science. The Iowa Gambling Task (IGT) was the first neuropsychological task used to model real-life decisions in a way that factors reward, punishment, and uncertainty. Clinically, it has been used to detect decision-making impairments characteristic of patients with prefrontal cortex lesions. Here, we used performance on repeated blocks of the IGT as a behavioral measure of advantageous and disadvantageous decision making in young and mature adults. Both adult groups performed poorly by predominately making disadvantageous selections in the beginning stages of the task. In later phases of the task, young adults shifted to more advantageous selections and outperformed mature adults. Modularity analysis revealed stark underlying differences in visual, sensorimotor and medial prefrontal cortex community structure. In addition, changes in orbitofrontal cortex connectivity predicted behavioral deficits in IGT performance. Contrasts were driven by a difference in age but may also prove relevant to neuropsychiatric disorders associated with poor decision making, including the vulnerability to alcohol and/or drug addiction.

A Neural Signature Encoding Decisions under Perceptual Ambiguity

PubMed Central

Sun, Sai; Yu, Rongjun

2017-01-01

Abstract People often make perceptual decisions with ambiguous information, but it remains unclear whether the brain has a common neural substrate that encodes various forms of perceptual ambiguity. Here, we used three types of perceptually ambiguous stimuli as well as task instructions to examine the neural basis for both stimulus-driven and task-driven perceptual ambiguity. We identified a neural signature, the late positive potential (LPP), that encoded a general form of stimulus-driven perceptual ambiguity. In addition to stimulus-driven ambiguity, the LPP was also modulated by ambiguity in task instructions. To further specify the functional role of the LPP and elucidate the relationship between stimulus ambiguity, behavioral response, and the LPP, we employed regression models and found that the LPP was specifically associated with response latency and confidence rating, suggesting that the LPP encoded decisions under perceptual ambiguity. Finally, direct behavioral ratings of stimulus and task ambiguity confirmed our neurophysiological findings, which could not be attributed to differences in eye movements either. Together, our findings argue for a common neural signature that encodes decisions under perceptual ambiguity but is subject to the modulation of task ambiguity. Our results represent an essential first step toward a complete neural understanding of human perceptual decision making. PMID:29177189

A Neural Signature Encoding Decisions under Perceptual Ambiguity.

PubMed

Sun, Sai; Yu, Rongjun; Wang, Shuo

2017-01-01

People often make perceptual decisions with ambiguous information, but it remains unclear whether the brain has a common neural substrate that encodes various forms of perceptual ambiguity. Here, we used three types of perceptually ambiguous stimuli as well as task instructions to examine the neural basis for both stimulus-driven and task-driven perceptual ambiguity. We identified a neural signature, the late positive potential (LPP), that encoded a general form of stimulus-driven perceptual ambiguity. In addition to stimulus-driven ambiguity, the LPP was also modulated by ambiguity in task instructions. To further specify the functional role of the LPP and elucidate the relationship between stimulus ambiguity, behavioral response, and the LPP, we employed regression models and found that the LPP was specifically associated with response latency and confidence rating, suggesting that the LPP encoded decisions under perceptual ambiguity. Finally, direct behavioral ratings of stimulus and task ambiguity confirmed our neurophysiological findings, which could not be attributed to differences in eye movements either. Together, our findings argue for a common neural signature that encodes decisions under perceptual ambiguity but is subject to the modulation of task ambiguity. Our results represent an essential first step toward a complete neural understanding of human perceptual decision making.

A rational framework for production decision making in blood establishments.

PubMed

Ramoa, Augusto; Maia, Salomé; Lourenço, Anália

2012-07-24

SAD_BaSe is a blood bank data analysis software, created to assist in the management of blood donations and the blood production chain in blood establishments. In particular, the system keeps track of several collection and production indicators, enables the definition of collection and production strategies, and the measurement of quality indicators required by the Quality Management System regulating the general operation of blood establishments. This paper describes the general scenario of blood establishments and its main requirements in terms of data management and analysis. It presents the architecture of SAD_BaSe and identifies its main contributions. Specifically, it brings forward the generation of customized reports driven by decision making needs and the use of data mining techniques in the analysis of donor suspensions and donation discards.

A Rational Framework for Production Decision Making in Blood Establishments.

PubMed

Ramoa, Augusto; Maia, Salomé; Lourenço, Anália

2012-12-01

SAD_BaSe is a blood bank data analysis software, created to assist in the management of blood donations and the blood production chain in blood establishments. In particular, the system keeps track of several collection and production indicators, enables the definition of collection and production strategies, and the measurement of quality indicators required by the Quality Management System regulating the general operation of blood establishments. This paper describes the general scenario of blood establishments and its main requirements in terms of data management and analysis. It presents the architecture of SAD_BaSe and identifies its main contributions. Specifically, it brings forward the generation of customized reports driven by decision making needs and the use of data mining techniques in the analysis of donor suspensions and donation discards.

Evidence-informed decision making for nutrition: African experiences and way forward.

PubMed

Aryeetey, Richmond; Holdsworth, Michelle; Taljaard, Christine; Hounkpatin, Waliou Amoussa; Colecraft, Esi; Lachat, Carl; Nago, Eunice; Hailu, Tesfaye; Kolsteren, Patrick; Verstraeten, Roos

2017-11-01

Although substantial amount of nutrition research is conducted in Africa, the research agenda is mainly donor-driven. There is a clear need for a revised research agenda in Africa which is both driven by and responding to local priorities. The present paper summarises proceedings of a symposium on how evidence can guide decision makers towards context-appropriate priorities and decisions in nutrition. The paper focuses on lessons learnt from case studies by the Evidence Informed Decision Making in Nutrition and Health Network implemented between 2015 and 2016 in Benin, Ghana and South Africa. Activities within these countries were organised around problem-oriented evidence-informed decision-making (EIDM), capacity strengthening and leadership and horizontal collaboration. Using a combination of desk-reviews, stakeholder influence-mapping, semi-structured interviews and convening platforms, these country-level studies demonstrated strong interest for partnership between researchers and decision makers, and use of research evidence for prioritisation and decision making in nutrition. Identified capacity gaps were addressed through training workshops on EIDM, systematic reviews, cost-benefit evaluations and evidence contextualisation. Investing in knowledge partnerships and development of capacity and leadership are key to drive appropriate use of evidence in nutrition policy and programming in Africa.

Factors That Affect a School District's Ability to Successfully Implement the Use of Data Warehouse Applications in the Data Driven Decision Making Process

ERIC Educational Resources Information Center

DeLoach, Robin

2012-01-01

The purpose of this study was to explore the factors that influence the ability of teachers and administrators to use data obtained from a data warehouse to inform instruction. The mixed methods study was guided by the following questions: 1) What data warehouse application features affect the ability of an educator to effectively use the…

Optimal information networks: Application for data-driven integrated health in populations

PubMed Central

Servadio, Joseph L.; Convertino, Matteo

2018-01-01

Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables, instead considering only individual correlations. In addition, a unified method for assessing integrated health statuses of populations is lacking, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator, representing integrated health in a city. PMID:29423440

Clinical Note Creation, Binning, and Artificial Intelligence

PubMed Central

Deliberato, Rodrigo Octávio; Stone, David J

2017-01-01

The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans. PMID:28778845

Revisiting Statistical Aspects of Nuclear Material Accounting

DOE PAGES

Burr, T.; Hamada, M. S.

2013-01-01

Nuclear material accounting (NMA) is the only safeguards system whose benefits are routinely quantified. Process monitoring (PM) is another safeguards system that is increasingly used, and one challenge is how to quantify its benefit. This paper considers PM in the role of enabling frequent NMA, which is referred to as near-real-time accounting (NRTA). We quantify NRTA benefits using period-driven and data-driven testing. Period-driven testing makes a decision to alarm or not at fixed periods. Data-driven testing decides as the data arrives whether to alarm or continue testing. The difference between period-driven and datad-riven viewpoints is illustrated by using one-year andmore » two-year periods. For both one-year and two-year periods, period-driven NMA using once-per-year cumulative material unaccounted for (CUMUF) testing is compared to more frequent Shewhart and joint sequential cusum testing using either MUF or standardized, independently transformed MUF (SITMUF) data. We show that the data-driven viewpoint is appropriate for NRTA and that it can be used to compare safeguards effectiveness. In addition to providing period-driven and data-driven viewpoints, new features include assessing the impact of uncertainty in the estimated covariance matrix of the MUF sequence and the impact of both random and systematic measurement errors.« less

Modern data-driven decision support systems: the role of computing with words and computational linguistics

NASA Astrophysics Data System (ADS)

Kacprzyk, Janusz; Zadrożny, Sławomir

2010-05-01

We present how the conceptually and numerically simple concept of a fuzzy linguistic database summary can be a very powerful tool for gaining much insight into the very essence of data. The use of linguistic summaries provides tools for the verbalisation of data analysis (mining) results which, in addition to the more commonly used visualisation, e.g. via a graphical user interface, can contribute to an increased human consistency and ease of use, notably for supporting decision makers via the data-driven decision support system paradigm. Two new relevant aspects of the analysis are also outlined which were first initiated by the authors. First, following Kacprzyk and Zadrożny, it is further considered how linguistic data summarisation is closely related to some types of solutions used in natural language generation (NLG). This can make it possible to use more and more effective and efficient tools and techniques developed in NLG. Second, similar remarks are given on relations to systemic functional linguistics. Moreover, following Kacprzyk and Zadrożny, comments are given on an extremely relevant aspect of scalability of linguistic summarisation of data, using a new concept of a conceptual scalability.

A Framework for Facilitating Ecosystem Services-based Decision-making and the Development of Decision-making Tools

EPA Science Inventory

There is an increasing understanding that top-down regulatory and technology driven responses are not sufficient to address current and emerging environmental challenges such as climate change, sustainable communities, and environmental justice. The vast majority of environmenta...

Seriously Data-Driven Decision Making

ERIC Educational Resources Information Center

Casserly, Michael D.

2011-01-01

As states approach the funding cliff marking the end of federal stimulus help for education, school districts will be feeling more financial pain than they're experiencing now. But there's good news amid the bad: Big city districts are showing schools nationwide a way to save money and improve efficiency by working together. They've created the…

Clinical Reasoning in the Assessment and Intervention Planning for Writing Disorder

ERIC Educational Resources Information Center

Harrison, Gina L.; McManus, Kelly L.

2017-01-01

The incidence of writing disorder is as common as reading disorder, but it is frequently under-identified and rarely targeted for intervention. Increasing clinical understanding on various subtypes of writing disorder through assessment guided by data-driven decision making may alleviate this disparity for students with writing disorders. The…

Internal Consistency and Cross-Informant Agreement of the Lithuanian-Translated Behavioral and Emotional Rating Scale

ERIC Educational Resources Information Center

Sointu, Erkko T.; Geležiniene, Renata; Lambert, Matthew C.; Nordness, Philip D.

2015-01-01

Educational professionals need assessments that yield psychometrically sound scores to assess students' behavioral and emotional functioning in order to guide data-driven decision-making processes. Rating scales have been found to be effective and economical, and often multiple informant perspectives can be obtained. The agreement between multiple…

Dare to Share

ERIC Educational Resources Information Center

Briggs, Linda L.

2007-01-01

Today, as difficult as it is for large institutions to keep software and hardware up-to-date, the challenge and expense of keeping up is only amplified for smaller colleges and universities. In the area of data-driven decision-making (DDD), the challenge can be even greater. Because smaller schools are pressed for time and resources on nearly all…

Breaking the Habit of Low Performance: Successful School Restructuring Stories

ERIC Educational Resources Information Center

Brinson, Dana; Rhim, Lauren Morando

2009-01-01

The components of a successful school are clear. Many educators can easily list them: high expectations for all students, a safe and orderly learning environment, strong instructional leadership, highly qualified teachers, data-driven decision making, etc. Then why don't more schools change the what they are doing to mirror them? Knowing the…

A Predictive Model of Inquiry to Enrollment

ERIC Educational Resources Information Center

Goenner, Cullen F.; Pauls, Kenton

2006-01-01

The purpose of this paper is to build a predictive model of enrollment that provides data driven analysis to improve undergraduate recruitment efforts. We utilize an inquiry model, which examines the enrollment decisions of students that have made contact with our institution, a medium sized, public, Doctoral I university. A student, who makes an…

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.

Enhanced Risk Aversion, But Not Loss Aversion, in Unmedicated Pathological Anxiety.

PubMed

Charpentier, Caroline J; Aylward, Jessica; Roiser, Jonathan P; Robinson, Oliver J

2017-06-15

Anxiety disorders are associated with disruptions in both emotional processing and decision making. As a result, anxious individuals often make decisions that favor harm avoidance. However, this bias could be driven by enhanced aversion to uncertainty about the decision outcome (e.g., risk) or aversion to negative outcomes (e.g., loss). Distinguishing between these possibilities may provide a better cognitive understanding of anxiety disorders and hence inform treatment strategies. To address this question, unmedicated individuals with pathological anxiety (n = 25) and matched healthy control subjects (n = 23) completed a gambling task featuring a decision between a gamble and a safe (certain) option on every trial. Choices on one type of gamble-involving weighing a potential win against a potential loss (mixed)-could be driven by both loss and risk aversion, whereas choices on the other type-featuring only wins (gain only)-were exclusively driven by risk aversion. By fitting a computational prospect theory model to participants' choices, we were able to reliably estimate risk and loss aversion and their respective contribution to gambling decisions. Relative to healthy control subjects, pathologically anxious participants exhibited enhanced risk aversion but equivalent levels of loss aversion. Individuals with pathological anxiety demonstrate clear avoidance biases in their decision making. These findings suggest that this may be driven by a reduced propensity to take risks rather than a stronger aversion to losses. This important clarification suggests that psychological interventions for anxiety should focus on reducing risk sensitivity rather than reducing sensitivity to negative outcomes per se. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

From Vision to Action: How School Districts Use Data to Improve Performance. 3D: Data-Driven Decision Making

ERIC Educational Resources Information Center

Henke, Karen Greenwood

2005-01-01

With the passage of "No Child Left Behind" in 2001, schools are expected to provide a standards-based curriculum for students to attain math and reading proficiency and demonstrate progress each year. "NCLB" requires more frequent student testing with publicly reported results in an effort to close the achievement gap and to inform parents,…

Neural basis of quasi-rational decision making.

PubMed

Lee, Daeyeol

2006-04-01

Standard economic theories conceive homo economicus as a rational decision maker capable of maximizing utility. In reality, however, people tend to approximate optimal decision-making strategies through a collection of heuristic routines. Some of these routines are driven by emotional processes, and others are adjusted iteratively through experience. In addition, routines specialized for social decision making, such as inference about the mental states of other decision makers, might share their origins and neural mechanisms with the ability to simulate or imagine outcomes expected from alternative actions that an individual can take. A recent surge of collaborations across economics, psychology and neuroscience has provided new insights into how such multiple elements of decision making interact in the brain.

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.

Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

NASA Technical Reports Server (NTRS)

Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

2009-01-01

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

Hypothesis-Driven Story Building: Counteracting Human Cognitive Biases to Improve Medical Diagnosis Support

ERIC Educational Resources Information Center

Zhu, Shizhuo

2010-01-01

Clinical decision-making is challenging mainly because of two factors: (1) patient conditions are often complicated with partial and changing information; (2) people have cognitive biases in their decision-making and information-seeking. Consequentially, misdiagnoses and ineffective use of resources may happen. To better support clinical…

Entrepreneurial Decision Making and Institutional Governance within the Academy: A Case Study

ERIC Educational Resources Information Center

French, Edward F.

2011-01-01

This case study explored the relationship between entrepreneurial decision making and optimal institutional governance. The study focused on a single institution, characterized as a small, tuition-driven, private institution. Twelve participants were interviewed in the study, equally divided between members of the faculty and of the…

Augmented Personalized Health: How Smart Data with IoTs and AI is about to Change Healthcare

PubMed Central

Sheth, Amit; Jaimini, Utkarshani; Thirunarayan, Krishnaprasad; Banerjee, Tanvi

2017-01-01

Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data driven. While ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. This paper outlines current opportunities and challenges, with a focus on key AI approaches to make this a reality. The broader vision is exemplified using three ongoing applications (asthma in children, bariatric surgery, and pain management) as part of the Kno.e.sis kHealth personalized digital health initiative. PMID:29399675

Augmented Personalized Health: How Smart Data with IoTs and AI is about to Change Healthcare.

PubMed

Sheth, Amit; Jaimini, Utkarshani; Thirunarayan, Krishnaprasad; Banerjee, Tanvi

2017-09-01

Healthcare as we know it is in the process of going through a massive change - from episodic to continuous, from disease focused to wellness and quality of life focused, from clinic centric to anywhere a patient is, from clinician controlled to patient empowered, and from being driven by limited data to 360-degree, multimodal personal-public-population physical-cyber-social big data driven. While ability to create and capture data is already here, the upcoming innovations will be in converting this big data into smart data through contextual and personalized processing such that patients and clinicians can make better decisions and take timely actions for augmented personalized health. This paper outlines current opportunities and challenges, with a focus on key AI approaches to make this a reality. The broader vision is exemplified using three ongoing applications (asthma in children, bariatric surgery, and pain management) as part of the Kno.e.sis kHealth personalized digital health initiative.

Innovation in Data-Driven Decision Making within SWPBIS Systems: Welcome to the Gallery Walk

ERIC Educational Resources Information Center

Kennedy, Michael J.; Mimmack, Jody; Flannery, K. Brigid

2012-01-01

Schools implementing school-wide positive behavioral interventions and supports (SWPBIS) at the high school level face the same challenges as elementary and middle schools, but also encounter an additional set of barriers all their own. To name but a few, these barriers include the need to focus on dropout prevention, postsecondary outcomes,…

Multi-Dimensional Education: A Common Sense Approach to Data-Driven Thinking

ERIC Educational Resources Information Center

Corrigan, Michael W.; Grove, Doug; Vincent, Philip F.

2011-01-01

Schools aren't one dimensional. Your decision making shouldn't be either. If you want to look beyond student test scores to identify the specific areas that need improvement in your school, you will find practical tools for assessing multiple areas with confidence here. The authors detail a step-by-step framework for identifying, collecting,…

Building an Evidence-Driven Child Welfare Workforce: A University-Agency Partnership

ERIC Educational Resources Information Center

Lery, Bridgette; Wiegmann, Wendy; Berrick, Jill Duerr

2015-01-01

The federal government increasingly expects child welfare systems to be more responsive to the needs of their local populations, connect strategies to results, and use continuous quality improvement (CQI) to accomplish these goals. A method for improving decision making, CQI relies on an inflow of high-quality data, up-to-date research evidence,…

Racism? Administrative and Community Perspectives in Data-Driven Decision Making: Systemic Perspectives versus Technical-Rational Perspectives

ERIC Educational Resources Information Center

Khalifa, Muhammad A.; Jennings, Michael E.; Briscoe, Felecia; Oleszweski, Ashley M.; Abdi, Nimo

2014-01-01

This case study describes tensions that became apparent between community members and school administrators after a proposal to close a historically African American public high school in a large urban Southwestern city. When members of the city's longstanding African American community responded with outrage, the school district's senior…

Data-Driven Decision Making as a Tool to Improve Software Development Productivity

ERIC Educational Resources Information Center

Brown, Mary Erin

2013-01-01

The worldwide software project failure rate, based on a survey of information technology software manager's view of user satisfaction, product quality, and staff productivity, is estimated to be between 24% and 36% and software project success has not kept pace with the advances in hardware. The problem addressed by this study was the limited…

The Department of Homeland Security’s Pursuit of Data-Driven Decision Making

DTIC Science & Technology

2015-12-01

agencies’ information management systems pertaining to mission support and business operations 1 KT...Directorate’s operating environment. xviii managed . Meanwhile, adding to the intrinsic organizational change management challenges is the idea that...a timely manner. The lack of a single, enterprise-wide information management system has resulted in numerous, disparate systems operating within

First, Get Their Attention: Getting Your Results Used. Professional File. Number 122, Fall 2011

ERIC Educational Resources Information Center

Leimer, Christina

2011-01-01

Fostering data-driven decision-making is not an easy task, nor is getting busy people's attention in this age of information overload. How we write about and disseminate our findings can help. Writing to the audience, timing, formatting, choice of medium, and connecting results to institutional goals and current, even controversial, issues are…

From Data to Improved Decisions: Operations Research in Healthcare Delivery.

PubMed

Capan, Muge; Khojandi, Anahita; Denton, Brian T; Williams, Kimberly D; Ayer, Turgay; Chhatwal, Jagpreet; Kurt, Murat; Lobo, Jennifer Mason; Roberts, Mark S; Zaric, Greg; Zhang, Shengfan; Schwartz, J Sanford

2017-11-01

The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.

Data Analysis and Data Mining: Current Issues in Biomedical Informatics

PubMed Central

Bellazzi, Riccardo; Diomidous, Marianna; Sarkar, Indra Neil; Takabayashi, Katsuhiko; Ziegler, Andreas; McCray, Alexa T.

2011-01-01

Summary Background Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. Objectives To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, that reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. Results The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. Conclusions Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers. PMID:22146916

Graphics to facilitate informative discussion and team decision making

DOE PAGES

Anderson-Cook, Christine M.; Lu, Lu

2018-03-25

Everyone knows the expression “A picture is worth a thousand words,” and this effectively summarizes the ability of graphical summaries to convey information and persuade. However, in many cases, the goal for the right visualization is to encourage and guide discussion while helping focus a team to make carefully considered, defensible, and data-driven decisions. The aims of graphics differ if we are trying to communicate the merits of a single choice versus outlining several contending alternatives for further comparison and discussion. These choices each have their own strengths and weaknesses depending on how we value different criteria. They also servemore » different purposes at various stages of decision making. Often the role of statisticians is not to provide a single answer but to provide rich information and summaries in a manageable and compact form to enable productive discussion among team members. Through a series of diverse examples, this work present principles and strategies for encouraging discussion and informed decision making and discuss how they can be integrated with versatile use of graphical tools for examining multiple objectives, framing trade-offs between alternatives, and examining the impact of subjective priorities and uncertainty on the final decision.« less

Graphics to facilitate informative discussion and team decision making

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

Anderson-Cook, Christine M.; Lu, Lu

Everyone knows the expression “A picture is worth a thousand words,” and this effectively summarizes the ability of graphical summaries to convey information and persuade. However, in many cases, the goal for the right visualization is to encourage and guide discussion while helping focus a team to make carefully considered, defensible, and data-driven decisions. The aims of graphics differ if we are trying to communicate the merits of a single choice versus outlining several contending alternatives for further comparison and discussion. These choices each have their own strengths and weaknesses depending on how we value different criteria. They also servemore » different purposes at various stages of decision making. Often the role of statisticians is not to provide a single answer but to provide rich information and summaries in a manageable and compact form to enable productive discussion among team members. Through a series of diverse examples, this work present principles and strategies for encouraging discussion and informed decision making and discuss how they can be integrated with versatile use of graphical tools for examining multiple objectives, framing trade-offs between alternatives, and examining the impact of subjective priorities and uncertainty on the final decision.« less

Using the Alaska Ocean Observing System to Inform Decision Making for Coastal Resiliency Relating to Inundation, Ocean Acidification, Harmful Algal Blooms, Navigation Safety and Impacts of Vessel Traffic

NASA Astrophysics Data System (ADS)

McCammon, M.

2017-12-01

State and federal agencies, coastal communities and Alaska Native residents, and non-governmental organizations are increasingly turning to the Alaska Ocean Observing System (AOOS) as a major source of ocean and coastal data and information products to inform decision making relating to a changing Arctic. AOOS implements its mission to provide ocean observing data and information to meet stakeholder needs by ensuring that all programs are "science based, stakeholder driven and policy neutral." Priority goals are to increase access to existing coastal and ocean data; package information and data in useful ways to meet stakeholder needs; and increase observing and forecasting capacity in all regions of the state. Recently certified by NOAA, the AOOS Data Assembly Center houses the largest collection of real-time ocean and coastal data, environmental models, and biological data in Alaska, and develops tools and applications to make it more publicly accessible and useful. Given the paucity of observations in the Alaska Arctic, the challenge is how to make decisions with little data compared to other areas of the U.S. coastline. AOOS addresses this issue by: integrating and visualizing existing data; developing data and information products and tools to make data more useful; serving as a convener role in areas such as coastal inundation and flooding, impacts of warming temperatures on food security, ocean acidification, observing technologies and capacity; and facilitating planning efforts to increase observations. In this presentation, I will give examples of each of these efforts, lessons learned, and suggestions for future actions.

Automation for deep space vehicle monitoring

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.

1991-01-01

Information on automation for deep space vehicle monitoring is given in viewgraph form. Information is given on automation goals and strategy; the Monitor Analyzer of Real-time Voyager Engineering Link (MARVEL); intelligent input data management; decision theory for making tradeoffs; dynamic tradeoff evaluation; evaluation of anomaly detection results; evaluation of data management methods; system level analysis with cooperating expert systems; the distributed architecture of multiple expert systems; and event driven response.

Current understanding of decision-making in adolescents with cancer: A narrative systematic review

PubMed Central

Day, Emma; Jones, Louise; Langner, Richard; Bluebond-Langner, Myra

2016-01-01

Background: Policy guidance and bioethical literature urge the involvement of adolescents in decisions about their healthcare. It is uncertain how roles and expectations of adolescents, parents and healthcare professionals influence decision-making and to what extent this is considered in guidance. Aims: To identify recent empirical research on decision-making regarding care and treatment in adolescent cancer: (1) to synthesise evidence to define the role of adolescents, parents and healthcare professionals in the decision-making process and (2) to identify gaps in research. Design: A narrative systematic review of qualitative, quantitative and mixed-methods research. We adopted a textual approach to synthesis, using a theoretical framework of interactionism to interpret findings. Data Sources: The databases MEDLINE, PsycINFO, SCOPUS, EMBASE and CINHAL were searched from 2001 through May 2015 for publications on decision-making for adolescents (13–19 years) with cancer. Results: Twenty-eight articles were identified. Adolescents and parents initially find it difficult to participate in decision-making due to a lack of options in the face of protocol-driven care. Parent and adolescent preferences for information and response to loss of control vary between individuals and over time. No studies indicate parental or adolescent preference for a high degree of independence in decision-making. Conclusion: Striving to make parents and adolescents fully informed or urge them towards more independence than they prefer may add to distress and confusion. This may interfere with their ability to participate in their preferred way in decisions about care and treatment. Future research should include analysis of on-ground interactions among parents, adolescents and clinicians across the trajectory. PMID:27160700

Emotion and decision-making: affect-driven belief systems in anxiety and depression.

PubMed

Paulus, Martin P; Yu, Angela J

2012-09-01

Emotion processing and decision-making are integral aspects of daily life. However, our understanding of the interaction between these constructs is limited. In this review, we summarize theoretical approaches that link emotion and decision-making, and focus on research with anxious or depressed individuals to show how emotions can interfere with decision-making. We integrate the emotional framework based on valence and arousal with a Bayesian approach to decision-making in terms of probability and value processing. We discuss how studies of individuals with emotional dysfunctions provide evidence that alterations of decision-making can be viewed in terms of altered probability and value computation. We argue that the probabilistic representation of belief states in the context of partially observable Markov decision processes provides a useful approach to examine alterations in probability and value representation in individuals with anxiety and depression, and outline the broader implications of this approach. Copyright © 2012. Published by Elsevier Ltd.

Emotion and decision-making: affect-driven belief systems in anxiety and depression

PubMed Central

Paulus, Martin P.; Yu, Angela J.

2012-01-01

Emotion processing and decision-making are integral aspects of daily life. However, our understanding of the interaction between these constructs is limited. In this review, we summarize theoretical approaches to the link between emotion and decision-making, and focus on research with anxious or depressed individuals that reveals how emotions can interfere with decision-making. We integrate the emotional framework based on valence and arousal with a Bayesian approach to decision-making in terms of probability and value processing. We then discuss how studies of individuals with emotional dysfunctions provide evidence that alterations of decision-making can be viewed in terms of altered probability and value computation. We argue that the probabilistic representation of belief states in the context of partially observable Markov decision processes provides a useful approach to examine alterations in probability and value representation in individuals with anxiety and depression and outline the broader implications of this approach. PMID:22898207

THE BESIEGED FORTRESS: MAKING SENSE OF RUSSIA’S ANNEXATION OF CRIMEA AND WHAT IT MEANS TO U.S. POLICY MAKERS

DTIC Science & Technology

2017-02-13

those from whom he derives his true power--the Russian people. Driven to make Russia a great power again, I argue that Putin’s decision to invade...strategically valuable piece of terrain. While undoubtedly seeking to influence Kiev’s strategic decision - making , prior to the 2014 annexation, Putin...inhabited by ethnic kin with its motherland. Putin’s decision to annex Crimea in 2014 was in part motivated by, and rationalized through

Evolution of Patient Decision-Making Regarding Medical Treatment of Rheumatoid Arthritis.

PubMed

Mathews, Alexandra L; Coleska, Adriana; Burns, Patricia B; Chung, Kevin C

2016-03-01

The migration of health care toward a consumer-driven system favors increased patient participation during the treatment decision-making process. Patient involvement in treatment decision discussions has been linked to increased treatment adherence and patient satisfaction. Previous studies have quantified decision-making styles of patients with rheumatoid arthritis (RA); however, none of them have considered the evolution of patient involvement after living with RA for many years. We conducted a qualitative study to determine the decision-making model used by long-term RA patients, and to describe the changes in their involvement over time. Twenty participants were recruited from the ongoing Silicone Arthroplasty in Rheumatoid Arthritis study. Semistructured interviews were conducted and data were analyzed using grounded theory methodology. Nineteen out of 20 participants recalled using the paternalistic decision-making (PDM) model immediately following their diagnosis. Fourteen of the 19 participants who initially used PDM evolved to shared decision-making (SDM). Participants attributed the change in involvement to the development of a trusting relationship with their physician, as well as to becoming educated about the disease. When initially diagnosed with RA, patients may let their physician decide on the best treatment course. However, over time patients may evolve to exercise a more collaborative role. Physicians should understand that even within SDM, each patient can demonstrate a varied amount of autonomy. It is up to the physician to have a discussion with each patient to determine his or her desired level of involvement. © 2016, American College of Rheumatology.

Value-Based Reimbursement: Impact of Curtailing Physician Autonomy in Medical Decision Making.

PubMed

Gupta, Dipti; Karst, Ingolf; Mendelson, Ellen B

2016-02-01

In this article, we define value in the context of reimbursement and explore the effect of shifting reimbursement paradigms on the decision-making autonomy of a women's imaging radiologist. The current metrics used for value-based reimbursement such as report turnaround time are surrogate measures that do not measure value directly. The true measure of a physician's value in medicine is accomplishment of better health outcomes, which, in breast imaging, are best achieved with a physician-patient relationship. Complying with evidence-based medicine, which includes data-driven best clinical practices, a physician's clinical expertise, and the patient's values, will improve our science and preserve the art of medicine.

Shared decision-making and patient autonomy.

PubMed

Sandman, Lars; Munthe, Christian

2009-01-01

In patient-centred care, shared decision-making is advocated as the preferred form of medical decision-making. Shared decision-making is supported with reference to patient autonomy without abandoning the patient or giving up the possibility of influencing how the patient is benefited. It is, however, not transparent how shared decision-making is related to autonomy and, in effect, what support autonomy can give shared decision-making. In the article, different forms of shared decision-making are analysed in relation to five different aspects of autonomy: (1) self-realisation; (2) preference satisfaction; (3) self-direction; (4) binary autonomy of the person; (5) gradual autonomy of the person. It is argued that both individually and jointly these aspects will support the models called shared rational deliberative patient choice and joint decision as the preferred versions from an autonomy perspective. Acknowledging that both of these models may fail, the professionally driven best interest compromise model is held out as a satisfactory second-best choice.

Data quality and processing for decision making: divergence between corporate strategy and manufacturing processes

NASA Astrophysics Data System (ADS)

McNeil, Ronald D.; Miele, Renato; Shaul, Dennis

2000-10-01

Information technology is driving improvements in manufacturing systems. Results are higher productivity and quality. However, corporate strategy is driven by a number of factors and includes data and pressure from multiple stakeholders, which includes employees, managers, executives, stockholders, boards, suppliers and customers. It is also driven by information about competitors and emerging technology. Much information is based on processing of data and the resulting biases of the processors. Thus, stakeholders can base inputs on faulty perceptions, which are not reality based. Prior to processing, data used may be inaccurate. Sources of data and information may include demographic reports, statistical analyses, intelligence reports (e.g., marketing data), technology and primary data collection. The reliability and validity of data as well as the management of sources and information is critical element to strategy formulation. The paper explores data collection, processing and analyses from secondary and primary sources, information generation and report presentation for strategy formulation and contrast this with data and information utilized to drive internal process such as manufacturing. The hypothesis is that internal process, such as manufacturing, are subordinate to corporate strategies. The impact of possible divergence in quality of decisions at the corporate level on IT driven, quality-manufacturing processes based on measurable outcomes is significant. Recommendations for IT improvements at the corporate strategy level are given.

Comparative Research: An Approach to Teaching Research Methods in Political Science and Public Administration

ERIC Educational Resources Information Center

Engbers, Trent A

2016-01-01

The teaching of research methods has been at the core of public administration education for almost 30 years. But since 1990, this journal has published only two articles on the teaching of research methods. Given the increasing emphasis on data driven decision-making, greater insight is needed into the best practices for teaching public…

The Elementary School Success Profile Model of Assessment and Prevention: Balancing Effective Practice Standards and Feasibility

ERIC Educational Resources Information Center

Bowen, Natasha K.; Powers, Joelle D.

2011-01-01

Evidence-based practice and data-driven decision making (DDDM) are two approaches to accountability that have been promoted in the school literature. In spite of the push to promote these approaches in schools, barriers to their widespread, appropriate, and effective use have limited their impact on practice and student outcomes. This article…

Attitudes towards poverty, organizations, ethics and morals: Israeli social workers' shared decision making.

PubMed

Levin, Lia; Schwartz-Tayri, Talia

2017-06-01

Partnerships between service users and social workers are complex in nature and can be driven by both personal and contextual circumstances. This study sought to explore the relationship between social workers' involvement in shared decision making with service users, their attitudes towards service users in poverty, moral standards and health and social care organizations' policies towards shared decision making. Based on the responses of 225 licensed social workers from health and social care agencies in the public, private and third sectors in Israel, path analysis was used to test a hypothesized model. Structural attributions for poverty contributed to attitudes towards people who live in poverty, which led to shared decision making. Also, organizational support in shared decision making, and professional moral identity, contributed to ethical behaviour which led to shared decision making. The results of this analysis revealed that shared decision making may be a scion of branched roots planted in the relationship between ethics, organizations and Stigma. © 2016 The Authors. Health Expectations Published by John Wiley & Sons Ltd.

Planning for successful outcomes in the new millennium.

PubMed

Matthews, P

2000-02-01

The complexity of the health care environment will increase in the next millennium. Organizations must adopt an approach of selecting outcomes management solutions that are focused on data capture, analysis, and comparative reviews and reporting. They must decisively and creatively implement, in a phased approach, integrated solutions from existing robust systems, while considering future systems targeted for implementation. Outcomes management solutions must be integrated with the organization's information systems strategic plan. The successful organization must be able to turn business-critical data into information that supports both business and clinical decision-making activities. In short, health care organizations will have to become information-driven.

Indicators of ecosystem function identify alternate states in the sagebrush steppe.

PubMed

Kachergis, Emily; Rocca, Monique E; Fernandez-Gimenez, Maria E

2011-10-01

Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics for rangeland management. Our findings suggest that the IRH approximate ecosystem processes and can distinguish between alternate states and communities and identify transitions when building data-driven STMs. Functional indicators are a simple, efficient way to create data-driven models that are consistent with alternate state theory. Managers can use them to improve current model-building methods and thus apply state-and-transition models more broadly for land management decision-making.

Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao; Sridhar, Banavar

2013-01-01

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions.

An intelligent value-driven scheduling system for Space Station Freedom with special emphasis on the electric power system

NASA Technical Reports Server (NTRS)

Krupp, Joseph C.

1991-01-01

The Electric Power Control System (EPCS) created by Decision-Science Applications, Inc. (DSA) for the Lewis Research Center is discussed. This system makes decisions on what to schedule and when to schedule it, including making choices among various options or ways of performing a task. The system is goal-directed and seeks to shape resource usage in an optimal manner using a value-driven approach. Discussed here are considerations governing what makes a good schedule, how to design a value function to find the best schedule, and how to design the algorithm that finds the schedule that maximizes this value function. Results are shown which demonstrate the usefulness of the techniques employed.

Shared Decision Making: The Need For Patient-Clinician Conversation, Not Just Information.

PubMed

Hargraves, Ian; LeBlanc, Annie; Shah, Nilay D; Montori, Victor M

2016-04-01

The growth of shared decision making has been driven largely by the understanding that patients need information and choices regarding their health care. But while these are important elements for patients who make decisions in partnership with their clinicians, our experience suggests that they are not enough to address the larger issue: the need for the patient and clinician to jointly create a course of action that is best for the individual patient and his or her family. The larger need in evidence-informed shared decision making is for a patient-clinician interaction that offers conversation, not just information, and care, not just choice. Project HOPE—The People-to-People Health Foundation, Inc.

Personalized health care and health information technology policy: an exploratory analysis.

PubMed

Wald, Jonathan S; Shapiro, Michael

2013-01-01

Personalized healthcare (PHC) is envisioned to enhance clinical practice decision-making using new genome-driven knowledge that tailors diagnosis, treatment, and prevention to the individual patient. In 2012, we conducted a focused environmental scan and informal interviews with fifteen experts to anticipate how PHC might impact health Information Technology (IT) policy in the United States. Findings indicatedthat PHC has a variable impact on current clinical practice, creates complex questions for providers, patients, and policy-makers, and will require a robust health IT infrastructure with advanced data architecture, clinical decision support, provider workflow tools, and re-use of clinical data for research. A number of health IT challenge areas were identified, along with five policy areas including: interoperable clinical decision support, standards for patient values and preferences, patient engagement, data transparency, and robust privacy and security.

Data Science and its Relationship to Big Data and Data-Driven Decision Making.

PubMed

Provost, Foster; Fawcett, Tom

2013-03-01

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

Podium: Ranking Data Using Mixed-Initiative Visual Analytics.

PubMed

Wall, Emily; Das, Subhajit; Chawla, Ravish; Kalidindi, Bharath; Brown, Eli T; Endert, Alex

2018-01-01

People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas.

Integrating Strategic and Operational Decision Making Using Data-Driven Dashboards: The Case of St. Joseph Mercy Oakland Hospital.

PubMed

Jack Weiner; Balijepally, Venugopal; Tanniru, Mohan

2015-01-01

Hospitals have invested and continue to invest heavily in building information systems to support operations at various levels of administration. These systems generate a lot of data but fail to effectively convert these data into actionable information for decision makers. Such ineffectiveness often is attributed to a lack of alignment between strategic planning and information technology (IT) initiatives supporting operational goals. We present a case study that illustrates how the use of digital dashboards at St. Joseph Mercy Oakland (SJMO) Hospital in Pontiac, Michigan, was instrumental in supporting such an alignment. Driven by a focus on key performance indicators (KPIs), dashboard applications also led to other tangible and intangible benefits. An ability to track KPIs over time and against established targets, with drill-down capabilities, allowed leadership to hold staff members accountable for achieving their performance targets. By displaying the dashboards in prominent locations (such as operational unit floors, the physicians' cafeteria, and nursing stations), SJMO ushered in transparency in the planning and monitoring processes. The need to develop KPI metrics and drive data collection efforts became ingrained in the work ethos of people at every level of the organization. Although IT-enabled dashboards have been instrumental in supporting this cultural transformation, the focus of investment was the ability of technology to make collective vision and action the responsibility of all stakeholders.

IDEA at Age Forty: Weathering Common Core Standards and Data Driven Decision Making

ERIC Educational Resources Information Center

Bicehouse, Vaughn; Faieta, Jean

2017-01-01

Special education, a discipline that aims to provide specialized instruction to meet the unique needs of each child with a disability, has turned 40 years old in the United States. Ever since the passage of the Education for All Handicapped Children Act (P.L. 94-142) in 1975, every state has been directed to provide a free and appropriate…

A Team Approach to Data-Driven Decision-Making Literacy Instruction in Preschool Classrooms: Child Assessment and Intervention through Classroom Team Self-Reflection

ERIC Educational Resources Information Center

Abbott, Mary; Beecher, Constance; Petersen, Sarah; Greenwood, Charles R.; Atwater, Jane

2017-01-01

Many schools around the country are getting positive responses implementing Response to Intervention (RTI) within a Multi-Tiered System of Support (MTSS) framework (e.g., Abbott, 2011; Ball & Trammell, 2011; Buysee & Peisner-Feinberg, 2009). RTI refers to an instructional model that is based on a student's response to instruction. RTI…

Self-Efficacy as a Predictor of Career Decision Making among Secondary School Students in Busia County, Kenya

ERIC Educational Resources Information Center

Ogutu, Joel Peter; Odera, Peter; Maragia, Samuel N.

2017-01-01

The most common constrain to career progression among youth in Kenya is the inability to make informed career decisions. Majority of high school students suffer from excitement for attaining university degree self-actualization rather than taking up career that enhances development of talents and skills that are job market driven. This study aimed…

Speed in Information Processing with a Computer Driven Visual Display in a Real-time Digital Simulation. M.S. Thesis - Virginia Polytechnic Inst.

NASA Technical Reports Server (NTRS)

Kyle, R. G.

1972-01-01

Information transfer between the operator and computer-generated display systems is an area where the human factors engineer discovers little useful design data relating human performance to system effectiveness. This study utilized a computer-driven, cathode-ray-tube graphic display to quantify human response speed in a sequential information processing task. The performance criteria was response time to sixteen cell elements of a square matrix display. A stimulus signal instruction specified selected cell locations by both row and column identification. An equal probable number code, from one to four, was assigned at random to the sixteen cells of the matrix and correspondingly required one of four, matched keyed-response alternatives. The display format corresponded to a sequence of diagnostic system maintenance events, that enable the operator to verify prime system status, engage backup redundancy for failed subsystem components, and exercise alternate decision-making judgements. The experimental task bypassed the skilled decision-making element and computer processing time, in order to determine a lower bound on the basic response speed for given stimulus/response hardware arrangement.

A visualization tool to support decision making in environmental and biological planning

USGS Publications Warehouse

Romañach, Stephanie S.; McKelvy, James M.; Conzelmann, Craig; Suir, Kevin J.

2014-01-01

Large-scale ecosystem management involves consideration of many factors for informed decision making. The EverVIEW Data Viewer is a cross-platform desktop decision support tool to help decision makers compare simulation model outputs from competing plans for restoring Florida's Greater Everglades. The integration of NetCDF metadata conventions into EverVIEW allows end-users from multiple institutions within and beyond the Everglades restoration community to share information and tools. Our development process incorporates continuous interaction with targeted end-users for increased likelihood of adoption. One of EverVIEW's signature features is side-by-side map panels, which can be used to simultaneously compare species or habitat impacts from alternative restoration plans. Other features include examination of potential restoration plan impacts across multiple geographic or tabular displays, and animation through time. As a result of an iterative, standards-driven approach, EverVIEW is relevant to large-scale planning beyond Florida, and is used in multiple biological planning efforts in the United States.

Cognitive balanced model: a conceptual scheme of diagnostic decision making.

PubMed

Lucchiari, Claudio; Pravettoni, Gabriella

2012-02-01

Diagnostic reasoning is a critical aspect of clinical performance, having a high impact on quality and safety of care. Although diagnosis is fundamental in medicine, we still have a poor understanding of the factors that determine its course. According to traditional understanding, all information used in diagnostic reasoning is objective and logically driven. However, these conditions are not always met. Although we would be less likely to make an inaccurate diagnosis when following rational decision making, as described by normative models, the real diagnostic process works in a different way. Recent work has described the major cognitive biases in medicine as well as a number of strategies for reducing them, collectively called debiasing techniques. However, advances have encountered obstacles in achieving implementation into clinical practice. While traditional understanding of clinical reasoning has failed to consider contextual factors, most debiasing techniques seem to fail in raising sound and safer medical praxis. Technological solutions, being data driven, are fundamental in increasing care safety, but they need to consider human factors. Thus, balanced models, cognitive driven and technology based, are needed in day-to-day applications to actually improve the diagnostic process. The purpose of this article, then, is to provide insight into cognitive influences that have resulted in wrong, delayed or missed diagnosis. Using a cognitive approach, we describe the basis of medical error, with particular emphasis on diagnostic error. We then propose a conceptual scheme of the diagnostic process by the use of fuzzy cognitive maps. © 2011 Blackwell Publishing Ltd.

Shared decision making, paternalism and patient choice.

PubMed

Sandman, Lars; Munthe, Christian

2010-03-01

In patient centred care, shared decision making is a central feature and widely referred to as a norm for patient centred medical consultation. However, it is far from clear how to distinguish SDM from standard models and ideals for medical decision making, such as paternalism and patient choice, and e.g., whether paternalism and patient choice can involve a greater degree of the sort of sharing involved in SDM and still retain their essential features. In the article, different versions of SDM are explored, versions compatible with paternalism and patient choice as well as versions that go beyond these traditional decision making models. Whenever SDM is discussed or introduced it is of importance to be clear over which of these different versions are being pursued, since they connect to basic values and ideals of health care in different ways. It is further argued that we have reason to pursue versions of SDM involving, what is called, a high level dynamics in medical decision-making. This leaves four alternative models to choose between depending on how we balance between the values of patient best interest, patient autonomy, and an effective decision in terms of patient compliance or adherence: Shared Rational Deliberative Patient Choice, Shared Rational Deliberative Paternalism, Shared Rational Deliberative Joint Decision, and Professionally Driven Best Interest Compromise. In relation to these models it is argued that we ideally should use the Shared Rational Deliberative Joint Decision model. However, when the patient and professional fail to reach consensus we will have reason to pursue the Professionally Driven Best Interest Compromise model since this will best harmonise between the different values at stake: patient best interest, patient autonomy, patient adherence and a continued care relationship.

Clinical Note Creation, Binning, and Artificial Intelligence.

PubMed

Deliberato, Rodrigo Octávio; Celi, Leo Anthony; Stone, David J

2017-08-03

The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans. ©Rodrigo Octávio Deliberato, Leo Anthony Celi, David J Stone. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.08.2017.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

PubMed Central

2011-01-01

Background Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways. Results This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements. Conclusions We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge. PMID:22165854

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

NASA Technical Reports Server (NTRS)

Zhang, Xiaodong; Kirilenko, Andrei; Lim, Howe; Teng, Williams

2010-01-01

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Use of a Tier 3 Evidence-Based Intervention with Progress Monitoring, Formative Assessment, and Student Goal-Setting: An Evaluation of the Immediate and Long-Term Effects on Student Reading Achievement

ERIC Educational Resources Information Center

McMasters, Angela B.

2011-01-01

Early identification and intervention for students at risk for reading failure is essential to establish the foundational skills necessary for students to become skilled readers. The focus on evidence-based practices and data-driven decision making leads educators to consider additional instructional approaches, such as formative assessment (FA)…

Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

PubMed

Morrison, James J; Hostetter, Jason; Wang, Kenneth; Siegel, Eliot L

2015-02-01

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases.

PubMed

Panahiazar, Maryam; Taslimitehrani, Vahid; Jadhav, Ashutosh; Pathak, Jyotishman

2014-10-01

In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating "smart data" which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.

Out-of-Hospital Decision-Making and Factors Influencing the Regional Distribution of Injured Patients in a Trauma System

PubMed Central

Newgard, Craig D.; Nelson, Maria J.; Kampp, Michael; Saha, Somnath; Zive, Dana; Schmidt, Terri; Daya, Mohamud; Jui, Jonathan; Wittwer, Lynn; Warden, Craig; Sahni, Ritu; Stevens, Mark; Gorman, Kyle; Koenig, Karl; Gubler, Dean; Rosteck, Pontine; Lee, Jan; Hedges, Jerris R.

2011-01-01

Background The decision-making processes used for out-of-hospital trauma triage and hospital selection in regionalized trauma systems remain poorly understood. The objective of this study was to understand the process of field triage decision-making in an established trauma system. Methods We used a mixed methods approach, including EMS records to quantify triage decisions and reasons for hospital selection in a population-based, injury cohort (2006 - 2008), plus a focused ethnography to understand EMS cognitive reasoning in making triage decisions. The study included 10 EMS agencies providing service to a 4-county regional trauma system with 3 trauma centers and 13 non-trauma hospitals. For qualitative analyses, we conducted field observation and interviews with 35 EMS field providers and a round-table discussion with 40 EMS management personnel to generate an empirical model of out-of-hospital decision making in trauma triage. Results 64,190 injured patients were evaluated by EMS, of whom 56,444 (88.0%) were transported to acute care hospitals and 9,637 (17.1% of transports) were field trauma activations. For non-trauma activations, patient/family preference and proximity accounted for 78% of destination decisions. EMS provider judgment was cited in 36% of field trauma activations and was the sole criterion in 23% of trauma patients. The empirical model demonstrated that trauma triage is driven primarily by EMS provider “gut feeling” (judgment) and relies heavily on provider experience, mechanism of injury, and early visual cues at the scene. Conclusions Provider cognitive reasoning for field trauma triage is more heuristic than algorithmic and driven primarily by provider judgment, rather than specific triage criteria. PMID:21817971

How can surgeons facilitate resident intraoperative decision-making?

PubMed

Hill, Katherine A; Dasari, Mohini; Littleton, Eliza B; Hamad, Giselle G

2017-10-01

Cognitive skills such as decision-making are critical to developing operative autonomy. We explored resident decision-making using a recollection of specific examples, from the attending surgeon and resident, after laparoscopic cholecystectomy. In a separate semi-structured interview, the attending and resident both answered five questions, regarding the resident's operative roles and decisions, ways the attending helped, times when the attending operated, and the effect of the relationship between attending and resident. Themes were extracted using inductive methods. Thirty interviews were completed after 15 cases. Facilitators of decision-making included dialogue, safe struggle, and appreciation for retraction. Aberrant case characteristics, anatomic uncertainties, and time pressures provided barriers. Attending-resident mismatches included descriptions of transitioning control to the attending. Reciprocal dialogue, including concept-driven feedback, is helpful during intraoperative teaching. Unanticipated findings impede resident decision-making, and we describe differences in understanding transfers of operative control. Given these factors, we suggest that pre-operative discussions may be beneficial. Copyright © 2017 Elsevier Inc. All rights reserved.

People adopt optimal policies in simple decision-making, after practice and guidance.

PubMed

Evans, Nathan J; Brown, Scott D

2017-04-01

Organisms making repeated simple decisions are faced with a tradeoff between urgent and cautious strategies. While animals can adopt a statistically optimal policy for this tradeoff, findings about human decision-makers have been mixed. Some studies have shown that people can optimize this "speed-accuracy tradeoff", while others have identified a systematic bias towards excessive caution. These issues have driven theoretical development and spurred debate about the nature of human decision-making. We investigated a potential resolution to the debate, based on two factors that routinely differ between human and animal studies of decision-making: the effects of practice, and of longer-term feedback. Our study replicated the finding that most people, by default, are overly cautious. When given both practice and detailed feedback, people moved rapidly towards the optimal policy, with many participants reaching optimality with less than 1 h of practice. Our findings have theoretical implications for cognitive and neural models of simple decision-making, as well as methodological implications.

Intelligent Model Management in a Forest Ecosystem Management Decision Support System

Treesearch

Donald Nute; Walter D. Potter; Frederick Maier; Jin Wang; Mark Twery; H. Michael Rauscher; Peter Knopp; Scott Thomasma; Mayukh Dass; Hajime Uchiyama

2002-01-01

Decision making for forest ecosystem management can include the use of a wide variety of modeling tools. These tools include vegetation growth models, wildlife models, silvicultural models, GIS, and visualization tools. NED-2 is a robust, intelligent, goal-driven decision support system that integrates tools in each of these categories. NED-2 uses a blackboard...

Examining Candidate Information Search Processes: The Impact of Processing Goals and Sophistication.

ERIC Educational Resources Information Center

Huang, Li-Ning

2000-01-01

Investigates how 4 different information-processing goals, varying on the dimensions of effortful versus effortless and impression-driven versus non-impression-driven processing, and individual difference in political sophistication affect the depth at which undergraduate students process candidate information and their decision-making strategies.…

Caring Cooperators and Powerful Punishers: Differential Effects of Induced Care and Power Motivation on Different Types of Economic Decision Making.

PubMed

Chierchia, G; Lesemann, F H Parianen; Snower, D; Vogel, M; Singer, T

2017-09-11

Standard economic theory postulates that decisions are driven by stable context-insensitive preferences, while motivation psychology suggests they are driven by distinct context-sensitive motives with distinct evolutionary goals and characteristic psycho-physiological and behavioral patterns. To link these fields and test how distinct motives could differentially predict different types of economic decisions, we experimentally induced participants with either a Care or a Power motive, before having them take part in a suite of classic game theoretical paradigms involving monetary exchange. We show that the Care induction alone raised scores on a latent factor of cooperation-related behaviors, relative to a control condition, while, relative to Care, Power raised scores on a punishment-related factor. These findings argue against context-insensitive stable preferences and theories of strong reciprocity and in favor of a motive-based approach to economic decision making: Care and Power motivation have a dissociable fingerprint in shaping either cooperative or punishment behaviors.

Mentor judgements and decision-making in the assessment of student nurse competence in practice: A mixed-methods study.

PubMed

Burden, Sarah; Topping, Anne Elizabeth; O'Halloran, Catherine

2018-05-01

To investigate how mentors form judgements and reach summative assessment decisions regarding student competence in practice. Competence assessment is a significant component of pre-registration nursing programmes in the United Kingdom. Concerns exist that assessments are subjective, lack consistency and that mentors fail to judge student performance as unsatisfactory. A two-stage sequential embedded mixed-methods design. Data collected 2012-2013. This study involved a whole student cohort completing a UK undergraduate adult nursing programme (N = 41). Stage 1: quantitative data on mentor conduct of assessment interviews and the final decision recorded (N = 330 from 270 mentors) were extracted from student Practice Assessment Documents (PADs). Stage 2: mentor feedback in student PADs was used in Stimulated Recall interviews with a purposive sample of final placement mentors (N = 17). These were thematically analysed. Findings were integrated to develop a theoretically driven model of mentor decision-making. Course assessment strategies and documentation had limited effect in framing mentor judgements and decisions. Rather, mentors amassed impressions, moderated by expectations of an "idealized student" by practice area and programme stage that influenced their management and outcome of the assessment process. These impressions were accumulated and combined into judgements that informed the final decision. This process can best be understood and conceptualized through the Brunswik's lens model of social judgement. Mentor decisions were reasoned and there was a shared understanding of judgement criteria and their importance. This impression-based nature of mentor decision-making questions the reliability and validity of competency-based assessments used in nursing pre-registration programmes. © 2017 John Wiley & Sons Ltd.

Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

PubMed Central

2011-01-01

The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular. PMID:22206355

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

NASA Astrophysics Data System (ADS)

Liu, Chao; Gong, Yongqiang; Laflamme, Simon; Phares, Brent; Sarkar, Soumik

2017-01-01

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

A Decision Fusion Framework for Treatment Recommendation Systems.

PubMed

Mei, Jing; Liu, Haifeng; Li, Xiang; Xie, Guotong; Yu, Yiqin

2015-01-01

Treatment recommendation is a nontrivial task--it requires not only domain knowledge from evidence-based medicine, but also data insights from descriptive, predictive and prescriptive analysis. A single treatment recommendation system is usually trained or modeled with a limited (size or quality) source. This paper proposes a decision fusion framework, combining both knowledge-driven and data-driven decision engines for treatment recommendation. End users (e.g. using the clinician workstation or mobile apps) could have a comprehensive view of various engines' opinions, as well as the final decision after fusion. For implementation, we leverage several well-known fusion algorithms, such as decision templates and meta classifiers (of logistic and SVM, etc.). Using an outcome-driven evaluation metric, we compare the fusion engine with base engines, and our experimental results show that decision fusion is a promising way towards a more valuable treatment recommendation.

Invited review: Helping dairy farmers to improve economic performance utilizing data-driving decision support tools.

PubMed

Cabrera, V E

2018-01-01

The objective of this review paper is to describe the development and application of a suite of more than 40 computerized dairy farm decision support tools contained at the University of Wisconsin-Madison (UW) Dairy Management website http://DairyMGT.info. These data-driven decision support tools are aimed to help dairy farmers improve their decision-making, environmental stewardship and economic performance. Dairy farm systems are highly dynamic in which changing market conditions and prices, evolving policies and environmental restrictions together with every time more variable climate conditions determine performance. Dairy farm systems are also highly integrated with heavily interrelated components such as the dairy herd, soils, crops, weather and management. Under these premises, it is critical to evaluate a dairy farm following a dynamic integrated system approach. For this approach, it is crucial to use meaningful data records, which are every time more available. These data records should be used within decision support tools for optimal decision-making and economic performance. Decision support tools in the UW-Dairy Management website (http://DairyMGT.info) had been developed using combination and adaptation of multiple methods together with empirical techniques always with the primary goal for these tools to be: (1) highly user-friendly, (2) using the latest software and computer technologies, (3) farm and user specific, (4) grounded on the best scientific information available, (5) remaining relevant throughout time and (6) providing fast, concrete and simple answers to complex farmers' questions. DairyMGT.info is a translational innovative research website in various areas of dairy farm management that include nutrition, reproduction, calf and heifer management, replacement, price risk and environment. This paper discusses the development and application of 20 selected (http://DairyMGT.info) decision support tools.

Improvements in agricultural water decision support using remote sensing

NASA Astrophysics Data System (ADS)

Marshall, M. T.

2012-12-01

Population driven water scarcity, aggravated by climate-driven evaporative demand in dry regions of the world, has the potential of transforming ecological and social systems to the point of armed conflict. Water shortages will be most severe in agricultural areas, as the priority shifts to urban and industrial use. In order to design, evaluate, and monitor appropriate mitigation strategies, predictive models must be developed that quantify exposure to water shortage. Remote sensing data has been used for more than three decades now to parametrize these models, because field measurements are costly and difficult in remote regions of the world. In the past decade, decision-makers for the first time can make accurate and near real-time evaluations of field conditions with the advent of hyper- spatial and spectral and coarse resolution continuous remote sensing data. Here, we summarize two projects representing diverse applications of remote sensing to improve agricultural water decision support. The first project employs MODIS (coarse resolution continuous data) to drive an evapotranspiration index, which is combined with the Standardized Precipitation Index driven by meteorological satellite data to improve famine early warning in Africa. The combined index is evaluated using district-level crop yield data from Kenya and Malawi and national-level crop yield data from the United Nations Food and Agriculture Organization. The second project utilizes hyper- spatial (GeoEye 1, Quickbird, IKONOS, and RapidEye) and spectral (Hyperion/ALI), as well as multi-spectral (Landsat ETM+, SPOT, and MODIS) data to develop biomass estimates for key crops (alfalfa, corn, cotton, and rice) in the Central Valley of California. Crop biomass is an important indicator of crop water productivity. The remote sensing data is combined using various data fusion techniques and evaluated with field data collected in the summer of 2012. We conclude with a brief discussion on implementation of these tools into two new decision support systems: FEWSNET Early Warning Explorer (http://earlywarning.usgs.gov/fews/ewxindex.php) and the NASA Terrestrial Observation and Prediction System (http://ecocast.arc.nasa.gov/) for the first and second project respectively.

Medical sieve: a cognitive assistant for radiologists and cardiologists

NASA Astrophysics Data System (ADS)

Syeda-Mahmood, T.; Walach, E.; Beymer, D.; Gilboa-Solomon, F.; Moradi, M.; Kisilev, P.; Kakrania, D.; Compas, C.; Wang, H.; Negahdar, R.; Cao, Y.; Baldwin, T.; Guo, Y.; Gur, Y.; Rajan, D.; Zlotnick, A.; Rabinovici-Cohen, S.; Ben-Ari, R.; Guy, Amit; Prasanna, P.; Morey, J.; Boyko, O.; Hashoul, S.

2016-03-01

Radiologists and cardiologists today have to view large amounts of imaging data relatively quickly leading to eye fatigue. Further, they have only limited access to clinical information relying mostly on their visual interpretation of imaging studies for their diagnostic decisions. In this paper, we present Medical Sieve, an automated cognitive assistant for radiologists and cardiologists designed to help in their clinical decision-making. The sieve is a clinical informatics system that collects clinical, textual and imaging data of patients from electronic health records systems. It then analyzes multimodal content to detect anomalies if any, and summarizes the patient record collecting all relevant information pertinent to a chief complaint. The results of anomaly detection are then fed into a reasoning engine which uses evidence from both patient-independent clinical knowledge and large-scale patient-driven similar patient statistics to arrive at potential differential diagnosis to help in clinical decision making. In compactly summarizing all relevant information to the clinician per chief complaint, the system still retains links to the raw data for detailed review providing holistic summaries of patient conditions. Results of clinical studies in the domains of cardiology and breast radiology have already shown the promise of the system in differential diagnosis and imaging studies summarization.

Fleet DNA (Presentation)

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

Walkokwicz, K.; Duran, A.

2014-06-01

The Fleet DNA project objectives include capturing and quantifying drive cycle and technology variation for the multitude of medium- and heavy-duty vocations; providing a common data storage warehouse for medium- and heavy-duty vehicle fleet data across DOE activities and laboratories; and integrating existing DOE tools, models, and analyses to provide data-driven decision making capabilities. Fleet DNA advantages include: for Government - providing in-use data for standard drive cycle development, R&D, tech targets, and rule making; for OEMs - real-world usage datasets provide concrete examples of customer use profiles; for fleets - vocational datasets help illustrate how to maximize return onmore » technology investments; for Funding Agencies - ways are revealed to optimize the impact of financial incentive offers; and for researchers -a data source is provided for modeling and simulation.« less

Dopamine Receptor-Specific Contributions to the Computation of Value.

PubMed

Burke, Christopher J; Soutschek, Alexander; Weber, Susanna; Raja Beharelle, Anjali; Fehr, Ernst; Haker, Helene; Tobler, Philippe N

2018-05-01

Dopamine is thought to play a crucial role in value-based decision making. However, the specific contributions of different dopamine receptor subtypes to the computation of subjective value remain unknown. Here we demonstrate how the balance between D1 and D2 dopamine receptor subtypes shapes subjective value computation during risky decision making. We administered the D2 receptor antagonist amisulpride or placebo before participants made choices between risky options. Compared with placebo, D2 receptor blockade resulted in more frequent choice of higher risk and higher expected value options. Using a novel model fitting procedure, we concurrently estimated the three parameters that define individual risk attitude according to an influential theoretical account of risky decision making (prospect theory). This analysis revealed that the observed reduction in risk aversion under amisulpride was driven by increased sensitivity to reward magnitude and decreased distortion of outcome probability, resulting in more linear value coding. Our data suggest that different components that govern individual risk attitude are under dopaminergic control, such that D2 receptor blockade facilitates risk taking and expected value processing.

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework.

PubMed

Teeguarden, Justin G; Tan, Yu-Mei; Edwards, Stephen W; Leonard, Jeremy A; Anderson, Kim A; Corley, Richard A; Kile, Molly L; Simonich, Staci M; Stone, David; Tanguay, Robert L; Waters, Katrina M; Harper, Stacey L; Williams, David E

2016-05-03

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the "systems approaches" used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.

Decision Analysis for a Sustainable Environment, Economy, and Society: A Participatory Framework for Ecosystem Services-Based Decision-Making

EPA Science Inventory

There is an increasing understanding that top-down regulatory and technology driven responses are not sufficient to address current and emerging environmental challenges such as climate change, sustainable communities, and environmental justice. The vast majority of environmenta...

Conserving analyst attention units: use of multi-agent software and CEP methods to assist information analysis

NASA Astrophysics Data System (ADS)

Rimland, Jeffrey; McNeese, Michael; Hall, David

2013-05-01

Although the capability of computer-based artificial intelligence techniques for decision-making and situational awareness has seen notable improvement over the last several decades, the current state-of-the-art still falls short of creating computer systems capable of autonomously making complex decisions and judgments in many domains where data is nuanced and accountability is high. However, there is a great deal of potential for hybrid systems in which software applications augment human capabilities by focusing the analyst's attention to relevant information elements based on both a priori knowledge of the analyst's goals and the processing/correlation of a series of data streams too numerous and heterogeneous for the analyst to digest without assistance. Researchers at Penn State University are exploring ways in which an information framework influenced by Klein's (Recognition Primed Decision) RPD model, Endsley's model of situational awareness, and the Joint Directors of Laboratories (JDL) data fusion process model can be implemented through a novel combination of Complex Event Processing (CEP) and Multi-Agent Software (MAS). Though originally designed for stock market and financial applications, the high performance data-driven nature of CEP techniques provide a natural compliment to the proven capabilities of MAS systems for modeling naturalistic decision-making, performing process adjudication, and optimizing networked processing and cognition via the use of "mobile agents." This paper addresses the challenges and opportunities of such a framework for augmenting human observational capability as well as enabling the ability to perform collaborative context-aware reasoning in both human teams and hybrid human / software agent teams.

Improving Federal Cybersecurity Governance Through Data-Driven Decision Making and Execution

DTIC Science & Technology

2015-09-01

responsibility to set the environment 2 Federal IT Dashboard (https://www.itdashboard.gov/sites/default/files/exhibit53report/4), World Bank GDP (http...ance managers  members of the private sector supporting the audience members listed above Figure 1 below graphically depicts the audience for...for root-cause analysis. Common examples of indicators include the Bureau of Labor Statis- tics’ Unemployment Rate and Consumer Price Index. Indices can

Building a Public Health Response to the Flint Water Crisis: Implications for Policy and Decision-Making

NASA Astrophysics Data System (ADS)

Furr-Holden, D.

2017-12-01

Flint, MI has experienced a recent, man-made public health crisis. The Flint Water Crisis, caused by a switch in the municipal water supply and subsequent violation of engineering and regulatory standards to ensure water quality lead to a large portion of the city being exposed to excess metals (including lead), bacteria and other water-borne pathogens. The data used to initially rebut the existence of the crisis were ecologically flawed as they included large numbers of people who were not on the Flint water supply. Policy-makers, municipal officials, the medical community, and public health professionals were at odds over the existence of a problem and the lack of data only fueled the debate. Pediatricians, lead by Dr. Mona Hannah-Attisha, began testing children in the Hurley Children's Medical Center for blood-lead levels and observed a 2-fold increase in elevated blood lead levels in Flint children compared to children in the area not on the Flint municipal water supply, where no increases in elevated lead were observed. Subsequent geospatial analyses revealed spatial clustering of cases based on where children live, go to school and play. These data represented the first step in data driven decision making leading to the subsequent switch of the municipal water supply and launch of subsequent advocacy efforts to remediate the effect of the Water Crisis. Since that time, a multi-disciplinary team of scientists including engineers, bench scientists, physicians and public health researchers have mounted evidence to promote complete replacement of the city's aging water infrastructure, developed a data registry to track cases and coordinate care and services for affected residents, and implemented a community engagement model that puts residents and community stakeholders at the heart of the planning and implementation efforts. The presentation will include data used at various stages to mount a public health response to the Flint Water Crisis and establish the link between data-driven decisions and subsequent policies to mediate long term consequences.

Nonurgent use of a pediatric emergency department: a preliminary qualitative study.

PubMed

Chin, Nancy P; Goepp, Julius G; Malia, Timothy; Harris, LeWanza; Poordabbagh, Armin

2006-01-01

To understand patterns of decision making among families presenting to a pediatric emergency department (ED) for nonacute care and to understand pediatric ED staff responses. Cross-sectional qualitative study using in-depth interviews, direct observations, and nonidentifying demographic data. Eleven percent of visits made during the study period were identified as nonacute. All were made by families from low-income areas. Three main themes emerged: (1) most families had been referred by their primary care providers; (2) the complexity of living in low-income areas makes the ED a choice of convenience for these stressed families; and (3) mistrust of primary health services was not identified by our respondents as a motivator for ED utilization, in contrast with other published data. Two themes emerged from ED staff: (1) actual nonurgent visit rates were lower than staff estimates; and (2) these visits produced frustration among staff members, although their degrees of insight and understanding of factors motivating these visits were variable. In this setting, nonacute visits occurred with lower than perceived frequency and caused disproportionate frustration among staff and families. These visits appear to be driven more by consequences of system design and structure than by family members' decision making. Mistrust of primary care services was not a strong family decision-making factor; the study's setting may have limited its ability to capture such data. Recommended system changes to lower barriers to primary care include expanded office hours, subsidized staffing for offices in medically underserved areas, and lowering barriers to sick care.

Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making.

PubMed

Knight, Gwenan M; Dharan, Nila J; Fox, Gregory J; Stennis, Natalie; Zwerling, Alice; Khurana, Renuka; Dowdy, David W

2016-01-01

The dominant approach to decision-making in public health policy for infectious diseases relies heavily on expert opinion, which often applies empirical evidence to policy questions in a manner that is neither systematic nor transparent. Although systematic reviews are frequently commissioned to inform specific components of policy (such as efficacy), the same process is rarely applied to the full decision-making process. Mathematical models provide a mechanism through which empirical evidence can be methodically and transparently integrated to address such questions. However, such models are often considered difficult to interpret. In addition, models provide estimates that need to be iteratively re-evaluated as new data or considerations arise. Using the case study of a novel diagnostic for tuberculosis, a framework for improved collaboration between public health decision-makers and mathematical modellers that could lead to more transparent and evidence-driven policy decisions for infectious diseases in the future is proposed. The framework proposes that policymakers should establish long-term collaborations with modellers to address key questions, and that modellers should strive to provide clear explanations of the uncertainty of model structure and outputs. Doing so will improve the applicability of models and clarify their limitations when used to inform real-world public health policy decisions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Information systems: the key to evidence-based health practice.

PubMed Central

Rodrigues, R. J.

2000-01-01

Increasing prominence is being given to the use of best current evidence in clinical practice and health services and programme management decision-making. The role of information in evidence-based practice (EBP) is discussed, together with questions of how advanced information systems and technology (IS&T) can contribute to the establishment of a broader perspective for EBP. The author examines the development, validation and use of a variety of sources of evidence and knowledge that go beyond the well-established paradigm of research, clinical trials, and systematic literature review. Opportunities and challenges in the implementation and use of IS&T and knowledge management tools are examined for six application areas: reference databases, contextual data, clinical data repositories, administrative data repositories, decision support software, and Internet-based interactive health information and communication. Computerized and telecommunications applications that support EBP follow a hierarchy in which systems, tasks and complexity range from reference retrieval and the processing of relatively routine transactions, to complex "data mining" and rule-driven decision support systems. PMID:11143195

Court-Driven Reform and Equal Educational Opportunity: Centralization, Decentralization, and the Shifting Judicial Role

ERIC Educational Resources Information Center

Superfine, Benjamin Michael

2010-01-01

Judicial decisions focusing on equal educational opportunity involve significant issues of educational governance and often involve explicit questions about the extent to which authority to make educational decisions should be centralized or decentralized across various institutions and entities. This review aims at clarifying scholars'…

Applying a Data Stewardship Maturity Matrix to the NOAA Observing System Portfolio Integrated Assessment Process

NASA Astrophysics Data System (ADS)

Peng, G.; Austin, M.

2017-12-01

Identification and prioritization of targeted user community needs are not always considered until after data has been created and archived. Gaps in data curation and documentation in the data production and delivery phases limit data's broad utility specifically for decision makers. Expert understanding and knowledge of a particular dataset is often required as a part of the data and metadata curation process to establish the credibility of the data and support informed decision-making. To enhance curation practices, content from NOAA's Observing System Integrated Assessment (NOSIA) Value Tree, NOAA's Data Catalog/Digital Object Identifier (DOI) projects (collection-level metadata) have been integrated with Data/Stewardship Maturity Matrices (data and stewardship quality information) focused on assessment of user community needs. This results in user focused evidence based decision making tools created by NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) through identification and assessment of data content gaps related to scientific knowledge and application to key areas of societal benefit. Through enabling user need feedback from the beginning of data creation through archive allows users to determine the quality and value of data that is fit for purpose. Data gap assessment and prioritization are presented in a user-friendly way using the data stewardship maturity matrices as measurement of data management quality. These decision maker tools encourages data producers and data providers/stewards to consider users' needs prior to data creation and dissemination resulting in user driven data requirements increasing return on investment. A use case focused on need for NOAA observations linked societal benefit will be used to demonstrate the value of these tools.

Led into temptation? Rewarding brand logos bias the neural encoding of incidental economic decisions.

PubMed

Murawski, Carsten; Harris, Philip G; Bode, Stefan; Domínguez D, Juan F; Egan, Gary F

2012-01-01

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness.

Led into Temptation? Rewarding Brand Logos Bias the Neural Encoding of Incidental Economic Decisions

PubMed Central

Murawski, Carsten; Harris, Philip G.; Bode, Stefan; Domínguez D., Juan F.; Egan, Gary F.

2012-01-01

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness. PMID:22479547

Clinical decision-making and secondary findings in systems medicine.

PubMed

Fischer, T; Brothers, K B; Erdmann, P; Langanke, M

2016-05-21

Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it. This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their "quantified self." This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.

Exploring sales data during a healthy corner store intervention in Toronto: the Food Retail Environments Shaping Health (FRESH) project.

PubMed

Minaker, Leia M; Lynch, Meghan; Cook, Brian E; Mah, Catherine L

2017-10-01

Population health interventions in the retail food environment, such as corner store interventions, aim to influence the kind of cues consumers receive so that they are more often directed toward healthier options. Research that addresses financial aspects of retail interventions, particularly using outcome measures such as store sales that are central to retail decision making, is limited. This study explored store sales over time and across product categories during a healthy corner store intervention in a lowincome neighbourhood in Toronto, Ontario. Sales data (from August 2014 to April 2015) were aggregated by product category and by day. We used Microsoft Excel pivot tables to summarize and visually present sales data. We conducted t-tests to examine differences in product category sales by "peak" versus "nonpeak" sales days. Overall store sales peaked on the days at the end of each month, aligned with the issuing of social assistance payments. Revenue spikes on peak sales days were driven predominantly by transit pass sales. On peak sales days, mean sales of nonnutritious snacks and cigarettes were marginally higher than on other days of the month. Finally, creative strategies to increase sales of fresh vegetables and fruits seemed to substantially increase revenue from these product categories. Store sales data is an important store-level metric of food environment intervention success. Furthermore, data-driven decision making by retailers can be important for tailoring interventions. Future interventions and research should consider partnerships and additional success metrics for retail food environment interventions in diverse Canadian contexts.

Exploring sales data during a healthy corner store intervention in Toronto: the Food Retail Environments Shaping Health (FRESH) project

PubMed Central

Leia M., Minaker; Meghan, Lynch; Brian E., Cook; Catherine L., Mah

2017-01-01

Abstract Introduction: Population health interventions in the retail food environment, such as corner store interventions, aim to influence the kind of cues consumers receive so that they are more often directed toward healthier options. Research that addresses financial aspects of retail interventions, particularly using outcome measures such as store sales that are central to retail decision making, is limited. This study explored store sales over time and across product categories during a healthy corner store intervention in a lowincome neighbourhood in Toronto, Ontario. Methods: Sales data (from August 2014 to April 2015) were aggregated by product category and by day. We used Microsoft Excel pivot tables to summarize and visually present sales data. We conducted t-tests to examine differences in product category sales by “peak” versus “nonpeak” sales days. Results: Overall store sales peaked on the days at the end of each month, aligned with the issuing of social assistance payments. Revenue spikes on peak sales days were driven predominantly by transit pass sales. On peak sales days, mean sales of nonnutritious snacks and cigarettes were marginally higher than on other days of the month. Finally, creative strategies to increase sales of fresh vegetables and fruits seemed to substantially increase revenue from these product categories. Conclusion: Store sales data is an important store-level metric of food environment intervention success. Furthermore, data-driven decision making by retailers can be important for tailoring interventions. Future interventions and research should consider partnerships and additional success metrics for retail food environment interventions in diverse Canadian contexts. PMID:29043761

Proving Value in Radiology: Experience Developing and Implementing a Shareable Open Source Registry Platform Driven by Radiology Workflow.

PubMed

Gichoya, Judy Wawira; Kohli, Marc D; Haste, Paul; Abigail, Elizabeth Mills; Johnson, Matthew S

2017-10-01

Numerous initiatives are in place to support value based care in radiology including decision support using appropriateness criteria, quality metrics like radiation dose monitoring, and efforts to improve the quality of the radiology report for consumption by referring providers. These initiatives are largely data driven. Organizations can choose to purchase proprietary registry systems, pay for software as a service solution, or deploy/build their own registry systems. Traditionally, registries are created for a single purpose like radiation dosage or specific disease tracking like diabetes registry. This results in a fragmented view of the patient, and increases overhead to maintain such single purpose registry system by requiring an alternative data entry workflow and additional infrastructure to host and maintain multiple registries for different clinical needs. This complexity is magnified in the health care enterprise whereby radiology systems usually are run parallel to other clinical systems due to the different clinical workflow for radiologists. In the new era of value based care where data needs are increasing with demand for a shorter turnaround time to provide data that can be used for information and decision making, there is a critical gap to develop registries that are more adapt to the radiology workflow with minimal overhead on resources for maintenance and setup. We share our experience of developing and implementing an open source registry system for quality improvement and research in our academic institution that is driven by our radiology workflow.

Bottlenecks in Geospatial Data-Driven Decision-Making for Natural Disaster Management: A Case Study of Forest Fire Prevention and Control in Guatemala's Maya Biosphere Reserve

NASA Astrophysics Data System (ADS)

Berenter, J. S.; Mueller, J. M.; Morrison, I.

2016-12-01

Annual forest fires are a source of great economic and environmental cost in the Maya Biosphere Reserve (MBR), a region of high ecological and historical value in Guatemala's department of Petén. Scarce institutional resources, limited local response capacity, and difficult terrain place a premium on the use of Earth observation data for forest fire management in the MBR, but also present significant institutional barriers to optimizing the value of this data. Drawing upon key informant interviews and a contingent valuation survey of national and local actors conducted during a three-year performance evaluation of the USAID/NASA Regional Visualization and Monitoring System (SERVIR), this paper traces the flow of SERVIR data from acquisition to decision in order to assess the institutional and contextual factors affecting the value of Earth observation data for forest fire management in the MBR. Findings indicate that the use of satellite data for forest fire management in the MBR is widespread and multi-dimensional: historical assessments of land use and land cover, fire scarring, and climate data help central-level fire management agencies identify and regulate fire-sensitive areas; regular monitoring and dissemination of climate data enables coordination between agricultural burning activities and fire early warning systems; and daily satellite detection of thermal anomalies in land surface temperature permits first responders to monitor and react to "hotspot" activity. Findings also suggest, however, that while the decentralized operations of Petén's fire management systems foster the use of Earth observation data, systemic bottlenecks, including budgetary constraints, inadequate data infrastructure and interpretation capacity, and obstacles to regulatory enforcement, impede the flow of information and use of technology and thus impact the value of that data, particularly in remote and under-resourced areas of the MBR. A geographic expansion and fortification of support systems for use of Earth observation data is thus required to maximize the value of data-driven forest fire management in the MBR. Findings further validate a need for continued cooperation between scientific and governance institutions to disseminate and integrate geospatial data into environmental decision-making.

Pay-for-performance policy and data-driven decision making within nursing homes: a qualitative study.

PubMed

Abrahamson, Kathleen; Miech, Edward; Davila, Heather Wood; Mueller, Christine; Cooke, Valerie; Arling, Greg

2015-05-01

Health systems globally and within the USA have introduced nursing home pay-for-performance (P4P) programmes in response to the need for improved nursing home quality. Central to the challenge of administering effective P4P is the availability of accurate, timely and clinically appropriate data for decision making. We aimed to explore ways in which data were collected, thought about and used as a result of participation in a P4P programme. Semistructured interviews were conducted with 232 nursing home employees from within 70 nursing homes that participated in P4P-sponsored quality improvement (QI) projects. Interview data were analysed to identify themes surrounding collecting, thinking about and using data for QI decision making. The term 'data' appeared 247 times in the interviews, and over 92% of these instances (228/247) were spontaneous references by nursing home staff. Overall, 34% of respondents (79/232) referred directly to 'data' in their interviews. Nursing home leadership more frequently discussed data use than direct care staff. Emergent themes included using data to identify a QI problem, gathering data in new ways at the local level, and measuring outcomes in response to P4P participation. Alterations in data use as a result of policy change were theoretically consistent with the revised version of the Promoting Action on Research Implementation in Health Services framework, which posits that successful implementation is a function of evidence, context and facilitation. Providing a reimbursement context that facilitates the collection and use of reliable local evidence may be an important consideration to others contemplating the adaptation of P4P policies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Developing a video intervention to model effective patient-physician communication and health-related decision-making skills for a multiethnic audience.

PubMed

Richter, D L; Greaney, M L; McKeown, R E; Cornell, C E; Littleton, M A; Pulley, L; Groff, J Y; Byrd, T L; Herman, C J

2001-01-01

The ENDOW study is a multisite, community-based project designed to improve decision-making and patient-physician communication skills for midlife African-American, white, and Hispanic women facing decisions about hysterectomy. Based on results of initial focus groups, a patient education video was developed in English and Spanish to serve as the centerpiece of various interventions. The video uses community women to model appropriate decision-making and patient-physician communication skills. Women in the target populations rated the video as useful to very useful and would recommend it to others. The use of theory-driven approaches and pilot testing of draft products resulted in the production of a well-accepted, useful video suitable for diverse populations in intervention sites in several states.

Cloud Geospatial Analysis Tools for Global-Scale Comparisons of Population Models for Decision Making

NASA Astrophysics Data System (ADS)

Hancher, M.; Lieber, A.; Scott, L.

2017-12-01

The volume of satellite and other Earth data is growing rapidly. Combined with information about where people are, these data can inform decisions in a range of areas including food and water security, disease and disaster risk management, biodiversity, and climate adaptation. Google's platform for planetary-scale geospatial data analysis, Earth Engine, grants access to petabytes of continually updating Earth data, programming interfaces for analyzing the data without the need to download and manage it, and mechanisms for sharing the analyses and publishing results for data-driven decision making. In addition to data about the planet, data about the human planet - population, settlement and urban models - are now available for global scale analysis. The Earth Engine APIs enable these data to be joined, combined or visualized with economic or environmental indicators such as nighttime lights trends, global surface water, or climate projections, in the browser without the need to download anything. We will present our newly developed application intended to serve as a resource for government agencies, disaster response and public health programs, or other consumers of these data to quickly visualize the different population models, and compare them to ground truth tabular data to determine which model suits their immediate needs. Users can further tap into the power of Earth Engine and other Google technologies to perform a range of analysis from simple statistics in custom regions to more complex machine learning models. We will highlight case studies in which organizations around the world have used Earth Engine to combine population data with multiple other sources of data, such as water resources and roads data, over deep stacks of temporal imagery to model disease risk and accessibility to inform decisions.

How much are you prepared to PAY for a forecast?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Coughlan, Erin; Ramos, Maria-Helena; Pappenberger, Florian; Wetterhall, Fredrik; Bachofen, Carina; van Andel, Schalk Jan

2015-04-01

Probabilistic hydro-meteorological forecasts are a crucial element of the decision-making chain in the field of flood prevention. The operational use of probabilistic forecasts is increasingly promoted through the development of new novel state-of-the-art forecast methods and numerical skill is continuously increasing. However, the value of such forecasts for flood early-warning systems is a topic of diverging opinions. Indeed, the word value, when applied to flood forecasting, is multifaceted. It refers, not only to the raw cost of acquiring and maintaining a probabilistic forecasting system (in terms of human and financial resources, data volume and computational time), but also and most importantly perhaps, to the use of such products. This game aims at investigating this point. It is a willingness to pay game, embedded in a risk-based decision-making experiment. Based on a ``Red Cross/Red Crescent, Climate Centre'' game, it is a contribution to the international Hydrologic Ensemble Prediction Experiment (HEPEX). A limited number of probabilistic forecasts will be auctioned to the participants; the price of these forecasts being market driven. All participants (irrespective of having bought or not a forecast set) will then be taken through a decision-making process to issue warnings for extreme rainfall. This game will promote discussions around the topic of the value of forecasts for decision-making in the field of flood prevention.

Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making

NASA Astrophysics Data System (ADS)

Jiang, Wen; Wei, Boya

2018-02-01

The theory of intuitionistic fuzzy sets (IFS) is widely used for dealing with vagueness and the Dempster-Shafer (D-S) evidence theory has a widespread use in multiple criteria decision-making problems under uncertain situation. However, there are many methods to aggregate intuitionistic fuzzy numbers (IFNs), but the aggregation operator to fuse basic probability assignment (BPA) is rare. Power average (P-A) operator, as a powerful operator, is useful and important in information fusion. Motivated by the idea of P-A power, in this paper, a new operator based on the IFS and D-S evidence theory is proposed, which is named as intuitionistic fuzzy evidential power average (IFEPA) aggregation operator. First, an IFN is converted into a BPA, and the uncertainty is measured in D-S evidence theory. Second, the difference between BPAs is measured by Jousselme distance and a satisfying support function is proposed to get the support degree between each other effectively. Then the IFEPA operator is used for aggregating the original IFN and make a more reasonable decision. The proposed method is objective and reasonable because it is completely driven by data once some parameters are required. At the same time, it is novel and interesting. Finally, an application of developed models to the 'One Belt, One road' investment decision-making problems is presented to illustrate the effectiveness and feasibility of the proposed operator.

Data-Based Decision Making in Education: Challenges and Opportunities

ERIC Educational Resources Information Center

Schildkamp, Kim, Ed.; Lai, Mei Kuin, Ed.; Earl, Lorna, Ed.

2013-01-01

In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of…

Managing industrial risk--having a tested and proven system to prevent and assess risk.

PubMed

Heller, Stephen

2006-03-17

Some relatively easy techniques exist to improve the risk picture/profile to aid in preventing losses. Today with the advent of computer system resources, focusing on specific aspects of risk through systematic scoring and comparison, the risk analysis can be relatively easy to achieve. Techniques like these demonstrate how working experience and common sense can be combined mathematically into a flexible risk management tool or risk model for analyzing risk. The risk assessment methodology provided by companies today is no longer the ideas and practices of one group or even one company. It is reflective of the practice of many companies, as well as the ideas and expertise of academia and government regulators. The use of multi-criteria decision making (MCDM) techniques for making critical decisions has been recognized for many years for a variety of purposes. In today's computer age, the easy accessing and user-friendly nature for using these techniques, makes them a favorable choice for use in the risk assessment environment. The new user of these methodologies should find many ideas directly applicable to his or her needs when approaching risk decision making. The user should find their ideas readily adapted, with slight modification, to accurately reflect a specific situation using MCDM techniques. This makes them an attractive feature for use in assessment and risk modeling. The main advantage of decision making techniques, such as MCDM, is that in the early stages of a risk assessment, accurate data on industrial risk, and failures are lacking. In most cases, it is still insufficient to perform a thorough risk assessment using purely statistical concepts. The practical advantages towards deviating from strict data-driven protocol seem to outweigh the drawbacks. Industry failure data often comes at a high cost when a loss occurs. We can benefit from this unfortunate acquisition of data through the continuous refining of our decisions by incorporating this new information into our assessments. MCDM techniques offer flexibility in accessing comparison within broad data sets to reflect our best estimation of their importance towards contribution to the risk picture. This allows for the accurate determination of the more probable and more consequential issues. This can later be refined using more intensive risk techniques and the avoidance of less critical issues.

Simultaneous modeling of visual saliency and value computation improves predictions of economic choice.

PubMed

Towal, R Blythe; Mormann, Milica; Koch, Christof

2013-10-01

Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to the final decision. Moreover, the relative influence of saliency and value is unclear. Here we address these questions with an integrated model that combines a perceptual decision process about where and when to look with an economic decision process about what to choose. The perceptual decision process is modeled as a drift-diffusion model (DDM) process for each alternative. Using psychophysical data from a multiple-alternative, forced-choice task, in which subjects have to pick one food item from a crowded display via eye movements, we test four models where each DDM process is driven by (i) saliency or (ii) value alone or (iii) an additive or (iv) a multiplicative combination of both. We find that models including both saliency and value weighted in a one-third to two-thirds ratio (saliency-to-value) significantly outperform models based on either quantity alone. These eye fixation patterns modulate an economic decision process, also described as a DDM process driven by value. Our combined model quantitatively explains fixation patterns and choices with similar or better accuracy than previous models, suggesting that visual saliency has a smaller, but significant, influence than value and that saliency affects choices indirectly through perceptual decisions that modulate economic decisions.

Simultaneous modeling of visual saliency and value computation improves predictions of economic choice

PubMed Central

Towal, R. Blythe; Mormann, Milica; Koch, Christof

2013-01-01

Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to the final decision. Moreover, the relative influence of saliency and value is unclear. Here we address these questions with an integrated model that combines a perceptual decision process about where and when to look with an economic decision process about what to choose. The perceptual decision process is modeled as a drift–diffusion model (DDM) process for each alternative. Using psychophysical data from a multiple-alternative, forced-choice task, in which subjects have to pick one food item from a crowded display via eye movements, we test four models where each DDM process is driven by (i) saliency or (ii) value alone or (iii) an additive or (iv) a multiplicative combination of both. We find that models including both saliency and value weighted in a one-third to two-thirds ratio (saliency-to-value) significantly outperform models based on either quantity alone. These eye fixation patterns modulate an economic decision process, also described as a DDM process driven by value. Our combined model quantitatively explains fixation patterns and choices with similar or better accuracy than previous models, suggesting that visual saliency has a smaller, but significant, influence than value and that saliency affects choices indirectly through perceptual decisions that modulate economic decisions. PMID:24019496

Siting and Routing Assessment for Solid Waste Management Under Uncertainty Using the Grey Mini-Max Regret Criterion

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Davila, Eric

2006-10-01

Solid waste management (SWM) is at the forefront of environmental concerns in the Lower Rio Grande Valley (LRGV), South Texas. The complexity in SWM drives area decision makers to look for innovative and forward-looking solutions to address various waste management options. In decision analysis, it is not uncommon for decision makers to go by an option that may minimize the maximum regret when some determinant factors are vague, ambiguous, or unclear. This article presents an innovative optimization model using the grey mini-max regret (GMMR) integer programming algorithm to outline an optimal regional coordination of solid waste routing and possible landfill/incinerator construction under an uncertain environment. The LRGV is an ideal location to apply the GMMR model for SWM planning because of its constant urban expansion, dwindling landfill space, and insufficient data availability signifying the planning uncertainty combined with vagueness in decision-making. The results give local decision makers hedged sets of options that consider various forms of systematic and event-based uncertainty. By extending the dimension of decision-making, this may lead to identifying a variety of beneficial solutions with efficient waste routing and facility siting for the time frame of 2005 through 2010 in LRGV. The results show the ability of the GMMR model to open insightful scenario planning that can handle situational and data-driven uncertainty in a way that was previously unavailable. Research findings also indicate that the large capital investment of incineration facilities makes such an option less competitive among municipal options for landfills. It is evident that the investment from a municipal standpoint is out of the question, but possible public-private partnerships may alleviate this obstacle.

Embedded performance validity testing in neuropsychological assessment: Potential clinical tools.

PubMed

Rickards, Tyler A; Cranston, Christopher C; Touradji, Pegah; Bechtold, Kathleen T

2018-01-01

The article aims to suggest clinically-useful tools in neuropsychological assessment for efficient use of embedded measures of performance validity. To accomplish this, we integrated available validity-related and statistical research from the literature, consensus statements, and survey-based data from practicing neuropsychologists. We provide recommendations for use of 1) Cutoffs for embedded performance validity tests including Reliable Digit Span, California Verbal Learning Test (Second Edition) Forced Choice Recognition, Rey-Osterrieth Complex Figure Test Combination Score, Wisconsin Card Sorting Test Failure to Maintain Set, and the Finger Tapping Test; 2) Selecting number of performance validity measures to administer in an assessment; and 3) Hypothetical clinical decision-making models for use of performance validity testing in a neuropsychological assessment collectively considering behavior, patient reporting, and data indicating invalid or noncredible performance. Performance validity testing helps inform the clinician about an individual's general approach to tasks: response to failure, task engagement and persistence, compliance with task demands. Data-driven clinical suggestions provide a resource to clinicians and to instigate conversation within the field to make more uniform, testable decisions to further the discussion, and guide future research in this area.

Automated control of hierarchical systems using value-driven methods

NASA Technical Reports Server (NTRS)

Pugh, George E.; Burke, Thomas E.

1990-01-01

An introduction is given to the Value-driven methodology, which has been successfully applied to solve a variety of difficult decision, control, and optimization problems. Many real-world decision processes (e.g., those encountered in scheduling, allocation, and command and control) involve a hierarchy of complex planning considerations. For such problems it is virtually impossible to define a fixed set of rules that will operate satisfactorily over the full range of probable contingencies. Decision Science Applications' value-driven methodology offers a systematic way of automating the intuitive, common-sense approach used by human planners. The inherent responsiveness of value-driven systems to user-controlled priorities makes them particularly suitable for semi-automated applications in which the user must remain in command of the systems operation. Three examples of the practical application of the approach in the automation of hierarchical decision processes are discussed: the TAC Brawler air-to-air combat simulation is a four-level computerized hierarchy; the autonomous underwater vehicle mission planning system is a three-level control system; and the Space Station Freedom electrical power control and scheduling system is designed as a two-level hierarchy. The methodology is compared with rule-based systems and with other more widely-known optimization techniques.

Improved Lower Mekong River Basin Hydrological Decision Making Using NASA Satellite-based Earth Observation Systems

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Mohammed, I. N.; Srinivasan, R.; Lakshmi, V.

2017-12-01

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation. Index Terms: 1622: Earth system modeling; 1631: Land/atmosphere interactions; 1800: Hydrology; 1836 Hydrological cycles and budgets; 1840 Hydrometeorology; 1855: Remote sensing; 1866: Soil moisture; 6334: Regional Planning

Role of Science at EPA

EPA Pesticide Factsheets

Decision-making is driven by research with the highest standards for integrity, peer review, transparency, and ethics. Ongoing positive impacts include reducing pollution, improving air quality, defining exposure pathways, and protecting water sources.

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

PubMed Central

Teeguarden, Justin. G.; Tan, Yu-Mei; Edwards, Stephen W.; Leonard, Jeremy A.; Anderson, Kim A.; Corley, Richard A.; Harding, Anna K; Kile, Molly L.; Simonich, Staci M; Stone, David; Tanguay, Robert L.; Waters, Katrina M.; Harper, Stacey L.; Williams, David E.

2016-01-01

Synopsis Driven by major scientific advances in analytical methods, biomonitoring, computational tools, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the Aggregate Exposure Pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the Adverse Outcome Pathway (AOP) concept in the toxicological sciences. Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more efficient integration of exposure assessment and hazard identification. Together, the two pathways form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making. PMID:26759916

Completing the link between exposure science and toxicology for improved environmental health decision making: The aggregate exposure pathway framework

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

Teeguarden, Justin G.; Tan, Yu -Mei; Edwards, Stephen W.

Here, driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences.more » Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.« less

Completing the link between exposure science and toxicology for improved environmental health decision making: The aggregate exposure pathway framework

DOE PAGES

Teeguarden, Justin G.; Tan, Yu -Mei; Edwards, Stephen W.; ...

2016-01-13

Here, driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) concept in the toxicological sciences.more » Aggregate exposure pathways offer an intuitive framework to organize exposure data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathways and adverse outcome pathways, completing the source to outcome continuum for more meaningful integration of exposure assessment and hazard identification. Together, the two frameworks form and inform a decision-making framework with the flexibility for risk-based, hazard-based, or exposure-based decision making.« less

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

PubMed Central

Schöner, Gregor; Gail, Alexander

2012-01-01

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations. PMID:23166483

A Prototype for Content-Rich Decision-Making Support in NOAA using Data as an Asset

NASA Astrophysics Data System (ADS)

Austin, M.; Peng, G.

2015-12-01

Data Producers and Data Providers do not always collaborate to ensure that the data meets the needs of a broad range of user communities. User needs are not always considered in the beginning of the data production and delivery phases. Often data experts are required to explain or create custom output so that the data can be used by decision makers. Lack of documentation and quality information can result in poor user acceptance or data misinterpretation. This presentation will describe how new content integration tools have been created by NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) to improve quality throughout the data management lifecycle. The prototype integrates contents into a decision-making support tool from NOAA's Observing System Integrated Assessment (NOSIA) Value Tree, NOAA's Data Catalog/Digital Object Identifier (DOI) projects (collection-level metadata) and Data/Stewardship Maturity Matrices (Data and Stewardship Quality Rating Information). The National Centers for Environmental Information's (NCEI) Global Historical Climatology Network-Monthly (GHCN) dataset is used as a case study to formulate/develop the prototype tool and demonstrate its power with the content-centric approach in addition to completeness of metadata elements. This demonstrates the benefits of the prototype tool in both bottom roll-up and top roll-down fashion. The prototype tool delivers a standards based methodology that allows users to determine the quality and value of data that is fit for purpose. It encourages data producers and data providers/stewards to consider users' needs prior to data creation and dissemination resulting in user driven data requirements increasing return on investment.

Evaluation of the Performance Driven Budget Initiative of the New York City Board of Education. First Annual Report.

ERIC Educational Resources Information Center

Siegel, Dorothy; Naphtali, Zvia Segal; Fruchter, Norm; Berne, Robert

In 1996 the Chancellor introduced Performance Driven Budgeting (PDB) to the New York City schools. PDB is a form of decentralized budgetary decision making intended to provide local educators with increased control and flexibility over the use of resources. The plan established a framework of goals and principles, outlined a phased-in…

Investigating the application of Rasch theory in measuring change in middle school student performance in physical science

NASA Astrophysics Data System (ADS)

Cunningham, Jessica D.

Newton's Universe (NU), an innovative teacher training program, strives to obtain measures from rural, middle school science teachers and their students to determine the impact of its distance learning course on understanding of temperature. No consensus exists on the most appropriate and useful method of analysis to measure change in psychological constructs over time. Several item response theory (IRT) models have been deemed useful in measuring change, which makes the choice of an IRT model not obvious. The appropriateness and utility of each model, including a comparison to a traditional analysis of variance approach, was investigated using middle school science student performance on an assessment over an instructional period. Predetermined criteria were outlined to guide model selection based on several factors including research questions, data properties, and meaningful interpretations to determine the most appropriate model for this study. All methods employed in this study reiterated one common interpretation of the data -- specifically, that the students of teachers with any NU course experience had significantly greater gains in performance over the instructional period. However, clear distinctions were made between an analysis of variance and the racked and stacked analysis using the Rasch model. Although limited research exists examining the usefulness of the Rasch model in measuring change in understanding over time, this study applied these methods and detailed plausible implications for data-driven decisions based upon results for NU and others. Being mindful of the advantages and usefulness of each method of analysis may help others make informed decisions about choosing an appropriate model to depict changes to evaluate other programs. Results may encourage other researchers to consider the meaningfulness of using IRT for this purpose. Results have implications for data-driven decisions for future professional development courses, in science education and other disciplines. KEYWORDS: Item Response Theory, Rasch Model, Racking and Stacking, Measuring Change in Student Performance, Newton's Universe teacher training

Stop making plans; start making decisions.

PubMed

Mankins, Michael C; Steele, Richard

2006-01-01

Many executives have grown skeptical of strategic planning. Is it any wonder? Despite all the time and energy that go into it, strategic planning most often acts as a barrier to good decision making and does little to influence strategy. Strategic planning fails because of two factors: It typically occurs annually, and it focuses on individual business units. As such, the process is completely at odds with the way executives actually make important strategy decisions, which are neither constrained by the calendar nor defined by unit boundaries. Thus, according to a survey of 156 large companies, senior executives often make strategic decisions outside the planning process, in an ad hoc fashion and without rigorous analysis or productive debate. But companies can fix the process if they attack its root problems. A few forward-looking firms have thrown out their calendar-driven, business-unit-focused planning procedures and replaced them with continuous, issues-focused decision making. In doing so, they rely on several basic principles: They separate, but integrate, decision making and plan making. They focus on a few key themes. And they structure strategy reviews to produce real decisions. When companies change the timing and focus of strategic planning, they also change the nature of senior management's discussions about strategy--from "review and approve" to "debate and decide," in which top executives actively think through every major decision and its implications for the company's performance and value. The authors have found that these companies make more than twice as many important strategic decisions per year as companies that follow the traditional planning model.

The Effect of Career Decisions on Entrepreneurial Intention Levels of University Students Studying Sport Sciences

ERIC Educational Resources Information Center

Yildiz, Kadir

2018-01-01

This study investigates the entrepreneurial intention levels and career decisions of a sample of 340 university students studying sport sciences. Entrepreneurship refers to a career-related choice that is driven by a risk-taking and innovation imperative. Entrepreneurs of the future are expected to make their career related choices well before…

Life support decision making in critical care: Identifying and appraising the qualitative research evidence.

PubMed

Giacomini, Mita; Cook, Deborah; DeJean, Deirdre

2009-04-01

The objective of this study is to identify and appraise qualitative research evidence on the experience of making life-support decisions in critical care. In six databases and supplementary sources, we sought original research published from January 1990 through June 2008 reporting qualitative empirical studies of the experience of life-support decision making in critical care settings. Fifty-three journal articles and monographs were included. Of these, 25 reported prospective studies and 28 reported retrospective studies. We abstracted methodologic characteristics relevant to the basic critical appraisal of qualitative research (prospective data collection, ethics approval, purposive sampling, iterative data collection and analysis, and any method to corroborate findings). Qualitative research traditions represented include grounded theory (n = 15, 28%), ethnography or naturalistic methods (n = 15, 28%), phenomenology (n = 9, 17%), and other or unspecified approaches (n = 14, 26%). All 53 documents describe the research setting; 97% indicate purposive sampling of participants. Studies vary in their capture of multidisciplinary clinician and family perspectives. Thirty-one (58%) report research ethics board review. Only 49% report iterative data collection and analysis, and eight documents (15%) describe an analytically driven stopping point for data collection. Thirty-two documents (60%) indicated a method for corroborating findings. Qualitative evidence often appears outside of clinical journals, with most research from the United States. Prospective, observation-based studies follow life-support decision making directly. These involve a variety of participants and yield important insights into interactions, communication, and dynamics. Retrospective, interview-based studies lack this direct engagement, but focus on the recollections of fewer types of participants (particularly patients and physicians), and typically address specific issues (communication and stress). Both designs can provide useful reflections for improving care. Given the diversity of qualitative research in critical care, room for improvement exists regarding both the quality and transparency of reported methodology.

Big Data in Medicine is Driving Big Changes

PubMed Central

Verspoor, K.

2014-01-01

Summary Objectives To summarise current research that takes advantage of “Big Data” in health and biomedical informatics applications. Methods Survey of trends in this work, and exploration of literature describing how large-scale structured and unstructured data sources are being used to support applications from clinical decision making and health policy, to drug design and pharmacovigilance, and further to systems biology and genetics. Results The survey highlights ongoing development of powerful new methods for turning that large-scale, and often complex, data into information that provides new insights into human health, in a range of different areas. Consideration of this body of work identifies several important paradigm shifts that are facilitated by Big Data resources and methods: in clinical and translational research, from hypothesis-driven research to data-driven research, and in medicine, from evidence-based practice to practice-based evidence. Conclusions The increasing scale and availability of large quantities of health data require strategies for data management, data linkage, and data integration beyond the limits of many existing information systems, and substantial effort is underway to meet those needs. As our ability to make sense of that data improves, the value of the data will continue to increase. Health systems, genetics and genomics, population and public health; all areas of biomedicine stand to benefit from Big Data and the associated technologies. PMID:25123716

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

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

Luo, Y; McShan, D; Schipper, M

2014-06-01

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity tomore » different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for use in radiation treatments for other cancers. P01-CA59827;R01CA142840.« less

HPV vaccine introduction at the local level in a developing country: attitudes and criteria among key actors.

PubMed

Piñeros, Marion; Wiesner, Carolina; Cortés, Claudia; Trujillo, Lina María

2010-05-01

In most developing countries, HPV vaccines have been licensed but there are no national policy recommendations, nor is it clear how decisions on the introduction of this new vaccine are made. Decentralization processes in many Latin American countries favor decision-making at the local level. Through a qualitative study we explored knowledge regarding the HPV vaccine and the criteria that influence decision-making among local health actors in four regions of Colombia. We conducted a total of 14 in-depths interviews with different actors; for the analysis we performed content analysis. Results indicate that decision-making on the HPV vaccine at the local level has mainly been driven by pressure from local political actors, in a setting where there is low technical knowledge of the vaccine. This increases the risk of initiatives that may foster inequity. Local decisions and initiatives need to be strengthened technically and supported by national-level decisions, guidelines and follow-up.

Smart Manufacturing Technologies and Data Analytics for Improving Energy Efficiency in Industrial Energy Systems

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

Nimbalkar, Sachin U.; Guo, Wei; Wenning, Thomas J.

Smart manufacturing and advanced data analytics can help the manufacturing sector unlock energy efficiency from the equipment level to the entire manufacturing facility and the whole supply chain. These technologies can make manufacturing industries more competitive, with intelligent communication systems, real-time energy savings, and increased energy productivity. Smart manufacturing can give all employees in an organization the actionable information they need, when they need it, so that each person can contribute to the optimal operation of the corporation through informed, data-driven decision making. This paper examines smart technologies and data analytics approaches for improving energy efficiency and reducing energy costsmore » in process-supporting energy systems. It dives into energy-saving improvement opportunities through smart manufacturing technologies and sophisticated data collection and analysis. The energy systems covered in this paper include those with motors and drives, fans, pumps, air compressors, steam, and process heating.« less

A time for change: for the road to excellence for health care professionals.

PubMed

Nichols, D H

2001-01-01

This article addresses the changes affecting all of health care. Change should first be driven by data--data are what will be used to make clinical and business decisions that will result in better quality care. Employees should be held accountable for results, and celebrations should be provided for these changes. Customers have needs and goals that must be met, and if we do not meet the needs, our competition will. Management must understand the principles of quality and must encourage growth in employees. To bring change to your health care organization, you must embrace and encourage change.

Telehealth: When Technology Meets Health Care

MedlinePlus

... of digital information and communication technologies, such as computers and mobile devices, to access health care services ... your medical history may not be considered. The computer-driven decision-making model may not be optimal ...

The Impact of Data-Based Science Instruction on Standardized Test Performance

NASA Astrophysics Data System (ADS)

Herrington, Tia W.

Increased teacher accountability efforts have resulted in the use of data to improve student achievement. This study addressed teachers' inconsistent use of data-driven instruction in middle school science. Evidence of the impact of data-based instruction on student achievement and school and district practices has been well documented by researchers. In science, less information has been available on teachers' use of data for classroom instruction. Drawing on data-driven decision making theory, the purpose of this study was to examine whether data-based instruction impacted performance on the science Criterion Referenced Competency Test (CRCT) and to explore the factors that impeded its use by a purposeful sample of 12 science teachers at a data-driven school. The research questions addressed in this study included understanding: (a) the association between student performance on the science portion of the CRCT and data-driven instruction professional development, (b) middle school science teachers' perception of the usefulness of data, and (c) the factors that hindered the use of data for science instruction. This study employed a mixed methods sequential explanatory design. Data collected included 8th grade CRCT data, survey responses, and individual teacher interviews. A chi-square test revealed no improvement in the CRCT scores following the implementation of professional development on data-driven instruction (chi 2 (1) = .183, p = .67). Results from surveys and interviews revealed that teachers used data to inform their instruction, indicating time as the major hindrance to their use. Implications for social change include the development of lesson plans that will empower science teachers to deliver data-based instruction and students to achieve identified academic goals.

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

Pugmire, David; Kress, James; Choi, Jong

Data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. Data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. Here, this paper discusses initial research into visualization and analysis of distributed datamore » workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.« less

Risky Decisions Despite Counter Evidence: Modeling a Culture of Safer Sexual Practices

PubMed Central

Patel, Vimla L.; Yoskowitz, Nicole A.; Kaufman, David R.; Gutnik, Lily A.; Shortliffe, Edward H.

2005-01-01

To create a culture of safe practices, we need to understand how and under what conditions the public makes risky decisions about their health. Because risky sexual behaviors are known to be common in young adults, we investigated their decision making regarding sexual activities that could incur a high risk of HIV infection. Sixty young urban adults maintained journals for two weeks and were interviewed regarding condom use and sexual history. We characterized four patterns of condom use behavior: consistent (35.0%), inconsistent (16.7%), consistent to inconsistent (35.0%), and inconsistent to consistent (13.3%). Directionality of reasoning was analyzed in the explanations provided for condom use decisions. The consistent and inconsistent patterns were associated with data-driven heuristic reasoning, where behavior becomes automated and is associated with a high level of confidence in one’s judgment. In the other two patterns, the shift in behavior was due to a significant event that influenced a change in directionality to explanation-based reasoning. We discuss these results within the framework of identifying potentially high-risk groups for whom customized intervention strategies (such as computer-based educational programs) can be used to reduce risk, thereby creating a culture of safer sexual practices. PMID:16779109

Risky decisions despite counter evidence: modeling a culture of safer sexual practices.

PubMed

Patel, Vimla L; Yoskowitz, Nicole A; Kaufman, David R; Gutnik, Lily A; Shortliffe, Edward H

2005-01-01

To create a culture of safe practices, we need to understand how and under what conditions the public makes risky decisions about their health. Because risky sexual behaviors are known to be common in young adults, we investigated their decision making regarding sexual activities that could incur a high risk of HIV infection. Sixty young urban adults maintained journals for two weeks and were interviewed regarding condom use and sexual history. We characterized four patterns of condom use behavior: consistent (35.0%), inconsistent (16.7%), consistent to inconsistent (35.0%), and inconsistent to consistent (13.3%). Directionality of reasoning was analyzed in the explanations provided for condom use decisions. The consistent and inconsistent patterns were associated with data-driven heuristic reasoning, where behavior becomes automated and is associated with a high level of confidence in one's judgment. In the other two patterns, the shift in behavior was due to a significant event that influenced a change in directionality to explanation-based reasoning. We discuss these results within the framework of identifying potentially high-risk groups for whom customized intervention strategies (such as computer-based educational programs) can be used to reduce risk, thereby creating a culture of safer sexual practices.

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.

User Driven Data Mining, Visualization and Decision Making for NOAA Observing System and Data Investments

NASA Astrophysics Data System (ADS)

Austin, M.

2016-12-01

The National Oceanic and Atmospheric Administration (NOAA) observing system enterprise represents a $2.4B annual investment. Earth observations from these systems are foundational to NOAA's mission to describe, understand, and predict the Earth's environment. NOAA's decision makers are charged with managing this complex portfolio of observing systems to serve the national interest effectively and efficiently. The Technology Planning & Integration for Observation (TPIO) Office currently maintains an observing system portfolio for NOAA's validated user observation requirements, observing capabilities, and resulting data products and services. TPIO performs data analytics to provide NOAA leadership business case recommendations for making sound budgetary decisions. Over the last year, TPIO has moved from massive spreadsheets to intuitive dashboards that enable Federal agencies as well as the general public the ability to explore user observation requirements and environmental observing systems that monitor and predict changes in the environment. This change has led to an organizational data management shift to analytics and visualizations by allowing analysts more time to focus on understanding the data, discovering insights, and effectively communicating the information to decision makers. Moving forward, the next step is to facilitate a cultural change toward self-serve data sharing across NOAA, other Federal agencies, and the public using intuitive data visualizations that answer relevant business questions for users of NOAA's Observing System Enterprise. Users and producers of environmental data will become aware of the need for enhancing communication to simplify information exchange to achieve multipurpose goals across a variety of disciplines. NOAA cannot achieve its goal of producing environmental intelligence without data that can be shared by multiple user communities. This presentation will describe where we are on this journey and will provide examples of these visualizations, promoting a better understanding of NOAA's environmental sensing capabilities that enable improved communication to decision makers in an effective and intuitive manner.

Social-economical decision making in current and remitted major depression.

PubMed

Pulcu, E; Thomas, E J; Trotter, P D; McFarquhar, M; Juhasz, G; Sahakian, B J; Deakin, J F W; Anderson, I M; Zahn, R; Elliott, R

2015-04-01

Prosocial emotions related to self-blame are important in guiding human altruistic decisions. These emotions are elevated in major depressive disorder (MDD), such that MDD has been associated with guilt-driven pathological hyper-altruism. However, the impact of such emotional impairments in MDD on different types of social decision-making is unknown. In order to address this issue, we investigated different kinds of altruistic behaviour (interpersonal cooperation and fund allocation, altruistic punishment and charitable donation) in 33 healthy subjects, 35 patients in full remission (unmedicated) and 24 currently depressed patients (11 on medication) using behavioural-economical paradigms. We show a significant main effect of clinical status on altruistic decisions (p = 0.04) and a significant interaction between clinical status and type of altruistic decisions (p = 0.03). More specifically, symptomatic patients defected significantly more in the Prisoner's Dilemma game (p < 0.05) and made significantly lower charitable donations, whether or not these incurred a personal cost (p < 0.05 and p < 0.01, respectively). Currently depressed patients also reported significantly higher guilt elicited by receiving unfair financial offers in the Ultimatum Game (p < 0.05). Currently depressed individuals were less altruistic in both a charitable donation and an interpersonal cooperation task. Taken together, our results challenge the guilt-driven pathological hyper-altruism hypothesis in depression. There were also differences in both current and remitted patients in the relationship between altruistic behaviour and pathological self-blaming, suggesting an important role for these emotions in moral and social decision-making abnormalities in depression.

How do we trust strangers? The neural correlates of decision making and outcome evaluation of generalized trust

PubMed Central

Wang, Yiwen; Zhang, Zhen; Jing, Yiming; Valadez, Emilio A.

2016-01-01

This study investigates the brain correlates of decision making and outcome evaluation of generalized trust (i.e. trust in unfamiliar social agents)—a core component of social capital which facilitates civic cooperation and economic exchange. We measured 18 (9 male) Chinese participants’ event-related potentials while they played the role of the trustor in a one-shot trust game with unspecified social agents (trustees) allegedly selected from a large representative sample. At the decision-making phase, greater N2 amplitudes were found for trustors’ distrusting decisions compared to trusting decisions, which may reflect greater cognitive control exerted to distrust. Source localization identified the precentral gyrus as one possible neuronal generator of this N2 component. At the outcome evaluation phase, principal components analysis revealed that the so called feedback-related negativity was in fact driven by a reward positivity, which was greater in response to gain feedback compared to loss feedback. This reduced reward positivity following loss feedback may indicate that the absence of reward for trusting decisions was unexpected by the trustor. In addition, we found preliminary evidence suggesting that the decision-making processes may differ between high trustors and low trustors. PMID:27317927

An Interactive Platform to Visualize Data-Driven Clinical Pathways for the Management of Multiple Chronic Conditions.

PubMed

Zhang, Yiye; Padman, Rema

2017-01-01

Patients with multiple chronic conditions (MCC) pose an increasingly complex health management challenge worldwide, particularly due to the significant gap in our understanding of how to provide coordinated care. Drawing on our prior research on learning data-driven clinical pathways from actual practice data, this paper describes a prototype, interactive platform for visualizing the pathways of MCC to support shared decision making. Created using Python web framework, JavaScript library and our clinical pathway learning algorithm, the visualization platform allows clinicians and patients to learn the dominant patterns of co-progression of multiple clinical events from their own data, and interactively explore and interpret the pathways. We demonstrate functionalities of the platform using a cluster of 36 patients, identified from a dataset of 1,084 patients, who are diagnosed with at least chronic kidney disease, hypertension, and diabetes. Future evaluation studies will explore the use of this platform to better understand and manage MCC.

Comparative analysis of nursing and midwifery regulatory and professional bodies' scope of practice and associated decision-making frameworks: a discussion paper.

PubMed

Kennedy, Catriona; O'Reilly, Pauline; Fealy, Gerard; Casey, Mary; Brady, Anne-Marie; McNamara, Martin; Prizeman, Geraldine; Rohde, Daniela; Hegarty, Josephine

2015-08-01

To review, discuss and compare nursing and midwifery regulatory and professional bodies' scope of practice and associated decision-making frameworks. Scope of practice in professional nursing and midwifery is an evolving process which needs to be responsive to clinical, service, societal, demographic and fiscal changes. Codes and frameworks offer a system of rules and principles by which the nursing and midwifery professions are expected to regulate members and demonstrate responsibility to society. Discussion paper. Twelve scope of practice and associated decision-making frameworks (January 2000-March 2014). Two main approaches to the regulation of the scope of practice and associated decision-making frameworks exist internationally. The first approach is policy and regulation driven and behaviour oriented. The second approach is based on notions of autonomous decision-making, professionalism and accountability. The two approaches are not mutually exclusive, but have similar elements with a different emphasis. Both approaches lack explicit recognition of the aesthetic aspects of care and patient choice, which is a fundamental principle of evidence-based practice. Nursing organizations, regulatory authorities and nurses should recognize that scope of practice and the associated responsibility for decision-making provides a very public statement about the status of nursing in a given jurisdiction. © 2015 John Wiley & Sons Ltd.

A practical approach for active camera coordination based on a fusion-driven multi-agent system

NASA Astrophysics Data System (ADS)

Bustamante, Alvaro Luis; Molina, José M.; Patricio, Miguel A.

2014-04-01

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.

Ontology-Driven Knowledge-Based Health-Care System, An Emerging Area - Challenges And Opportunities - Indian Scenario

NASA Astrophysics Data System (ADS)

Sunitha, A.; Babu, G. Suresh

2014-11-01

Recent studies in the decision making efforts in the area of public healthcare systems have been tremendously inspired and influenced by the entry of ontology. Ontology driven systems results in the effective implementation of healthcare strategies for the policy makers. The central source of knowledge is the ontology containing all the relevant domain concepts such as locations, diseases, environments and their domain sensitive inter-relationships which is the prime objective, concern and the motivation behind this paper. The paper further focuses on the development of a semantic knowledge-base for public healthcare system. This paper describes the approach and methodologies in bringing out a novel conceptual theme in establishing a firm linkage between three different ontologies related to diseases, places and environments in one integrated platform. This platform correlates the real-time mechanisms prevailing within the semantic knowledgebase and establishing their inter-relationships for the first time in India. This is hoped to formulate a strong foundation for establishing a much awaited basic need for a meaningful healthcare decision making system in the country. Introduction through a wide range of best practices facilitate the adoption of this approach for better appreciation, understanding and long term outcomes in the area. The methods and approach illustrated in the paper relate to health mapping methods, reusability of health applications, and interoperability issues based on mapping of the data attributes with ontology concepts in generating semantic integrated data driving an inference engine for user-interfaced semantic queries.

Performance enhancement using a balanced scorecard in a Patient-centered Medical Home.

PubMed

Fields, Scott A; Cohen, Deborah

2011-01-01

Oregon Health & Science University Family Medicine implemented a balanced scorecard within our clinics that embraces the inherent tensions between care quality, financial productivity, and operational efficiency. This data-driven performance improvement process involved: (1) consensus-building around specific indicators to be measured, (2) developing and refining the balanced scorecard, and (3) using the balanced scorecard in the quality improvement process. Developing and implementing the balanced scorecard stimulated an important culture shift among clinics; practice members now actively use data to recognize successes, understand emerging problems, and make changes in response to these problems. Our experience shows how Patient-centered Medical Homes can be enhanced through use of information technology and evidence-based tools that support improved decision making and performance and help practices develop into learning organizations.

Making things difficult in lexical decision: the impact of pseudohomophones and transposed-letter nonwords on frequency and semantic priming effects.

PubMed

Lupker, Stephen J; Pexman, Penny M

2010-09-01

Performance in a lexical decision task is crucially dependent on the difficulty of the word-nonword discrimination. More wordlike nonwords cause not only a latency increase for words but also, as reported by Stone and Van Orden (1993), larger word frequency effects. Several current models of lexical decision making can explain these types of results in terms of a single mechanism, a mechanism driven by the nature of the interactions within the lexicon. In 2 experiments, we replicated Stone and Van Orden's increased frequency effect using both pseudohomophones (e.g., BEEST) and transposed-letter nonwords (e.g., JUGDE) as the more wordlike nonwords. In a 3rd experiment, we demonstrated that simply increasing word latencies without changing the difficulty of the word-nonword discrimination does not produce larger frequency effects. These results are reasonably consistent with many current models. In contrast, neither pseudohomophones nor transposed-letter nonwords altered the size of semantic priming effects across 4 additional experiments, posing a challenge to models that would attempt to explain both nonword difficulty effects and semantic priming effects in lexical decision tasks in terms of a single, lexically driven mechanism. (c) 2010 APA, all rights reserved).

Skin cancer screening: recommendations for data-driven screening guidelines and a review of the US Preventive Services Task Force controversy

PubMed Central

Johnson, Mariah M; Leachman, Sancy A; Aspinwall, Lisa G; Cranmer, Lee D; Curiel-Lewandrowski, Clara; Sondak, Vernon K; Stemwedel, Clara E; Swetter, Susan M; Vetto, John; Bowles, Tawnya; Dellavalle, Robert P; Geskin, Larisa J; Grossman, Douglas; Grossmann, Kenneth F; Hawkes, Jason E; Jeter, Joanne M; Kim, Caroline C; Kirkwood, John M; Mangold, Aaron R; Meyskens, Frank; Ming, Michael E; Nelson, Kelly C; Piepkorn, Michael; Pollack, Brian P; Robinson, June K; Sober, Arthur J; Trotter, Shannon; Venna, Suraj S; Agarwala, Sanjiv; Alani, Rhoda; Averbook, Bruce; Bar, Anna; Becevic, Mirna; Box, Neil; E Carson, William; Cassidy, Pamela B; Chen, Suephy C; Chu, Emily Y; Ellis, Darrel L; Ferris, Laura K; Fisher, David E; Kendra, Kari; Lawson, David H; Leming, Philip D; Margolin, Kim A; Markovic, Svetomir; Martini, Mary C; Miller, Debbie; Sahni, Debjani; Sharfman, William H; Stein, Jennifer; Stratigos, Alexander J; Tarhini, Ahmad; Taylor, Matthew H; Wisco, Oliver J; Wong, Michael K

2017-01-01

Melanoma is usually apparent on the skin and readily detected by trained medical providers using a routine total body skin examination, yet this malignancy is responsible for the majority of skin cancer-related deaths. Currently, there is no national consensus on skin cancer screening in the USA, but dermatologists and primary care providers are routinely confronted with making the decision about when to recommend total body skin examinations and at what interval. The objectives of this paper are: to propose rational, risk-based, data-driven guidelines commensurate with the US Preventive Services Task Force screening guidelines for other disorders; to compare our proposed guidelines to recommendations made by other national and international organizations; and to review the US Preventive Services Task Force's 2016 Draft Recommendation Statement on skin cancer screening. PMID:28758010

Viewpoint: Availability of oestrogen receptor and HER2 status for the breast multidisciplinary meeting discussion; time to get it right.

PubMed

Francis, A; Bartlett, J; Rea, D; Pinder, S E; Stein, R C; Stobart, H; Purdie, C A; Rakha, E; Thompson, A; Shaaban, A M

2016-07-01

The efficacy and pivotal role of the multidisciplinary meeting (MDM) in informed decision making is well established. It aims to provide a forum in which clinical evidence combines with individual patient data to create a personalized treatment plan. It does not fulfil this role adequately when undertaken without the full results of the patient's investigations being available. Neither doctor nor patient can make an informed decision about treatment options without knowledge of the tumour receptor status. Both targeted therapies and the aim to treat a majority of patients within clinical trials must now drive MDM decision making to be based on accuracy and best available treatment choices. A fully informed decision on treatment delayed by 1-2 weeks is clearly preferable to rushed time target-driven decisions made without the patient being offered a fully informed choice as ratified by a multidisciplinary team. Whilst the early anxiety of waiting for all relevant information to be available may be stressful for patients, not being sure that they have been offered fully informed treatment choices is also stressful and could cause longer lasting anxiety both during and after treatment. MDMs need to develop (along with targeted therapies) to retain their role as a forum whereby patients receive a correct, but specifically a full diagnosis and allow a fully informed discussion of all treatment options, including pre-operative clinical trials. Copyright © 2016 Elsevier Ltd. All rights reserved.

"Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology".

PubMed

Lin, Ying Ling; Guerguerian, Anne-Marie; Tomasi, Jessica; Laussen, Peter; Trbovich, Patricia

2017-08-14

Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks. Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues. Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived. Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians' decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids.

Purchase decision-making is modulated by vestibular stimulation.

PubMed

Preuss, Nora; Mast, Fred W; Hasler, Gregor

2014-01-01

Purchases are driven by consumers' product preferences and price considerations. Using caloric vestibular stimulation (CVS), we investigated the role of vestibular-affective circuits in purchase decision-making. CVS is an effective noninvasive brain stimulation method, which activates vestibular and overlapping emotional circuits (e.g., the insular cortex and the anterior cingulate cortex (ACC)). Subjects were exposed to CVS and sham stimulation while they performed two purchase decision-making tasks. In Experiment 1 subjects had to decide whether to purchase or not. CVS significantly reduced probability of buying a product. In Experiment 2 subjects had to rate desirability of the products and willingness to pay (WTP) while they were exposed to CVS and sham stimulation. CVS modulated desirability of the products but not WTP. The results suggest that CVS interfered with emotional circuits and thus attenuated the pleasant and rewarding effect of acquisition, which in turn reduced purchase probability. The present findings contribute to the rapidly growing literature on the neural basis of purchase decision-making.

Purchase decision-making is modulated by vestibular stimulation

PubMed Central

Preuss, Nora; Mast, Fred W.; Hasler, Gregor

2014-01-01

Purchases are driven by consumers’ product preferences and price considerations. Using caloric vestibular stimulation (CVS), we investigated the role of vestibular-affective circuits in purchase decision-making. CVS is an effective noninvasive brain stimulation method, which activates vestibular and overlapping emotional circuits (e.g., the insular cortex and the anterior cingulate cortex (ACC)). Subjects were exposed to CVS and sham stimulation while they performed two purchase decision-making tasks. In Experiment 1 subjects had to decide whether to purchase or not. CVS significantly reduced probability of buying a product. In Experiment 2 subjects had to rate desirability of the products and willingness to pay (WTP) while they were exposed to CVS and sham stimulation. CVS modulated desirability of the products but not WTP. The results suggest that CVS interfered with emotional circuits and thus attenuated the pleasant and rewarding effect of acquisition, which in turn reduced purchase probability. The present findings contribute to the rapidly growing literature on the neural basis of purchase decision-making. PMID:24600365

Data-driven modeling of hydroclimatic trends and soil moisture: Multi-scale data integration and decision support

NASA Astrophysics Data System (ADS)

Coopersmith, Evan Joseph

The techniques and information employed for decision-making vary with the spatial and temporal scope of the assessment required. In modern agriculture, the farm owner or manager makes decisions on a day-to-day or even hour-to-hour basis for dozens of fields scattered over as much as a fifty-mile radius from some central location. Following precipitation events, land begins to dry. Land-owners and managers often trace serpentine paths of 150+ miles every morning to inspect the conditions of their various parcels. His or her objective lies in appropriate resource usage -- is a given tract of land dry enough to be workable at this moment or would he or she be better served waiting patiently? Longer-term, these owners and managers decide upon which seeds will grow most effectively and which crops will make their operations profitable. At even longer temporal scales, decisions are made regarding which fields must be acquired and sold and what types of equipment will be necessary in future operations. This work develops and validates algorithms for these shorter-term decisions, along with models of national climate patterns and climate changes to enable longer-term operational planning. A test site at the University of Illinois South Farms (Urbana, IL, USA) served as the primary location to validate machine learning algorithms, employing public sources of precipitation and potential evapotranspiration to model the wetting/drying process. In expanding such local decision support tools to locations on a national scale, one must recognize the heterogeneity of hydroclimatic and soil characteristics throughout the United States. Machine learning algorithms modeling the wetting/drying process must address this variability, and yet it is wholly impractical to construct a separate algorithm for every conceivable location. For this reason, a national hydrological classification system is presented, allowing clusters of hydroclimatic similarity to emerge naturally from annual regime curve data and facilitate the development of cluster-specific algorithms. Given the desire to enable intelligent decision-making at any location, this classification system is developed in a manner that will allow for classification anywhere in the U.S., even in an ungauged basin. Daily time series data from 428 catchments in the MOPEX database are analyzed to produce an empirical classification tree, partitioning the United States into regions of hydroclimatic similarity. In constructing a classification tree based upon 55 years of data, it is important to recognize the non-stationary nature of climate data. The shifts in climatic regimes will cause certain locations to shift their ultimate position within the classification tree, requiring decision-makers to alter land usage, farming practices, and equipment needs, and algorithms to adjust accordingly. This work adapts the classification model to address the issue of regime shifts over larger temporal scales and suggests how land-usage and farming protocol may vary from hydroclimatic shifts in decades to come. Finally, the generalizability of the hydroclimatic classification system is tested with a physically-based soil moisture model calibrated at several locations throughout the continental United States. The soil moisture model is calibrated at a given site and then applied with the same parameters at other sites within and outside the same hydroclimatic class. The model's performance deteriorates minimally if the calibration and validation location are within the same hydroclimatic class, but deteriorates significantly if the calibration and validates sites are located in different hydroclimatic classes. These soil moisture estimates at the field scale are then further refined by the introduction of LiDAR elevation data, distinguishing faster-drying peaks and ridges from slower-drying valleys. The inclusion of LiDAR enabled multiple locations within the same field to be predicted accurately despite non-identical topography. This cross-application of parametric calibrations and LiDAR-driven disaggregation facilitates decision-support at locations without proximally-located soil moisture sensors.

The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives.

PubMed

Rusyn, Ivan; Greene, Nigel

2018-02-01

The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Empirically derived guidance for social scientists to influence environmental policy

PubMed Central

Brown, Katrina; Crissman, Charles; De Young, Cassandra; Gooch, Margaret; James, Craig; Jessen, Sabine; Johnson, Dave; Marshall, Paul; Wachenfeld, Dave; Wrigley, Damian

2017-01-01

Failure to stem trends of ecological disruption and associated loss of ecosystem services worldwide is partly due to the inadequate integration of the human dimension into environmental decision-making. Decision-makers need knowledge of the human dimension of resource systems and of the social consequences of decision-making if environmental management is to be effective and adaptive. Social scientists have a central role to play, but little guidance exists to help them influence decision-making processes. We distil 348 years of cumulative experience shared by 31 environmental experts across three continents into advice for social scientists seeking to increase their influence in the environmental policy arena. Results focus on the importance of process, engagement, empathy and acumen and reveal the importance of understanding and actively participating in policy processes through co-producing knowledge and building trust. The insights gained during this research might empower a science-driven cultural change in science-policy relations for the routine integration of the human dimension in environmental decision making; ultimately for an improved outlook for earth’s ecosystems and the billions of people that depend on them. PMID:28278238

Evidence of strategic periodicities in collective conflict dynamics.

PubMed

Dedeo, Simon; Krakauer, David; Flack, Jessica

2011-09-07

We analyse the timescales of conflict decision-making in a primate society. We present evidence for multiple, periodic timescales associated with social decision-making and behavioural patterns. We demonstrate the existence of periodicities that are not directly coupled to environmental cycles or known ultraridian mechanisms. Among specific biological and socially defined demographic classes, periodicities span timescales between hours and days. Our results indicate that these periodicities are not driven by exogenous or internal regularities but are instead driven by strategic responses to social interaction patterns. Analyses also reveal that a class of individuals, playing a critical functional role, policing, have a signature timescale of the order of 1 h. We propose a classification of behavioural timescales analogous to those of the nervous system, with high frequency, or α-scale, behaviour occurring on hour-long scales, through to multi-hour, or β-scale, behaviour, and, finally γ periodicities observed on a timescale of days.

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.

Decision-making in social contexts in youth with ADHD.

PubMed

Ma, Ili; Lambregts-Rommelse, Nanda N J; Buitelaar, Jan K; Cillessen, Antonius H N; Scheres, Anouk P J

2017-03-01

This study examined reward-related decision-making in children and adolescents with ADHD in a social context, using economic games. We furthermore examined the role of individual differences in reward-related decision-making, specifically, the roles of reward sensitivity and prosocial skills. Children and adolescents (9-17 years) with ADHD-combined subtype (n = 29; 20 boys) and healthy controls (n = 38; 20 boys) completed the ultimatum game and dictator game as measures of reward-related decision-making in social contexts. Prosocial skills were measured with the Interpersonal Reactivity Index. The ADHD group had a larger discrepancy between ultimatum game and dictator game offers than controls, indicating strategic rather than fairness driven decisions. This finding was supported by self-reports showing fewer individuals with ADHD than controls who considered fairness as motive for the decisions. Perspective taking or empathic concern did not differ between groups and was not significantly associated with offers. In conclusion, the results suggest that rather than a failure to understand the perspective of others, children and adolescents with ADHD were less motivated by fairness than controls in simple social situations. Results encourage the use of economic games in ADHD research.

Neural dynamics of social tie formation in economic decision-making.

PubMed

Bault, Nadège; Pelloux, Benjamin; Fahrenfort, Johannes J; Ridderinkhof, K Richard; van Winden, Frans

2015-06-01

The disposition for prosocial conduct, which contributes to cooperation as arising during social interaction, requires cortical network dynamics responsive to the development of social ties, or care about the interests of specific interaction partners. Here, we formulate a dynamic computational model that accurately predicted how tie formation, driven by the interaction history, influences decisions to contribute in a public good game. We used model-driven functional MRI to test the hypothesis that brain regions key to social interactions keep track of dynamics in tie strength. Activation in the medial prefrontal cortex (mPFC) and posterior cingulate cortex tracked the individual's public good contributions. Activation in the bilateral posterior superior temporal sulcus (pSTS), and temporo-parietal junction was modulated parametrically by the dynamically developing social tie-as estimated by our model-supporting a role of these regions in social tie formation. Activity in these two regions further reflected inter-individual differences in tie persistence and sensitivity to behavior of the interaction partner. Functional connectivity between pSTS and mPFC activations indicated that the representation of social ties is integrated in the decision process. These data reveal the brain mechanisms underlying the integration of interaction dynamics into a social tie representation which in turn influenced the individual's prosocial decisions. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Attention as foraging for information and value

PubMed Central

Manohar, Sanjay G.; Husain, Masud

2013-01-01

What is the purpose of attention? One avenue of research has led to the proposal that attention might be crucial for gathering information about the environment, while other lines of study have demonstrated how attention may play a role in guiding behavior to rewarded options. Many experiments that study attention require participants to make a decision based on information acquired discretely at one point in time. In real-world situations, however, we are usually not presented with information about which option to select in such a manner. Rather we must initially search for information, weighing up reward values of options before we commit to a decision. Here, we propose that attention plays a role in both foraging for information and foraging for value. When foraging for information, attention is guided toward the unknown. When foraging for reward, attention is guided toward high reward values, allowing decision-making to proceed by accept-or-reject decisions on the currently attended option. According to this account, attention can be regarded as a low-cost alternative to moving around and physically interacting with the environment—“teleforaging”—before a decision is made to interact physically with the world. To track the timecourse of attention, we asked participants to seek out and acquire information about two gambles by directing their gaze, before choosing one of them. Participants often made multiple refixations on items before making a decision. Their eye movements revealed that early in the trial, attention was guided toward information, i.e., toward locations that reduced uncertainty about value. In contrast, late in the trial, attention was guided by expected value of the options. At the end of the decision period, participants were generally attending to the item they eventually chose. We suggest that attentional foraging shifts from an uncertainty-driven to a reward-driven mode during the evolution of a decision, permitting decisions to be made by an engage-or-search strategy. PMID:24204335

A unified framework for addiction: Vulnerabilities in the decision process

PubMed Central

Redish, A. David; Jensen, Steve; Johnson, Adam

2013-01-01

The understanding of decision-making systems has come together in recent years to form a unified theory of decision-making in the mammalian brain as arising from multiple, interacting systems (a planning system, a habit system, and a situation-recognition system). This unified decision-making system has multiple potential access points through which it can be driven to make maladaptive choices, particularly choices that entail seeking of certain drugs or behaviors. We identify 10 key vulnerabilities in the system: (1) moving away from homeostasis, (2) changing allostatic set points, (3) euphorigenic “reward-like” signals, (4) overvaluation in the planning system, (5) incorrect search of situation-action-outcome relationships, (6) misclassification of situations, (7) overvaluation in the habit system, (8) a mismatch in the balance of the two decision systems, (9) over-fast discounting processes, and (10) changed learning rates. These vulnerabilities provide a taxonomy of potential problems with decision-making systems. Although each vulnerability can drive an agent to return to the addictive choice, each vulnerability also implies a characteristic symptomology. Different drugs, different behaviors, and different individuals are likely to access different vulnerabilities. This has implications for an individual’s susceptibility to addiction and the transition to addiction, for the potential for relapse, and for the potential for treatment. PMID:18662461

Data-Based Decision-Making: Developing a Method for Capturing Teachers' Understanding of CBM Graphs

ERIC Educational Resources Information Center

Espin, Christine A.; Wayman, Miya Miura; Deno, Stanley L.; McMaster, Kristen L.; de Rooij, Mark

2017-01-01

In this special issue, we explore the decision-making aspect of "data-based decision-making". The articles in the issue address a wide range of research questions, designs, methods, and analyses, but all focus on data-based decision-making for students with learning difficulties. In this first article, we introduce the topic of…

Establishing the connection between crowd-sourced data and decision makers

NASA Astrophysics Data System (ADS)

Paxton, L. J.; Swartz, W.; Strong, S. B.; Nix, M. G.; Schaefer, R. K.; Weiss, M.

2014-12-01

There are many challenges in using, developing, and ensuring the viability of crowd-sourced data. Establishing and maintaining relevance is one of them but each participant in the challenge has different criteria for relevance. Consider, for example, the collection of data using smart phones. Some participants just like to contribute to something they consider good for the community. How do you engender that commitment? This becomes especially problematic when an additional sensor may need to be added to the smart phone. Certainly the humanitarian-egalitarian may be willing to "buy-in" but what value does it hold for the entrepreneurial-individualist? Another challenge is that of the crowd-sourced data themselves. Most readily available apps collect only one kind of data. The frontier lies in not only aggregating the data from those devices but in fusing the data with other data types (e.g. satellite imagery, installed sensors, radars, etc.). Doing this requires resources and the establishment and negotiation of data rights, how data are valued, how data are used, and the model used for support of the process (e.g. profit-driven, communal, scientific, etc.). In this talk we will discuss a few problems that we have looked at wherein distributed sensor networks provide potential value, data fusion is a "value multiplier" of those crowd-sourced data and how we make that connection to decision makers. We have explored active decision making through our Global Assimilation of Information for Action project (see our old website http://gaia.jhuapl.edu) and the use of "serious games" to establish affinities and illuminate opportunities and issues. We assert that the field of dreams approach ("build it and they will come") is not a sufficiently robust approach; the decision-makers (or paying customers) must be involved in the process of defining the data system products and quantifying the value proposition for their clients.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Data Mining for Understanding and Impriving Decision-Making Affecting Ground Delay Programs

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak; Wang, Yao Xun; Sridhar, Banavar

2013-01-01

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions. The work reported in this paper is focused on ground delay programs. Data mining algorithms have the potential to develop associations between weather patterns and the corresponding ground delay program responses. If successful, they can be used to improve and standardize TFM decision resulting in better predictability of traffic flows on days with reliable weather forecasts. The approach here seeks to develop a set of data mining and machine learning models and apply them to historical archives of weather observations and forecasts and TFM initiatives to determine the extent to which the theory can predict and explain the observed traffic flow behaviors.

Fronting Integrated Scientific Web Applications: Design Features and Benefits for Regulatory Environments

EPA Science Inventory

Integrated decision support systems for regulatory applications benefit from standardindustry practices such as code reuse, test-driven development, and modularization. Theseapproaches make meeting the federal government’s goals of transparency, efficiency, and quality assurance ...

Guidelines for Datacenter Energy Information System

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

Singh, Reshma; Mahdavi, Rod; Mathew, Paul

2013-12-01

The purpose of this document is to provide structured guidance to data center owners, operators, and designers, to empower them with information on how to specify and procure data center energy information systems (EIS) for managing the energy utilization of their data centers. Data centers are typically energy-intensive facilities that can consume up to 100 times more energy per unit area than a standard office building (FEMP 2013). This guidance facilitates “data-driven decision making,” which will be enabled by following the approach outlined in the guide. This will bring speed, clarity, and objectivity to any energy or asset management decisionsmore » because of the ability to monitor and track an energy management project’s performance.« less

The value of artefacts in stimulated-recall interviews.

PubMed

Burden, Sarah; Topping, Annie; O'Halloran, Catherine

2015-09-01

To assess the use of artefacts in semi-structured, stimulated-recall interviews in a study exploring mentors' decisions regarding students' competence in practice. Few empirical studies have examined how mentors reach a decision when assessing students' performance in practice. Concerns have repeatedly been voiced that students may lack essential skills at the point of registration or that mentors may have failed or been reticent to judge students' performance as unsatisfactory. Student practice assessment documents (PADs) were used in stimulated-recall (SR) interviews with mentors to explore decision making. A review of the literature identified that artefacts can play a role in triggering a more comprehensive retrospective examination of decision making, thus helping to capture the essence of a mentor's decision over time and in context. Use of an artefact to stimulate recall can elicit evidence of thought processes, which may be difficult to obtain in a normal, semi-structured interview. PADs proved to be a valuable way to generate naturalistic decision making. In addition, discussion of artefacts created by participants can promote participant-driven enquiry, thereby reducing researcher bias. Identifying an approach that captures post hoc decision making based on sustained engagement and interaction between students and their mentors was a challenge. Artefacts can be used to address the difficulties associated with retrospective introspection about a unique decision. There is the potential to increase the use of artefacts in healthcare research. SR can also help novice mentors develop their skills in making decisions regarding assessments of students.

Reply to the comment by B. Ghobadipour and B. Mojarradi "M. Abedi, S.A. Torabi, G.-H. Norouzi and M. Hamzeh; ELECTRE III: A knowledge-driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping"

NASA Astrophysics Data System (ADS)

Abedi, Maysam

2015-06-01

This reply discusses the results of two previously developed approaches in mineral prospectivity/potential mapping (MPM), i.e., ELECTRE III and PROMETHEE II as well-known methods in multi-criteria decision-making (MCDM) problems. Various geo-data sets are integrated to prepare MPM in which generated maps have acceptable matching with the drilled boreholes. Equal performance of the applied methods is indicated in the studied case. Complementary information of these methods is also provided in order to help interested readers to implement them in MPM process.

Psychopathic individuals exhibit but do not avoid regret during counterfactual decision making.

PubMed

Baskin-Sommers, Arielle; Stuppy-Sullivan, Allison M; Buckholtz, Joshua W

2016-12-13

Psychopathy is associated with persistent antisocial behavior and a striking lack of regret for the consequences of that behavior. Although explanatory models for psychopathy have largely focused on deficits in affective responsiveness, recent work indicates that aberrant value-based decision making may also play a role. On that basis, some have suggested that psychopathic individuals may be unable to effectively use prospective simulations to update action value estimates during cost-benefit decision making. However, the specific mechanisms linking valuation, affective deficits, and maladaptive decision making in psychopathy remain unclear. Using a counterfactual decision-making paradigm, we found that individuals who scored high on a measure of psychopathy were as or more likely than individuals low on psychopathy to report negative affect in response to regret-inducing counterfactual outcomes. However, despite exhibiting intact affective regret sensitivity, they did not use prospective regret signals to guide choice behavior. In turn, diminished behavioral regret sensitivity predicted a higher number of prior incarcerations, and moderated the relationship between psychopathy and incarceration history. These findings raise the possibility that maladaptive decision making in psychopathic individuals is not a consequence of their inability to generate or experience negative emotions. Rather, antisocial behavior in psychopathy may be driven by a deficit in the generation of forward models that integrate information about rules, costs, and goals with stimulus value representations to promote adaptive behavior.

Psychopathic individuals exhibit but do not avoid regret during counterfactual decision making

PubMed Central

Baskin-Sommers, Arielle; Stuppy-Sullivan, Allison M.; Buckholtz, Joshua W.

2016-01-01

Psychopathy is associated with persistent antisocial behavior and a striking lack of regret for the consequences of that behavior. Although explanatory models for psychopathy have largely focused on deficits in affective responsiveness, recent work indicates that aberrant value-based decision making may also play a role. On that basis, some have suggested that psychopathic individuals may be unable to effectively use prospective simulations to update action value estimates during cost–benefit decision making. However, the specific mechanisms linking valuation, affective deficits, and maladaptive decision making in psychopathy remain unclear. Using a counterfactual decision-making paradigm, we found that individuals who scored high on a measure of psychopathy were as or more likely than individuals low on psychopathy to report negative affect in response to regret-inducing counterfactual outcomes. However, despite exhibiting intact affective regret sensitivity, they did not use prospective regret signals to guide choice behavior. In turn, diminished behavioral regret sensitivity predicted a higher number of prior incarcerations, and moderated the relationship between psychopathy and incarceration history. These findings raise the possibility that maladaptive decision making in psychopathic individuals is not a consequence of their inability to generate or experience negative emotions. Rather, antisocial behavior in psychopathy may be driven by a deficit in the generation of forward models that integrate information about rules, costs, and goals with stimulus value representations to promote adaptive behavior. PMID:27911790

Predicting adverse hemodynamic events in critically ill patients.

PubMed

Yoon, Joo H; Pinsky, Michael R

2018-06-01

The art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized. Data mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains. Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

A conceptual framework for automating the operational and strategic decision-making process in the health care delivery system.

PubMed

Ruohonen, Toni; Ennejmy, Mohammed

2013-01-01

Making reliable and justified operational and strategic decisions is a really challenging task in the health care domain. So far, the decisions have been made based on the experience of managers and staff, or they are evaluated with traditional methods, using inadequate data. As a result of this kind of decision-making process, attempts to improve operations usually have failed or led to only local improvements. Health care organizations have a lot of operational data, in addition to clinical data, which is the key element for making reliable and justified decisions. However, it is progressively problematic to access it and make usage of it. In this paper we discuss about the possibilities how to exploit operational data in the most efficient way in the decision-making process. We'll share our future visions and propose a conceptual framework for automating the decision-making process.

Big data analytics in the building industry

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

Berger, Michael A.; Mathew, Paul A.; Walter, Travis

Catalyzed by recent market, technology, and policy trends, energy data collection in the building industry is becoming more widespread. This wealth of information allows more data-driven decision-making by designers, commissioning agents, facilities staff, and energy service providers during the course of building design, operation and retrofit. The U.S. Department of Energy’s Building Performance Database (BPD) has taken advantage of this wealth of building asset- and energy-related data by collecting, cleansing, and standardizing data from across the U.S. on over 870,00 buildings, and is designed to support building benchmarking, energy efficiency project design, and buildings-related policy development with real-world data. Here,more » this article explores the promises and perils energy professionals are faced with when leveraging such tools, presenting example analyses for commercial and residential buildings, highlighting potential issues, and discussing solutions and best practices that will enable designers, operators and commissioning agents to make the most of ‘big data’ resources such as the BPD.« less

Big data analytics in the building industry

DOE PAGES

Berger, Michael A.; Mathew, Paul A.; Walter, Travis

2016-07-01

Catalyzed by recent market, technology, and policy trends, energy data collection in the building industry is becoming more widespread. This wealth of information allows more data-driven decision-making by designers, commissioning agents, facilities staff, and energy service providers during the course of building design, operation and retrofit. The U.S. Department of Energy’s Building Performance Database (BPD) has taken advantage of this wealth of building asset- and energy-related data by collecting, cleansing, and standardizing data from across the U.S. on over 870,00 buildings, and is designed to support building benchmarking, energy efficiency project design, and buildings-related policy development with real-world data. Here,more » this article explores the promises and perils energy professionals are faced with when leveraging such tools, presenting example analyses for commercial and residential buildings, highlighting potential issues, and discussing solutions and best practices that will enable designers, operators and commissioning agents to make the most of ‘big data’ resources such as the BPD.« less

A Comprehensive Optimization Strategy for Real-time Spatial Feature Sharing and Visual Analytics in Cyberinfrastructure

NASA Astrophysics Data System (ADS)

Li, W.; Shao, H.

2017-12-01

For geospatial cyberinfrastructure enabled web services, the ability of rapidly transmitting and sharing spatial data over the Internet plays a critical role to meet the demands of real-time change detection, response and decision-making. Especially for the vector datasets which serve as irreplaceable and concrete material in data-driven geospatial applications, their rich geometry and property information facilitates the development of interactive, efficient and intelligent data analysis and visualization applications. However, the big-data issues of vector datasets have hindered their wide adoption in web services. In this research, we propose a comprehensive optimization strategy to enhance the performance of vector data transmitting and processing. This strategy combines: 1) pre- and on-the-fly generalization, which automatically determines proper simplification level through the introduction of appropriate distance tolerance (ADT) to meet various visualization requirements, and at the same time speed up simplification efficiency; 2) a progressive attribute transmission method to reduce data size and therefore the service response time; 3) compressed data transmission and dynamic adoption of a compression method to maximize the service efficiency under different computing and network environments. A cyberinfrastructure web portal was developed for implementing the proposed technologies. After applying our optimization strategies, substantial performance enhancement is achieved. We expect this work to widen the use of web service providing vector data to support real-time spatial feature sharing, visual analytics and decision-making.

Value-driven ERM: making ERM an engine for simultaneous value creation and value protection.

PubMed

Celona, John; Driver, Jeffrey; Hall, Edward

2011-01-01

Enterprise risk management (ERM) began as an effort to integrate the historically disparate silos of risk management in organizations. More recently, as recognition has grown of the need to cover the upside risks in value creation (financial and otherwise), organizations and practitioners have been searching for the means to do this. Existing tools such as heat maps and risk registers are not adequate for this task. Instead, a conceptually new value-driven framework is needed to realize the promise of enterprise-wide coverage of all risks, for both value protection and value creation. The methodology of decision analysis provides the means of capturing systemic, correlated, and value-creation risks on the same basis as value protection risks and has been integrated into the value-driven approach to ERM described in this article. Stanford Hospital and Clinics Risk Consulting and Strategic Decisions Group have been working to apply this value-driven ERM at Stanford University Medical Center. © 2011 American Society for Healthcare Risk Management of the American Hospital Association.

Goal driven kinematic simulation of flexible arm robot for space station missions

NASA Technical Reports Server (NTRS)

Janssen, P.; Choudry, A.

1987-01-01

Flexible arms offer a great degree of flexibility in maneuvering in the space environment. The problem of transporting an astronaut for extra-vehicular activity using a space station based flexible arm robot was studied. Inverse kinematic solutions of the multilink structure were developed. The technique is goal driven and can support decision making for configuration selection as required for stability and obstacle avoidance. Details of this technique and results are given.

Health and Retirement: Do Changes in Health Affect Retirement Expectations?

ERIC Educational Resources Information Center

McGarry, Kathleen

2004-01-01

Health plays a vital role in the decision making process of retirement for an employee. The changes in retirement expectations are driven to a much greater degree by change in health rather than change in income or wealth.

Troubleshooting Portfolios

ERIC Educational Resources Information Center

Crismond, David; Peterie, Matthew

2017-01-01

The Troubleshooting Portfolios approach was developed at the Olathe Northwest High School in Olathe, Kansas. This approach supports integrated STEM and "informed design" thinking and learning, in which students: (1) use design strategies effectively; (2) work creatively and collaboratively in teams; (3) make knowledge-driven decisions;…

Gambling with your life: the process of breast cancer treatment decision making in Chinese women.

PubMed

Lam, Wendy Wt; Fielding, Richard; Chan, Miranda; Chow, Louis; Or, Amy

2005-01-01

Treatment decision making (TDM) studies have primarily focused on assessing TDM quality and predominantly presume rational analytic processes as the gold standard. In a grounded theory study of 22 Hong Kong Chinese women following breast surgery who completed an in-depth interview exploring the process of TDM in breast cancer (BC), narrative data showed that discovery of a breast abnormality and emotional responses to BC diagnosis influence the TDM process. Lack of guidance from surgeons impaired TDM. Decisions were, for the most part, made using intuitive, pragmatic and emotionally driven criteria in the absence of complete information. The experience of TDM, which was likened to gambling, did not end once the decision was made but unfolded while waiting for surgery and the post-operative report. In this waiting period, women were emotionally overwhelmed by fear of death and the uncertainty of the surgical outcome, and equivocated over whether they had made the 'right' choice. This suggests that Chinese women feel they are gambling with their lives during TDM. These women are particularly emotionally vulnerable whilst waiting for their surgery and the post-surgical clinical pathology results. Providing emotional support is particularly important at this time when these women are overwhelmed by uncertainty. 2004 John Wiley & Sons, Ltd.

Analysis of the decision-making process of nurse managers: a collective reflection.

PubMed

Eduardo, Elizabete Araujo; Peres, Aida Maris; de Almeida, Maria de Lourdes; Roglio, Karina de Dea; Bernardino, Elizabeth

2015-01-01

to analyze the decision-making model adopted by nurses from the perspective of some decision-making process theories. qualitative approach, based on action research. Semi-structured questionnaires and seminars were conducted from April to June 2012 in order to understand the nature of decisions and the decision-making process of nine nurses in position of managers at a public hospital in Southern Brazil. Data were subjected to content analysis. data were classified in two categories: the current situation of decision-making, which showed a lack of systematization; the construction and collective decision-making, which emphasizes the need to develop a decision-making model. the decision-making model used by nurses is limited because it does not consider two important factors: the limits of human rationality, and the external and internal organizational environments that influence and determine right decisions.

Many faces of rationality: Implications of the great rationality debate for clinical decision-making.

PubMed

Djulbegovic, Benjamin; Elqayam, Shira

2017-10-01

Given that more than 30% of healthcare costs are wasted on inappropriate care, suboptimal care is increasingly connected to the quality of medical decisions. It has been argued that personal decisions are the leading cause of death, and 80% of healthcare expenditures result from physicians' decisions. Therefore, improving healthcare necessitates improving medical decisions, ie, making decisions (more) rational. Drawing on writings from The Great Rationality Debate from the fields of philosophy, economics, and psychology, we identify core ingredients of rationality commonly encountered across various theoretical models. Rationality is typically classified under umbrella of normative (addressing the question how people "should" or "ought to" make their decisions) and descriptive theories of decision-making (which portray how people actually make their decisions). Normative theories of rational thought of relevance to medicine include epistemic theories that direct practice of evidence-based medicine and expected utility theory, which provides the basis for widely used clinical decision analyses. Descriptive theories of rationality of direct relevance to medical decision-making include bounded rationality, argumentative theory of reasoning, adaptive rationality, dual processing model of rationality, regret-based rationality, pragmatic/substantive rationality, and meta-rationality. For the first time, we provide a review of wide range of theories and models of rationality. We showed that what is "rational" behaviour under one rationality theory may be irrational under the other theory. We also showed that context is of paramount importance to rationality and that no one model of rationality can possibly fit all contexts. We suggest that in context-poor situations, such as policy decision-making, normative theories based on expected utility informed by best research evidence may provide the optimal approach to medical decision-making, whereas in the context-rich circumstances other types of rationality, informed by human cognitive architecture and driven by intuition and emotions such as the aim to minimize regret, may provide better solution to the problem at hand. The choice of theory under which we operate is important as it determines both policy and our individual decision-making. © 2017 The Authors Journal of Evaluation in Clinical Practice Published by John Wiley & Sons Ltd.

An overview of future EU health systems. An insight into governance, primary care, data collection and citizens' participation.

PubMed

Quaglio, Gianluca; Figueras, Josep; Mantoan, Domenico; Dawood, Amr; Karapiperis, Theodoros; Costongs, Caroline; Bernal-Delgado, Enrique

2018-03-26

Health systems in the European Union (EU) are being questioned over their effectiveness and sustainability. In pursuing both goals, they have to conciliate coexisting, not always aligned, realities. This paper originated from a workshop entitled 'Health systems for the future' held at the European Parliament. Experts and decision makers were asked to discuss measures that may increase the effectiveness and sustainability of health systems, namely: (i) increasing citizens' participation; (ii) the importance of primary care in providing integrated services; (iii) improving the governance and (iv) fostering better data collection and information channels to support the decision making process. In the parliamentary debate, was discussed the concept that, in the near future, health systems' effectiveness and sustainability will very much depend on effective access to integrated services where primary care is pivotal, a clearer shift from care-oriented systems to health promotion and prevention, a profound commitment to good governance, particularly to stakeholders participation, and a systematic reuse of data meant to build health data-driven learning systems. Many health issues, such as future health systems in the EU, are potentially transformative and hence an intense political issue. It is policy-making leadership that will mostly determine how well EU health systems are prepared to face future challenges.

Neural signatures of experience-based improvements in deterministic decision-making.

PubMed

Tremel, Joshua J; Laurent, Patryk A; Wolk, David A; Wheeler, Mark E; Fiez, Julie A

2016-12-15

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural signatures of experience-based improvements in deterministic decision-making

PubMed Central

Tremel, Joshua J.; Laurent, Patryk A.; Wolk, David A.; Wheeler, Mark E.; Fiez, Julie A.

2016-01-01

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. PMID:27523644

Building University Capacity to Visualize Solutions to Complex Problems in the Arctic

NASA Astrophysics Data System (ADS)

Broderson, D.; Veazey, P.; Raymond, V. L.; Kowalski, K.; Prakash, A.; Signor, B.

2016-12-01

Rapidly changing environments are creating complex problems across the globe, which are particular magnified in the Arctic. These worldwide challenges can best be addressed through diverse and interdisciplinary research teams. It is incumbent on such teams to promote co-production of knowledge and data-driven decision-making by identifying effective methods to communicate their findings and to engage with the public. Decision Theater North (DTN) is a new semi-immersive visualization system that provides a space for teams to collaborate and develop solutions to complex problems, relying on diverse sets of skills and knowledge. It provides a venue to synthesize the talents of scientists, who gather information (data); modelers, who create models of complex systems; artists, who develop visualizations; communicators, who connect and bridge populations; and policymakers, who can use the visualizations to develop sustainable solutions to pressing problems. The mission of Decision Theater North is to provide a cutting-edge visual environment to facilitate dialogue and decision-making by stakeholders including government, industry, communities and academia. We achieve this mission by adopting a multi-faceted approach reflected in the theater's design, technology, networking capabilities, user support, community relationship building, and strategic partnerships. DTN is a joint project of Alaska's National Science Foundation Experimental Program to Stimulate Competitive Research (NSF EPSCoR) and the University of Alaska Fairbanks (UAF), who have brought the facility up to full operational status and are now expanding its development space to support larger team science efforts. Based in Fairbanks, Alaska, DTN is uniquely poised to address changes taking place in the Arctic and subarctic, and is connected with a larger network of decision theaters that include the Arizona State University Decision Theater Network and the McCain Institute in Washington, DC.

Decisions and desire.

PubMed

Morse, Gardiner

2006-01-01

When we make decisions, we're not always in charge. One moment we hotheadedly let our emotions get the better of us; the next, we're paralyzed by uncertainty. Then we'll pull a brilliant decision out of thin air--and wonder how we did it. Though we may have no idea how decision making happens, neuroscientists peering deep into our brains are beginning to get the picture. What they're finding may not be what you want to hear, but it's worth listening. We have dog brains, basically, with human cortexes stuck on top. By watching the brain in action as it deliberates and decides, neuroscientists are finding that not a second goes by that our animal brains aren't conferring with our modern cortexes to influence their choices. Scientists have discovered, for example, that the "reward" circuits in the brain that activate in response to cocaine, chocolate, sex, and music also find pleasure in the mere anticipation of making money--or getting revenge. And the "aversion" circuits that react to the threat of physical pain also respond with disgust when we feel cheated by a partner. In this article, HBR senior editor Gardiner Morse describes the experiments that illuminate the aggressive participation of our emotion-driven animal brains in decision making. This research also shows that our emotional brains needn't always operate beneath our radar. While our dog brains sometimes hijack our higher cognitive functions to drive bad, or at least illogical, decisions, they play an important part in rational decision making as well. The more we understand about how we make decisions, the better we can manage them.

A cross-cultural study of noblesse oblige in economic decision-making.

PubMed

Fiddick, Laurence; Cummins, Denise Dellarosa; Janicki, Maria; Lee, Sean; Erlich, Nicole

2013-09-01

A cornerstone of economic theory is that rational agents are self-interested, yet a decade of research in experimental economics has shown that economic decisions are frequently driven by concerns for fairness, equity, and reciprocity. One aspect of other-regarding behavior that has garnered attention is noblesse oblige, a social norm that obligates those of higher status to be generous in their dealings with those of lower status. The results of a cross-cultural study are reported in which marked noblesse oblige was observed on a reciprocal-contract decision-making task. Participants from seven countries that vary along hierarchical and individualist/collectivist social dimensions were more tolerant of non-reciprocation when they adopted a high-ranking perspective compared with a low-ranking perspective.

Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.

Economic inequality increases risk taking.

PubMed

Payne, B Keith; Brown-Iannuzzi, Jazmin L; Hannay, Jason W

2017-05-02

Rising income inequality is a global trend. Increased income inequality has been associated with higher rates of crime, greater consumer debt, and poorer health outcomes. The mechanisms linking inequality to poor outcomes among individuals are poorly understood. This research tested a behavioral account linking inequality to individual decision making. In three experiments ( n = 811), we found that higher inequality in the outcomes of an economic game led participants to take greater risks to try to achieve higher outcomes. This effect of unequal distributions on risk taking was driven by upward social comparisons. Next, we estimated economic risk taking in daily life using large-scale data from internet searches. Risk taking was higher in states with greater income inequality, an effect driven by inequality at the upper end of the income distribution. Results suggest that inequality may promote poor outcomes, in part, by increasing risky behavior.

Dynamics of Entropy in Quantum-like Model of Decision Making

NASA Astrophysics Data System (ADS)

Basieva, Irina; Khrennikov, Andrei; Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu

2011-03-01

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices. By using this equilibrium point Alice determines her mixed (i.e., probabilistic) strategy with respect to Bob. Thus our model is a model of thinking through decoherence of initially pure mental state. Decoherence is induced by interaction with memory and external environment. In this paper we study (numerically) dynamics of quantum entropy of Alice's state in the process of decision making. Our analysis demonstrates that this dynamics depends nontrivially on the initial state of Alice's mind on her own actions and her prediction state (for possible actions of Bob.)

Effective crisis decision-making.

PubMed

Kaschner, Holger

2017-01-01

When an organisation's reputation is at stake, crisis decision-making (CDM) is challenging and prone to failure. Most CDM schemes are strong at certain aspects of the overall CDM process, but almost none are strong at all of them. This paper defines criteria for good CDM schemes, analyses common approaches and introduces an alternative, stakeholder-driven scheme. Focusing on the most important stakeholders and directing any actions to preserve the relationships with them is crucial. When doing so, the interdependencies between the stakeholders must be identified and considered. Without knowledge of the sometimes less than obvious links, wellmeaning actions can cause adverse effects, so a cross-check for the impacts of potential options is recommended before making the final decision. The paper also gives recommendations on how to implement these steps at any organisation in order to enhance the quality of CDM and thus protect the organisation's reputation.

Normalization is a general neural mechanism for context-dependent decision making

PubMed Central

Louie, Kenway; Khaw, Mel W.; Glimcher, Paul W.

2013-01-01

Understanding the neural code is critical to linking brain and behavior. In sensory systems, divisive normalization seems to be a canonical neural computation, observed in areas ranging from retina to cortex and mediating processes including contrast adaptation, surround suppression, visual attention, and multisensory integration. Recent electrophysiological studies have extended these insights beyond the sensory domain, demonstrating an analogous algorithm for the value signals that guide decision making, but the effects of normalization on choice behavior are unknown. Here, we show that choice models using normalization generate significant (and classically irrational) choice phenomena driven by either the value or number of alternative options. In value-guided choice experiments, both monkey and human choosers show novel context-dependent behavior consistent with normalization. These findings suggest that the neural mechanism of value coding critically influences stochastic choice behavior and provide a generalizable quantitative framework for examining context effects in decision making. PMID:23530203

Control fast or control smart: When should invading pathogens be controlled?

PubMed

Thompson, Robin N; Gilligan, Christopher A; Cunniffe, Nik J

2018-02-01

The intuitive response to an invading pathogen is to start disease management as rapidly as possible, since this would be expected to minimise the future impacts of disease. However, since more spread data become available as an outbreak unfolds, processes underpinning pathogen transmission can almost always be characterised more precisely later in epidemics. This allows the future progression of any outbreak to be forecast more accurately, and so enables control interventions to be targeted more precisely. There is also the chance that the outbreak might die out without any intervention whatsoever, making prophylactic control unnecessary. Optimal decision-making involves continuously balancing these potential benefits of waiting against the possible costs of further spread. We introduce a generic, extensible data-driven algorithm based on parameter estimation and outbreak simulation for making decisions in real-time concerning when and how to control an invading pathogen. The Control Smart Algorithm (CSA) resolves the trade-off between the competing advantages of controlling as soon as possible and controlling later when more information has become available. We show-using a generic mathematical model representing the transmission of a pathogen of agricultural animals or plants through a population of farms or fields-how the CSA allows the timing and level of deployment of vaccination or chemical control to be optimised. In particular, the algorithm outperforms simpler strategies such as intervening when the outbreak size reaches a pre-specified threshold, or controlling when the outbreak has persisted for a threshold length of time. This remains the case even if the simpler methods are fully optimised in advance. Our work highlights the potential benefits of giving careful consideration to the question of when to start disease management during emerging outbreaks, and provides a concrete framework to allow policy-makers to make this decision.

Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat

PubMed Central

Erlich, Jeffrey C; Brunton, Bingni W; Duan, Chunyu A; Hanks, Timothy D; Brody, Carlos D

2015-01-01

Numerous brain regions have been shown to have neural correlates of gradually accumulating evidence for decision-making, but the causal roles of these regions in decisions driven by accumulation of evidence have yet to be determined. Here, in rats performing an auditory evidence accumulation task, we inactivated the frontal orienting fields (FOF) and posterior parietal cortex (PPC), two rat cortical regions that have neural correlates of accumulating evidence and that have been proposed as central to decision-making. We used a detailed model of the decision process to analyze the effect of inactivations. Inactivation of the FOF induced substantial performance impairments that were quantitatively best described as an impairment in the output pathway of an evidence accumulator with a long integration time constant (>240 ms). In contrast, we found a minimal role for PPC in decisions guided by accumulating auditory evidence, even while finding a strong role for PPC in internally-guided decisions. DOI: http://dx.doi.org/10.7554/eLife.05457.001 PMID:25869470

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

NASA Astrophysics Data System (ADS)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

Perceptual and categorical decision making: goal-relevant representation of two domains at different levels of abstraction.

PubMed

Shankar, Swetha; Kayser, Andrew S

2017-06-01

To date it has been unclear whether perceptual decision making and rule-based categorization reflect activation of similar cognitive processes and brain regions. On one hand, both map potentially ambiguous stimuli to a smaller set of motor responses. On the other hand, decisions about perceptual salience typically concern concrete sensory representations derived from a noisy stimulus, while categorization is typically conceptualized as an abstract decision about membership in a potentially arbitrary set. Previous work has primarily examined these types of decisions in isolation. Here we independently varied salience in both the perceptual and categorical domains in a random dot-motion framework by manipulating dot-motion coherence and motion direction relative to a category boundary, respectively. Behavioral and modeling results suggest that categorical (more abstract) information, which is more relevant to subjects' decisions, is weighted more strongly than perceptual (more concrete) information, although they also have significant interactive effects on choice. Within the brain, BOLD activity within frontal regions strongly differentiated categorical salience and weakly differentiated perceptual salience; however, the interaction between these two factors activated similar frontoparietal brain networks. Notably, explicitly evaluating feature interactions revealed a frontal-parietal dissociation: parietal activity varied strongly with both features, but frontal activity varied with the combined strength of the information that defined the motor response. Together, these data demonstrate that frontal regions are driven by decision-relevant features and argue that perceptual decisions and rule-based categorization reflect similar cognitive processes and activate similar brain networks to the extent that they define decision-relevant stimulus-response mappings. NEW & NOTEWORTHY Here we study the behavioral and neural dynamics of perceptual categorization when decision information varies in multiple domains at different levels of abstraction. Behavioral and modeling results suggest that categorical (more abstract) information is weighted more strongly than perceptual (more concrete) information but that perceptual and categorical domains interact to influence decisions. Frontoparietal brain activity during categorization flexibly represents decision-relevant features and highlights significant dissociations in frontal and parietal activity during decision making. Copyright © 2017 the American Physiological Society.

Perceptual and categorical decision making: goal-relevant representation of two domains at different levels of abstraction

PubMed Central

Kayser, Andrew S.

2017-01-01

To date it has been unclear whether perceptual decision making and rule-based categorization reflect activation of similar cognitive processes and brain regions. On one hand, both map potentially ambiguous stimuli to a smaller set of motor responses. On the other hand, decisions about perceptual salience typically concern concrete sensory representations derived from a noisy stimulus, while categorization is typically conceptualized as an abstract decision about membership in a potentially arbitrary set. Previous work has primarily examined these types of decisions in isolation. Here we independently varied salience in both the perceptual and categorical domains in a random dot-motion framework by manipulating dot-motion coherence and motion direction relative to a category boundary, respectively. Behavioral and modeling results suggest that categorical (more abstract) information, which is more relevant to subjects’ decisions, is weighted more strongly than perceptual (more concrete) information, although they also have significant interactive effects on choice. Within the brain, BOLD activity within frontal regions strongly differentiated categorical salience and weakly differentiated perceptual salience; however, the interaction between these two factors activated similar frontoparietal brain networks. Notably, explicitly evaluating feature interactions revealed a frontal-parietal dissociation: parietal activity varied strongly with both features, but frontal activity varied with the combined strength of the information that defined the motor response. Together, these data demonstrate that frontal regions are driven by decision-relevant features and argue that perceptual decisions and rule-based categorization reflect similar cognitive processes and activate similar brain networks to the extent that they define decision-relevant stimulus-response mappings. NEW & NOTEWORTHY Here we study the behavioral and neural dynamics of perceptual categorization when decision information varies in multiple domains at different levels of abstraction. Behavioral and modeling results suggest that categorical (more abstract) information is weighted more strongly than perceptual (more concrete) information but that perceptual and categorical domains interact to influence decisions. Frontoparietal brain activity during categorization flexibly represents decision-relevant features and highlights significant dissociations in frontal and parietal activity during decision making. PMID:28250149

Distributed Cognition in Cancer Treatment Decision Making: An Application of the DECIDE Decision-Making Styles Typology.

PubMed

Krieger, Janice L; Krok-Schoen, Jessica L; Dailey, Phokeng M; Palmer-Wackerly, Angela L; Schoenberg, Nancy; Paskett, Electra D; Dignan, Mark

2017-07-01

Distributed cognition occurs when cognitive and affective schemas are shared between two or more people during interpersonal discussion. Although extant research focuses on distributed cognition in decision making between health care providers and patients, studies show that caregivers are also highly influential in the treatment decisions of patients. However, there are little empirical data describing how and when families exert influence. The current article addresses this gap by examining decisional support in the context of cancer randomized clinical trial (RCT) decision making. Data are drawn from in-depth interviews with rural, Appalachian cancer patients ( N = 46). Analysis of transcript data yielded empirical support for four distinct models of health decision making. The implications of these findings for developing interventions to improve the quality of treatment decision making and overall well-being are discussed.

The Commercial Energy Consumer: About Whom Are We Speaking?

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

Payne, Christopher

2006-05-12

Who are commercial sector customers, and how do they make decisions about energy consumption and energy efficiency investment? The energy policy field has not done a thorough job of describing energy consumption in the commercial sector. First, the discussion of the commercial sector itself is dominated by discussion of large businesses/buildings. Second, discussion of this portion of the commercial sectors consumption behavior is driven primarily by theory, with very little field data collected on the way commercial sector decision-makers describe their own options, choices, and reasons for taking action. These limitations artificially constrain energy policy options. This paper reviews themore » extant literature on commercial sector energy consumption behavior and identifies gaps in our knowledge. In particular, it argues that the primary energy policy model of commercial sector energy consumption is a top-down model that uses macro-level investment data to make conclusions about commercial behavior. Missing from the discussion is a model of consumption behavior that builds up to a theoretical framework informed by the micro-level data provided by commercial decision-makers themselves. Such a bottom-up model could enhance the effectiveness of commercial sector energy policy. In particular, translation of some behavioral models from the residential sector to the commercial sector may offer new opportunities for policies to change commercial energy consumption behavior. Utility bill consumption feedback is considered as one example of a policy option that may be applicable to both the residential and small commercial sector.« less

Driving Ms. Data: Creating Data-Driven Possibilities

ERIC Educational Resources Information Center

Hoffman, Richard

2005-01-01

This article describes how driven Web sites help schools and districts maximize their IT resources by making online content more "self-service" for users. It shows how to set up the capacity to create data-driven sites. By definition, a data-driven Web site is one in which the content comes from some back-end data source, such as a…

How to pose the question matters: Behavioural Economics concepts in decision making on the basis of ensemble forecasts

NASA Astrophysics Data System (ADS)

Alfonso, Leonardo; van Andel, Schalk Jan

2014-05-01

Part of recent research in ensemble and probabilistic hydro-meteorological forecasting analyses which probabilistic information is required by decision makers and how it can be most effectively visualised. This work, in addition, analyses if decision making in flood early warning is also influenced by the way the decision question is posed. For this purpose, the decision-making game "Do probabilistic forecasts lead to better decisions?", which Ramos et al (2012) conducted at the EGU General Assembly 2012 in the city of Vienna, has been repeated with a small group and expanded. In that game decision makers had to decide whether or not to open a flood release gate, on the basis of flood forecasts, with and without uncertainty information. A conclusion of that game was that, in the absence of uncertainty information, decision makers are compelled towards a more risk-averse attitude. In order to explore to what extent the answers were driven by the way the questions were framed, in addition to the original experiment, a second variant was introduced where participants were asked to choose between a sure value (for either loosing or winning with a giving probability) and a gamble. This set-up is based on Kahneman and Tversky (1979). Results indicate that the way how the questions are posed may play an important role in decision making and that Prospect Theory provides promising concepts to further understand how this works.

ARL and Association 3.0: Ten Management Challenges

ERIC Educational Resources Information Center

Funk, Carla J.

2009-01-01

Association management in today's "association 3.0" environment presents some new challenges and new perspectives on old ones. This paper summarizes 10 such challenges including collaboration, diversity, innovation, transparency, financial stability, member benefits, knowledge-based decision-making, a demand-driven association model, pro-activity…

Creating Smarter Classrooms: Data-Based Decision Making for Effective Classroom Management

ERIC Educational Resources Information Center

Gage, Nicholas A.; McDaniel, Sara

2012-01-01

The term "data-based decision making" (DBDM) has become pervasive in education and typically refers to the use of data to make decisions in schools, from assessment of an individual student's academic progress to whole-school reform efforts. Research suggests that special education teachers who use progress monitoring data (a DBDM…

The potential of expert systems for remote sensing application

NASA Technical Reports Server (NTRS)

Mooneyhan, D. W.

1983-01-01

An overview of the status and potential of artificial intelligence-driven expert systems in the role of image data analysis is presented. An expert system is defined and its structure is summarized. Three such systems designed for image interpretation are outlined. The use of an expert system to detect changes on the earth's surface is discussed, and the components of a knowledge-based image interpretation system and their make-up are outlined. An example of how such a system should work for an area in the tropics where deforestation has occurred is presented as a sequence of situation/action decisions.

Reinforcement learning improves behaviour from evaluative feedback

NASA Astrophysics Data System (ADS)

Littman, Michael L.

2015-05-01

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Reinforcement learning improves behaviour from evaluative feedback.

PubMed

Littman, Michael L

2015-05-28

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Composite collective decision-making

PubMed Central

Czaczkes, Tomer J.; Czaczkes, Benjamin; Iglhaut, Carolin; Heinze, Jürgen

2015-01-01

Individual animals are adept at making decisions and have cognitive abilities, such as memory, which allow them to hone their decisions. Social animals can also share information. This allows social animals to make adaptive group-level decisions. Both individual and collective decision-making systems also have drawbacks and limitations, and while both are well studied, the interaction between them is still poorly understood. Here, we study how individual and collective decision-making interact during ant foraging. We first gathered empirical data on memory-based foraging persistence in the ant Lasius niger. We used these data to create an agent-based model where ants may use social information (trail pheromones), private information (memories) or both to make foraging decisions. The combined use of social and private information by individuals results in greater efficiency at the group level than when either information source was used alone. The modelled ants couple consensus decision-making, allowing them to quickly exploit high-quality food sources, and combined decision-making, allowing different individuals to specialize in exploiting different resource patches. Such a composite collective decision-making system reaps the benefits of both its constituent parts. Exploiting such insights into composite collective decision-making may lead to improved decision-making algorithms. PMID:26019155

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Open cyberGIS software for geospatial research and education in the big data era

NASA Astrophysics Data System (ADS)

Wang, Shaowen; Liu, Yan; Padmanabhan, Anand

CyberGIS represents an interdisciplinary field combining advanced cyberinfrastructure, geographic information science and systems (GIS), spatial analysis and modeling, and a number of geospatial domains to improve research productivity and enable scientific breakthroughs. It has emerged as new-generation GIS that enable unprecedented advances in data-driven knowledge discovery, visualization and visual analytics, and collaborative problem solving and decision-making. This paper describes three open software strategies-open access, source, and integration-to serve various research and education purposes of diverse geospatial communities. These strategies have been implemented in a leading-edge cyberGIS software environment through three corresponding software modalities: CyberGIS Gateway, Toolkit, and Middleware, and achieved broad and significant impacts.

Decision-making in nursing practice: An integrative literature review.

PubMed

Nibbelink, Christine W; Brewer, Barbara B

2018-03-01

To identify and summarise factors and processes related to registered nurses' patient care decision-making in medical-surgical environments. A secondary goal of this literature review was to determine whether medical-surgical decision-making literature included factors that appeared to be similar to concepts and factors in naturalistic decision making (NDM). Decision-making in acute care nursing requires an evaluation of many complex factors. While decision-making research in acute care nursing is prevalent, errors in decision-making continue to lead to poor patient outcomes. Naturalistic decision making may provide a framework for further exploring decision-making in acute care nursing practice. A better understanding of the literature is needed to guide future research to more effectively support acute care nurse decision-making. PubMed and CINAHL databases were searched, and research meeting criteria was included. Data were identified from all included articles, and themes were developed based on these data. Key findings in this review include nursing experience and associated factors; organisation and unit culture influences on decision-making; education; understanding patient status; situation awareness; and autonomy. Acute care nurses employ a variety of decision-making factors and processes and informally identify experienced nurses to be important resources for decision-making. Incorporation of evidence into acute care nursing practice continues to be a struggle for acute care nurses. This review indicates that naturalistic decision making may be applicable to decision-making nursing research. Experienced nurses bring a broad range of previous patient encounters to their practice influencing their intuitive, unconscious processes which facilitates decision-making. Using naturalistic decision making as a conceptual framework to guide research may help with understanding how to better support less experienced nurses' decision-making for enhanced patient outcomes. © 2017 John Wiley & Sons Ltd.

New Approaches to Capture High Frequency Agricultural Dynamics in Africa through Mobile Phones

NASA Astrophysics Data System (ADS)

Evans, T. P.; Attari, S.; Plale, B. A.; Caylor, K. K.; Estes, L. D.; Sheffield, J.

2015-12-01

Crop failure early warning systems relying on remote sensing constitute a new critical resource to assess areas where food shortages may arise, but there is a disconnect between the patterns of crop production on the ground and the environmental and decision-making dynamics that led to a particular crop production outcome. In Africa many governments use mid-growing season household surveys to get an on-the-ground assessment of current agricultural conditions. But these efforts are cost prohibitive over large scales and only offer a one-time snapshot at a particular time point. They also rely on farmers to recall past decisions and farmer recall may be imperfect when answering retrospectively on a decision made several months back (e.g. quantity of seed planted). We introduce a novel mobile-phone based approach to acquire information from farmers over large spatial extents, at high frequency at relatively low-cost compared to household survey approaches. This system makes compromises in number of questions which can feasibly be asked of a respondent (compared to household interviews), but the benefit of capturing weekly data from farmers is very exciting. We present data gathered from farmers in Kenya and Zambia to understand key dimensions of agricultural decision making such as choice of seed variety/planting date, frequency and timing of weeding/fertilizing and coping strategies such as pursuing off-farm labor. A particularly novel aspect of this work is reporting from farmers of what their expectation of end-season harvest will be on a week-by-week basis. Farmer's themselves can serve as sentinels of crop failure in this system. And farmers responses to drought are as much driven by their expectations of looming crop failure that may be different from that gleaned from remote sensing based assessment. This work is one piece of a larger design to link farmers to high-density meteorological data in Africa as an additional tool to improve crop failure early warning systems and understand adaptation to climate variability.

Moving data off the shelf and into action: an intervention to improve data-informed decision making in Côte d'Ivoire.

PubMed

Nutley, Tara; Gnassou, Léontine; Traore, Moussa; Bosso, Abitche Edwige; Mullen, Stephanie

2014-01-01

Improving a health system requires data, but too often they are unused or under-used by decision makers. Without interventions to improve the use of data in decision making, health systems cannot meet the needs of the populations they serve. In 2008, in Côte d'Ivoire, data were largely unused in health decision-making processes. To implement and evaluate an intervention to improve the use of data in decision making in Cote d'Ivoire. From 2008 to 2012, Cote d'Ivoire sought to improve the use of national health data through an intervention that broadens participation in and builds links between data collection and decision-making processes; identifies information needs; improves data quality; builds capacity to analyze, synthesize, and interpret data; and develops policies to support data use. To assess the results, a Performance of Routine Information System Management Assessment was conducted before and after the intervention using a combination of purposeful and random sampling. In 2008, the sample consisted of the central level, 12 districts, and 119 facilities, and in 2012, the sample consisted of the central level, 20 districts, and 190 health facilities. To assess data use, we developed dichotomous indicators: discussions of analysis findings, decisions taken based on the analysis, and decisions referred to upper management for action. We aggregated the indicators to generate a composite, continuous index of data use. From 2008 to 2012, the district data-use score increased from 40 to 70%; the facility score remained the same - 38%. The central score is not reported, because of a methodological difference in the two assessments. The intervention improved the use of data in decision making at the district level in Côte d'Ivoire. This study provides an example of, and guidance for, implementing a large-scale intervention to improve data-informed decision making.

The impact of geographic information systems on emergency management decision making at the U.S. Department of Homeland Security

NASA Astrophysics Data System (ADS)

King, Steven Gray

Geographic information systems (GIS) reveal relationships and patterns from large quantities of diverse data in the form of maps and reports. The United States spends billions of dollars to use GIS to improve decisions made during responses to natural disasters and terrorist attacks, but precisely how GIS improves or impairs decision making is not known. This research examined how GIS affect decision making during natural disasters, and how GIS can be more effectively used to improve decision making for emergency management. Using a qualitative case study methodology, this research examined decision making at the U.S. Department of Homeland Security (DHS) during a large full-scale disaster exercise. This study indicates that GIS provided decision makers at DHS with an outstanding context for information that would otherwise be challenging to understand, especially through the integration of multiple data sources and dynamic three-dimensional interactive maps. Decision making was hampered by outdated information, a reliance on predictive models based on hypothetical data rather than actual event data, and a lack of understanding of the capabilities of GIS beyond cartography. Geospatial analysts, emergency managers, and other decision makers who use GIS should take specific steps to improve decision making based on GIS for disaster response and emergency management.

Silencing the Critics: Understanding the Effects of Cocaine Sensitization on Dorsolateral and Ventral Striatum in the Context of an Actor/Critic Model

PubMed Central

Takahashi, Yuji; Schoenbaum, Geoffrey; Niv, Yael

2008-01-01

A critical problem in daily decision making is how to choose actions now in order to bring about rewards later. Indeed, many of our actions have long-term consequences, and it is important to not be myopic in balancing the pros and cons of different options, but rather to take into account both immediate and delayed consequences of actions. Failures to do so may be manifest as persistent, maladaptive decision-making, one example of which is addiction where behavior seems to be driven by the immediate positive experiences with drugs, despite the delayed adverse consequences. A recent study by Takahashi et al. (2007) investigated the effects of cocaine sensitization on decision making in rats and showed that drug use resulted in altered representations in the ventral striatum and the dorsolateral striatum, areas that have been implicated in the neural instantiation of a computational solution to optimal long-term actions selection called the Actor/Critic framework. In this Focus article we discuss their results and offer a computational interpretation in terms of drug-induced impairments in the Critic. We first survey the different lines of evidence linking the subparts of the striatum to the Actor/Critic framework, and then suggest two possible scenarios of breakdown that are suggested by Takahashi et al.'s (2007) data. As both are compatible with the current data, we discuss their different predictions and how these could be empirically tested in order to further elucidate (and hopefully inch towards curing) the neural basis of drug addiction. PMID:18982111

City-Level Energy Decision Making. Data Use in Energy Planning, Implementation, and Evaluation in U.S. Cities

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

Aznar, Alexandra; Day, Megan; Doris, Elizabeth

2015-07-08

The Cities-LEAP technical report, City-Level Energy Decision Making: Data Use in Energy Planning, Implementation, and Evaluation in U.S. Cities, explores how a sample of cities incorporates data into making energy-related decisions. This report provides the foundation for forthcoming components of the Cities-LEAP project that will help cities improve energy decision making by mapping specific city energy or climate policies and actions to measurable impacts and results.

A Data-Driven Approach to Interactive Visualization of Power Grids

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

Zhu, Jun

Driven by emerging industry standards, electric utilities and grid coordination organizations are eager to seek advanced tools to assist grid operators to perform mission-critical tasks and enable them to make quick and accurate decisions. The emerging field of visual analytics holds tremendous promise for improving the business practices in today’s electric power industry. The conducted investigation, however, has revealed that the existing commercial power grid visualization tools heavily rely on human designers, hindering user’s ability to discover. Additionally, for a large grid, it is very labor-intensive and costly to build and maintain the pre-designed visual displays. This project proposes amore » data-driven approach to overcome the common challenges. The proposed approach relies on developing powerful data manipulation algorithms to create visualizations based on the characteristics of empirically or mathematically derived data. The resulting visual presentations emphasize what the data is rather than how the data should be presented, thus fostering comprehension and discovery. Furthermore, the data-driven approach formulates visualizations on-the-fly. It does not require a visualization design stage, completely eliminating or significantly reducing the cost for building and maintaining visual displays. The research and development (R&D) conducted in this project is mainly divided into two phases. The first phase (Phase I & II) focuses on developing data driven techniques for visualization of power grid and its operation. Various data-driven visualization techniques were investigated, including pattern recognition for auto-generation of one-line diagrams, fuzzy model based rich data visualization for situational awareness, etc. The R&D conducted during the second phase (Phase IIB) focuses on enhancing the prototyped data driven visualization tool based on the gathered requirements and use cases. The goal is to evolve the prototyped tool developed during the first phase into a commercial grade product. We will use one of the identified application areas as an example to demonstrate how research results achieved in this project are successfully utilized to address an emerging industry need. In summary, the data-driven visualization approach developed in this project has proven to be promising for building the next-generation power grid visualization tools. Application of this approach has resulted in a state-of-the-art commercial tool currently being leveraged by more than 60 utility organizations in North America and Europe .« less

Couple decision making and use of cultural scripts in Malawi.

PubMed

Mbweza, Ellen; Norr, Kathleen F; McElmurry, Beverly

2008-01-01

To examine the decision-making processes of husband and wife dyads in matrilineal and patrilineal marriage traditions of Malawi in the areas of money, food, pregnancy, contraception, and sexual relations. Qualitative grounded theory using simultaneous interviews of 60 husbands and wives (30 couples). Data were analyzed according to the guidelines of simultaneous data collection and analysis. The analysis resulted in development of core categories and categories of decision-making process. Data matrixes were used to identify similarities and differences within couples and across cases. Most couples reported using a mix of final decision-making approaches: husband-dominated, wife-dominated, and shared. Gender based and nongender based cultural scripts provided rationales for their approaches to decision making. Gender based cultural scripts (husband-dominant and wife-dominant) were used to justify decision-making approaches. Non-gender based cultural scripts (communicating openly, maintaining harmony, and children's welfare) supported shared decision making. Gender based cultural scripts were used in decision making more often among couples from the district with a patrilineal marriage tradition and where the husband had less than secondary school education and was not formally employed. Nongender based cultural scripts to encourage shared decision making can be used in designing culturally tailored reproductive health interventions for couples. Nurses who work with women and families should be aware of the variations that occur in actual couple decision-making approaches. Shared decision making can be used to encourage the involvement of men in reproductive health programs.

Use of cost-effectiveness data in priority setting decisions: experiences from the national guidelines for heart diseases in Sweden

PubMed Central

Eckard, Nathalie; Janzon, Magnus; Levin, Lars-Åke

2014-01-01

Background: The inclusion of cost-effectiveness data, as a basis for priority setting rankings, is a distinguishing feature in the formulation of the Swedish national guidelines. Guidelines are generated with the direct intent to influence health policy and support decisions about the efficient allocation of scarce healthcare resources. Certain medical conditions may be given higher priority rankings i.e. given more resources than others, depending on how serious the medical condition is. This study investigated how a decision-making group, the Priority Setting Group (PSG), used cost-effectiveness data in ranking priority setting decisions in the national guidelines for heart diseases. Methods: A qualitative case study methodology was used to explore the use of such data in ranking priority setting healthcare decisions. The study addressed availability of cost-effectiveness data, evidence understanding, interpretation difficulties, and the reliance on evidence. We were also interested in the explicit use of data in ranking decisions, especially in situations where economic arguments impacted the reasoning behind the decisions. Results: This study showed that cost-effectiveness data was an important and integrated part of the decision-making process. Involvement of a health economist and reliance on the data facilitated the use of cost-effectiveness data. Economic arguments were used both as a fine-tuning instrument and a counterweight for dichotomization. Cost-effectiveness data were used when the overall evidence base was weak and the decision-makers had trouble making decisions due to lack of clinical evidence and in times of uncertainty. Cost-effectiveness data were also used for decisions on the introduction of new expensive medical technologies. Conclusion: Cost-effectiveness data matters in decision-making processes and the results of this study could be applicable to other jurisdictions where health economics is implemented in decision-making. This study contributes to knowledge on how cost-effectiveness data is used in actual decision-making, to ensure that the decisions are offered on equal terms and that patients receive medical care according their needs in order achieve maximum benefit. PMID:25396208

Role of ideas and ideologies in evidence-based health policy.

PubMed

Prinja, S

2010-01-01

Policy making in health is largely thought to be driven by three 'I's namely ideas, interests and institutions. Recent years have seen a shift in approach with increasing reliance being placed on role of evidence for policy making. The present article ascertains the role of ideas and ideologies in shaping evidence which is used to aid in policy decisions. The article discusses different theories of research-policy interface and the relative freedom of research-based evidence from the influence of ideas. Examples from developed and developed countries are cited to illustrate the contentions made. The article highlights the complexity of the process of evidence-based policy making, in a world driven by existing political, social and cultural ideologies. Consideration of this knowledge is paramount where more efforts are being made to bridge the gap between the 'two worlds' of researchers and policy makers to make evidence-based policy as also for policy analysts.

Effective Rating Scale Development for Speaking Tests: Performance Decision Trees

ERIC Educational Resources Information Center

Fulcher, Glenn; Davidson, Fred; Kemp, Jenny

2011-01-01

Rating scale design and development for testing speaking is generally conducted using one of two approaches: the measurement-driven approach or the performance data-driven approach. The measurement-driven approach prioritizes the ordering of descriptors onto a single scale. Meaning is derived from the scaling methodology and the agreement of…

Technology Infusion Challenges from a Decision Support Perspective

NASA Technical Reports Server (NTRS)

Adumitroaie, V.; Weisbin, C. R.

2009-01-01

In a restricted science budget environment and increasingly numerous required technology developments, the technology investment decisions within NASA are objectively more and more difficult to make such that the end results are satisfying the technical objectives and all the organizational constraints. Under these conditions it is rationally desirable to build an investment portfolio, which has the highest possible technology infusion rate. Arguably the path to infusion is subject to many influencing factors, but here only the challenges associated with the very initial stages are addressed: defining the needs and the subsequent investment decision-support process. It is conceivable that decision consistency and possibly its quality suffer when the decision-making process has limited or no traceability. This paper presents a structured decision-support framework aiming to provide traceable, auditable, infusion- driven recommendations towards a selection process in which these recommendations are used as reference points in further discussions among stakeholders. In this framework addressing well-defined requirements, different measures of success can be defined based on traceability to specific selection criteria. As a direct result, even by using simplified decision models the likelihood of infusion can be probed and consequently improved.

Microfinance participation and contraceptive decision-making: results from a national sample of women in Bangladesh.

PubMed

Murshid, N S; Ely, G E

2016-10-01

Our objective was to assess whether microfinance participation affords greater contraceptive decision-making power to women. Population based secondary data analysis. In this cross-sectional study using nationally representative data from the Bangladesh Demographic and Health Survey 2011 we conducted multinomial logistic regression to estimate the odds of contraceptive decision-making by respondents and their husbands based on microfinance participation. Microfinance participation was measured as a dichotomous variable and contraceptive decision-making was conceptualized based on who made decisions about contraceptive use: respondents only; their partners or husbands only; or both. The odds of decision-making by the respondent, with the reference case being joint decision-making, were higher for microfinance participants, but they were not significant. The odds of decision-making by the husband, with the reference case again being joint decision-making, were significantly lower among men who were partnered with women who participated in microfinance (RRR = 0.70, P < 0.01). Microfinance participation by women allowed men to share decision-making power with their wives that resulted in higher odds of joint decision-making. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

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

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response

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

Jin, Xin; Baker, Kyri A; Isley, Steven C

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

PubMed

Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

2017-09-29

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

Composite collective decision-making.

PubMed

Czaczkes, Tomer J; Czaczkes, Benjamin; Iglhaut, Carolin; Heinze, Jürgen

2015-06-22

Individual animals are adept at making decisions and have cognitive abilities, such as memory, which allow them to hone their decisions. Social animals can also share information. This allows social animals to make adaptive group-level decisions. Both individual and collective decision-making systems also have drawbacks and limitations, and while both are well studied, the interaction between them is still poorly understood. Here, we study how individual and collective decision-making interact during ant foraging. We first gathered empirical data on memory-based foraging persistence in the ant Lasius niger. We used these data to create an agent-based model where ants may use social information (trail pheromones), private information (memories) or both to make foraging decisions. The combined use of social and private information by individuals results in greater efficiency at the group level than when either information source was used alone. The modelled ants couple consensus decision-making, allowing them to quickly exploit high-quality food sources, and combined decision-making, allowing different individuals to specialize in exploiting different resource patches. Such a composite collective decision-making system reaps the benefits of both its constituent parts. Exploiting such insights into composite collective decision-making may lead to improved decision-making algorithms. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Integrating complex business processes for knowledge-driven clinical decision support systems.

PubMed

Kamaleswaran, Rishikesan; McGregor, Carolyn

2012-01-01

This paper presents in detail the component of the Complex Business Process for Stream Processing framework that is responsible for integrating complex business processes to enable knowledge-driven Clinical Decision Support System (CDSS) recommendations. CDSSs aid the clinician in supporting the care of patients by providing accurate data analysis and evidence-based recommendations. However, the incorporation of a dynamic knowledge-management system that supports the definition and enactment of complex business processes and real-time data streams has not been researched. In this paper we discuss the process web service as an innovative method of providing contextual information to a real-time data stream processing CDSS.

Towards data-driven decision-making for sustainable diets in Vietnam: Identifying priority indicators through stakeholder engagement

NASA Astrophysics Data System (ADS)

Mayton, H.; Beal, T.; Rubin, J.; Sanchez, A.; Heller, M.; Hoey, L.; Khoury, C. K.; Jones, A.

2017-12-01

Globally, food systems impact and are impacted by the sustainability of environmental, societal, political, and public health factors. At the center of these systems are human diets, which vary substantially by culture and region, and have significant influence on human health, community livelihoods, climate change, and natural resources. However, rapidly growing and highly diverse lower middle-income countries like Vietnam face challenges in gathering data and defining clear policy intervention points and approaches that will provide a net-positive systemic influence across sectors. A new collaboration, Entry points to Advance Transitions towards Sustainable diets (EATS), between the University of Michigan and the International Center for Tropical Agriculture (CIAT) aims to identify ways that existing data and insights into the policy process can be leveraged to inform decision-making on where and how to intervene to effectively shift multiple axes of food systems to enhance the sustainability of diets. As a first step towards developing a model that other policy communities could follow, researchers aggregated and characterized approximately 50 major existing datasets on food, agriculture, and nutrition in Vietnam. They also created a conceptual framework for evaluating the sustainability of diets and for characterizing existing datasets, including eight domains and over 200 unique, measurable indicators. Figure 1 summarizes these domains and their key relationships, which forms a foundation for identifying leverage points that can positively impact multiple aspects of sustainable diets. Researchers then engaged food system stakeholders through informal interviews, surveys, and collaborative workshops to prioritize indicators and identify additional relevant data sources. Stakeholders included national government, research, NGO, and private sector representatives from across the range of identified domains. The key indicators identified by stakeholders will ultimately be used to create food system data profiles for policymakers, in order to enable more evidence-based decision-making to advance transitions toward sustainable diets.

Ketamine use among regular tobacco and alcohol users as revealed by respondent driven sampling in Taipei: prevalence, expectancy, and users’ risky decision making

PubMed Central

Chen, Wei J.; Ting, Te-Tien; Chang, Chao-Ming; Liu, Ying-Chun; Chen, Chuan-Yu

2014-01-01

The popularity of ketamine for recreational use among young people began to increase, particularly in Asia, in 2000. To gain more knowledge about the use of ketamine among high risk individuals, a respondent-driven sampling (RDS) was implemented among regular alcohol and tobacco users in the Taipei metropolitan area from 2007 to 2010. The sampling was initiated in three different settings (i.e., two in the community and one in a clinic) to recruit seed individuals. Each participant was asked to refer one to five friends known to be regular tobacco smokers and alcohol drinkers to participate in the present study. Incentives were offered differentially upon the completion of an interview and successful referral. Information pertaining to drug use experience was collected by an audio computer-assisted self-interview instrument. Software built for RDS analyses was used for data analyses. Of the 1,115 subjects recruited, about 11.7% of the RDS respondents reported ever having used ketamine. Positive expectancy of ketamine use was positively associated with ketamine use; in contrast, negative expectancy inversely associated with ketamine use. Decision-making characteristics as measured on the Iowa Gambling Task using reinforcement learning models revealed that ketamine users learned less from the most recent event than both tobacco- and drug-naïve controls and regular tobacco and alcohol users. These findings about ketamine use among young people have implications for its prevention and intervention. PMID:25264412

Factors influencing antibiotic-prescribing decisions among inpatient physicians: a qualitative investigation

PubMed Central

Livorsi, D; Comer, AR; Matthias, MS; Perencevich, EN; Bair, MJ

2016-01-01

Objective To understand the professional and psychosocial factors that influence physicians' antibiotic-prescribing habits in the inpatient setting. Design We conducted semi-structured interviews with 30 inpatient physicians. Interviews consisted of open-ended questions and flexible probes based on participants' responses. Interviews were audio recorded, transcribed, de-identified, and reviewed for accuracy and completeness. Data were analyzed using emergent thematic analysis. Setting Two teaching hospitals in Indianapolis, IN Participants Thirty inpatient physicians (10 physicians-in-training, 20 supervising staff) Results Participants recognized that antibiotics are over-used, and many admitted to prescribing antibiotics even when the clinical evidence of infection was uncertain. Over-prescription was largely driven by anxiety about missing an infection while potential adverse effects of antibiotics did not strongly influence decision-making. Participants did not routinely disclose potential adverse effects of antibiotics to inpatients. Physicians-in-training were strongly influenced by the antibiotic prescribing behavior of their supervising staff physicians. Participants sometimes questioned their colleagues' antibiotic-prescribing decisions but frequently avoided providing direct feedback or critique, citing obstacles of hierarchy, infrequent face-to-face encounters, and the awkwardness of these conversations. Conclusion There is a physician-based culture of prescribing antibiotics, which involves over-using antibiotics and not challenging colleagues' decisions. The potential adverse effects of antibiotics do not strongly influence decision-making in this sample. A better understanding of these factors could be leveraged in future efforts to improve antibiotic-prescribing in the inpatient setting. PMID:26078017

District decision-making for health in low-income settings: a systematic literature review

PubMed Central

Avan, Bilal Iqbal

2016-01-01

Health management information systems (HMIS) produce large amounts of data about health service provision and population health, and provide opportunities for data-based decision-making in decentralized health systems. Yet the data are little-used locally. A well-defined approach to district-level decision-making using health data would help better meet the needs of the local population. In this second of four papers on district decision-making for health in low-income settings, our aim was to explore ways in which district administrators and health managers in low- and lower-middle-income countries use health data to make decisions, to describe the decision-making tools they used and identify challenges encountered when using these tools. A systematic literature review, following PRISMA guidelines, was undertaken. Experts were consulted about key sources of information. A search strategy was developed for 14 online databases of peer reviewed and grey literature. The resources were screened independently by two reviewers using pre-defined inclusion criteria. The 14 papers included were assessed for the quality of reported evidence and a descriptive evidence synthesis of the review findings was undertaken. We found 12 examples of tools to assist district-level decision-making, all of which included two key stages—identification of priorities, and development of an action plan to address them. Of those tools with more steps, four included steps to review or monitor the action plan agreed, suggesting the use of HMIS data. In eight papers HMIS data were used for prioritization. Challenges to decision-making processes fell into three main categories: the availability and quality of health and health facility data; human dynamics and financial constraints. Our findings suggest that evidence is available about a limited range of processes that include the use of data for decision-making at district level. Standardization and pre-testing in diverse settings would increase the potential that these tools could be used more widely. PMID:27591202

Normative evidence accumulation in unpredictable environments

PubMed Central

Glaze, Christopher M; Kable, Joseph W; Gold, Joshua I

2015-01-01

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals. DOI: http://dx.doi.org/10.7554/eLife.08825.001 PMID:26322383

Shared Decision-Making for Nursing Practice: An Integrative Review.

PubMed

Truglio-Londrigan, Marie; Slyer, Jason T

2018-01-01

Shared decision-making has received national and international interest by providers, educators, researchers, and policy makers. The literature on shared decision-making is extensive, dealing with the individual components of shared decision-making rather than a comprehensive process. This view of shared decision-making leaves healthcare providers to wonder how to integrate shared decision-making into practice. To understand shared decision-making as a comprehensive process from the perspective of the patient and provider in all healthcare settings. An integrative review was conducted applying a systematic approach involving a literature search, data evaluation, and data analysis. The search included articles from PubMed, CINAHL, the Cochrane Central Register of Controlled Trials, and PsycINFO from 1970 through 2016. Articles included quantitative experimental and non-experimental designs, qualitative, and theoretical articles about shared decision-making between all healthcare providers and patients in all healthcare settings. Fifty-two papers were included in this integrative review. Three categories emerged from the synthesis: (a) communication/ relationship building; (b) working towards a shared decision; and (c) action for shared decision-making. Each major theme contained sub-themes represented in the proposed visual representation for shared decision-making. A comprehensive understanding of shared decision-making between the nurse and the patient was identified. A visual representation offers a guide that depicts shared decision-making as a process taking place during a healthcare encounter with implications for the continuation of shared decisions over time offering patients an opportunity to return to the nurse for reconsiderations of past shared decisions.

Economic inequality increases risk taking

PubMed Central

Payne, B. Keith; Brown-Iannuzzi, Jazmin L.; Hannay, Jason W.

2017-01-01

Rising income inequality is a global trend. Increased income inequality has been associated with higher rates of crime, greater consumer debt, and poorer health outcomes. The mechanisms linking inequality to poor outcomes among individuals are poorly understood. This research tested a behavioral account linking inequality to individual decision making. In three experiments (n = 811), we found that higher inequality in the outcomes of an economic game led participants to take greater risks to try to achieve higher outcomes. This effect of unequal distributions on risk taking was driven by upward social comparisons. Next, we estimated economic risk taking in daily life using large-scale data from internet searches. Risk taking was higher in states with greater income inequality, an effect driven by inequality at the upper end of the income distribution. Results suggest that inequality may promote poor outcomes, in part, by increasing risky behavior. PMID:28416655

Advancing research collaborations among agencies through the Interagency Arctic Research Policy Committee: A necessary step for linking science to policy.

NASA Astrophysics Data System (ADS)

LaValley, M.; Starkweather, S.; Bowden, S.

2017-12-01

The Arctic is changing rapidly as average temperatures rise. As an Arctic nation, the United States is directly affected by these changes. It is imperative that these changes be understood to make effective policy decisions. Since the research needs of the Arctic are large and wide-ranging, most Federal agencies fund some aspect of Arctic research. As a result, the U.S. government regularly works to coordinate Federal Arctic research in order to reduce duplication of effort and costs, and to enhance the research's system perspective. The government's Interagency Arctic Research Policy Committee (IARPC) accomplishes this coordination through its policy-driven five-year Arctic Research Plans and collaboration teams (CTs), which are research topic-oriented teams tasked with implementing the plans. The policies put forth by IARPC thus inform science, however IARPC has been less successful of making these science outcomes part of an iterative decision making process. IARPC's mandate to facilitate coordinated research through information sharing communities can be viewed a prerequisite step in the science-to- decision making process. Research collaborations and the communities of practice facilitated by IARPC allow scientists to connect with a wider community of scientists and stakeholders and, in turn, the larger issues in need of policy solutions. These connections help to create a pathway through which research may increasingly reflect policy goals and inform decisions. IARPC has been growing into a more useful model for the science-to-decision making interface since the publication of its Arctic Research Plan FY2017-2021, and it is useful to evaluate how and why IARPC is progressing in this realm. To understand the challenges facing interagency research collaboration and the progress IARPC has made, the Chukchi Beaufort and Communities CTs, were evaluated as case studies. From the case studies, several recommendations for enhancing collaborations across Federal agencies emerge, including establishing appropriate agency leadership; determining focused and achievable scope of team goals; providing room for bottom-up, community-driven determination of goals; and finally, building relationships and creating an inclusive team environment.

Local public health resource allocation: limited choices and strategic decisions.

PubMed

Bekemeier, Betty; Chen, Anthony L-T; Kawakyu, Nami; Yang, Youngran

2013-12-01

Local health department leaders are expected to improve the health of their populations as they "use and contribute to" the evidence base for practice, but effectively providing and utilizing data and evidence for local public health decision making has proven difficult. This study was conducted in 2011 and initiated by Washington State's public health practice-based research network to identify factors influencing local resource allocation and programmatic decisions among public health leaders facing severe funding losses. Quantitative data informed sampling for the collection of interview data. Qualitative methods were used to capture diverse insights of Washington State's local public health leaders in making decisions regarding resource allocation. Local decision-making authority was perceived as greatly restricted by what public health activities were legally mandated and the categoric nature of funding sources, even as some leaders exercised deliberate strategic approaches. One's workforce and board of health were also influential in making decisions regarding resource allocations. Challenges were expressed regarding making use of data and research evidence for decision making. Data were analyzed in 2011-2012. Programmatic mandates, funding restrictions, local stakeholders, and workforce capacity appear to trump factors such as research evidence and perceived community need in public health resource allocation. Study findings highlight tensions between the literature descriptions of what "should" influence decision making in local public health and the realities of practice. Advancements in practice-based research and evidence-based decision making, however, provide opportunities for strengthening the development of evidence and research translation for local decision making to maximize resources and promote effective service provision. © 2013 American Journal of Preventive Medicine Published by American Journal of Preventive Medicine All rights reserved.

Development of a personalized decision aid for breast cancer risk reduction and management.

PubMed

Ozanne, Elissa M; Howe, Rebecca; Omer, Zehra; Esserman, Laura J

2014-01-14

Breast cancer risk reduction has the potential to decrease the incidence of the disease, yet remains underused. We report on the development a web-based tool that provides automated risk assessment and personalized decision support designed for collaborative use between patients and clinicians. Under Institutional Review Board approval, we evaluated the decision tool through a patient focus group, usability testing, and provider interviews (including breast specialists, primary care physicians, genetic counselors). This included demonstrations and data collection at two scientific conferences (2009 International Shared Decision Making Conference, 2009 San Antonio Breast Cancer Symposium). Overall, the evaluations were favorable. The patient focus group evaluations and usability testing (N = 34) provided qualitative feedback about format and design; 88% of these participants found the tool useful and 94% found it easy to use. 91% of the providers (N = 23) indicated that they would use the tool in their clinical setting. BreastHealthDecisions.org represents a new approach to breast cancer prevention care and a framework for high quality preventive healthcare. The ability to integrate risk assessment and decision support in real time will allow for informed, value-driven, and patient-centered breast cancer prevention decisions. The tool is being further evaluated in the clinical setting.

Chinese International Students' Decision-Making Perspectives: A Case Study

ERIC Educational Resources Information Center

Stewart, David

2017-01-01

Unprecedented rapidity of change occurring throughout the higher education sector linked to student mobility driven globalization momentum reinforces the benefits of attracting and cultivating the strongest students to contribute diversity of thought to learning environments. The purpose of this case study was to explore multiple perspectives of…

My Mentored Relationship with Harold Guetzkow

ERIC Educational Resources Information Center

Chadwick, Richard W.

2011-01-01

Harold Guetzkow's guidance of research on foreign policy decision making was driven by a core concern: the avoidance of nuclear war and preservation of peace. He aimed to do this by supporting the creation and distribution of new knowledge through experiments aimed at simulating the processes and conditions hypothesized to influence such…

Attention and Motivated Response to Simulated Male Advertisement Call Activates Forebrain Dopaminergic and Social Decision-Making Network Nuclei in Female Midshipman Fish.

PubMed

Forlano, Paul M; Licorish, Roshney R; Ghahramani, Zachary N; Timothy, Miky; Ferrari, Melissa; Palmer, William C; Sisneros, Joseph A

2017-10-01

Little is known regarding the coordination of audition with decision-making and subsequent motor responses that initiate social behavior including mate localization during courtship. Using the midshipman fish model, we tested the hypothesis that the time spent by females attending and responding to the advertisement call is correlated with the activation of a specific subset of catecholaminergic (CA) and social decision-making network (SDM) nuclei underlying auditory- driven sexual motivation. In addition, we quantified the relationship of neural activation between CA and SDM nuclei in all responders with the goal of providing a map of functional connectivity of the circuitry underlying a motivated state responsive to acoustic cues during mate localization. In order to make a baseline qualitative comparison of this functional brain map to unmotivated females, we made a similar correlative comparison of brain activation in females who were unresponsive to the advertisement call playback. Our results support an important role for dopaminergic neurons in the periventricular posterior tuberculum and ventral thalamus, putative A11 and A13 tetrapod homologues, respectively, as well as the posterior parvocellular preoptic area and dorsomedial telencephalon, (laterobasal amygdala homologue) in auditory attention and appetitive sexual behavior in fishes. These findings may also offer insights into the function of these highly conserved nuclei in the context of auditory-driven reproductive social behavior across vertebrates. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Logics of pre-merger decision-making processes: the case of Karolinska University Hospital.

PubMed

Choi, Soki; Brommels, Mats

2009-01-01

The purpose of this paper is to examine how and why a decision to merge two university hospitals in a public context might occur by using an in-depth case study of the pre-merger process of Karolinska University Hospital. Based on extensive document analysis and 35 key informant interviews the paper reconstructed the pre-merger process, searched for empirical patterns, and interpreted those by applying neo-institutional theory. Spanning nearly a decade, the pre-merger process goes from idea generation through transition to decision, and took place on two arenas, political, and scientific. Both research excellence and economic efficiency are stated merger motives. By applying a neo-institutional perspective, the paper finds that the two initial phases are driven by decision rationality, which is typical for political organizations and that the final phase demonstrated action rationality, which is typical for private firms. Critical factors behind this radical change of decision logic are means convergence, uniting key stakeholder groups, and an economic and political crisis, triggering critical incidents, which ultimately legitimized the formal decision. It is evident from the paper that merger decisions in the public sector might not necessarily result from stated and/or economic drivers only. This paper suggests that a change of decision logic from decision to action rationality might promote effective decision making on large and complex issues in a public context. This is the first systematic in-depth study of a university hospital merger employing a decision-making perspective.

Neuroeconomic measures of social decision-making across the lifespan.

PubMed

Zhu, Lusha; Walsh, Daniel; Hsu, Ming

2012-01-01

Social and decision-making deficits are often the first symptoms of a striking number of neurodegenerative disorders associated with aging. These includes not only disorders that directly impact dopamine and basal ganglia, such as Parkinson's disorder, but also degeneration in which multiple neural pathways are affected over the course of normal aging. The impact of such deficits can be dramatic, as in cases of financial fraud, which disproportionately affect the elderly. Unlike memory and motor impairments, however, which are readily recognized as symptoms of more serious underlying neurological conditions, social and decision-making deficits often do not elicit comparable concern in the elderly. Furthermore, few behavioral measures exist to quantify these deficits, due in part to our limited knowledge of the core cognitive components or their neurobiological substrates. Here we probe age-related differences in decision-making using a game theory paradigm previously shown to dissociate contributions of basal ganglia and prefrontal regions to behavior. Combined with computational modeling, we provide evidence that age-related changes in elderly participants are driven primarily by an over-reliance in trial-and-error reinforcement learning that does not take into account the strategic context, which may underlie cognitive deficits that contribute to social vulnerability in elderly individuals.

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.

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription.

PubMed

Shen, Ying; Yuan, Kaiqi; Chen, Daoyuan; Colloc, Joël; Yang, Min; Li, Yaliang; Lei, Kai

2018-03-01

The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. In such cases, a reliable antibiotic prescription support system is needed. This study describes the construction and optimization of the sensitivity and specificity of a decision support system named IDDAP, which is based on ontologies for infectious disease diagnosis and antibiotic therapy. The ontology for this system was constructed by collecting existing ontologies associated with infectious diseases, syndromes, bacteria and drugs into the ontology's hierarchical conceptual schema. First, IDDAP identifies a potential infectious disease based on a patient's self-described disease state. Then, the system searches for and proposes an appropriate antibiotic therapy specifically adapted to the patient based on factors such as the patient's body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, contraindications, drug-drug interactions between the proposed therapy and previously prescribed medication, and the route of therapy administration. The constructed domain ontology contains 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of "SubClassOf" that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components. The system includes 507 infectious diseases and their therapy methods in combination with 332 different infection sites, 936 relevant symptoms of the digestive, reproductive, neurological and other systems, 371 types of complications, 838,407 types of bacteria, 341 types of antibiotics, 1504 pairs of reaction rates (antibacterial spectrum) between antibiotics and bacteria, 431 pairs of drug interaction relationships and 86 pairs of antibiotic-specific population contraindicated relationships. Compared with the existing infectious disease-relevant ontologies in the field of knowledge comprehension, this ontology is more complete. Analysis of IDDAP's performance in terms of classifiers based on receiver operating characteristic (ROC) curve results (89.91%) revealed IDDAP's advantages when combined with our ontology. This study attempted to bridge the patient/caregiver gap by building a sophisticated application that uses artificial intelligence and machine learning computational techniques to perform data-driven decision-making at the point of primary care. The first level of decision-making is conducted by the IDDAP and provides the patient with a first-line therapy. Patients can then make a subjective judgment, and if any questions arise, should consult a physician for subsequent decisions, particularly in complicated cases or in cases in which the necessary information is not yet available in the knowledge base. Copyright © 2018 Elsevier B.V. All rights reserved.

School Characteristics Influencing the Implementation of a Data-Based Decision Making Intervention

ERIC Educational Resources Information Center

van Geel, Marieke; Visscher, Adrie J.; Teunis, Bernard

2017-01-01

There is an increasing global emphasis on using data for decision making, with a growing body of research on interventions aimed at implementing and sustaining data-based decision making (DBDM) in schools. Yet, little is known about the school features that facilitate or hinder the implementation of DBDM. Based on a literature review, the authors…

Data Decision-Making and Program-Wide Implementation of the Pyramid Model. Roadmap to Effective Intervention Practices #7

ERIC Educational Resources Information Center

Fox, Lise; Veguilla, Myrna; Perez Binder, Denise

2014-01-01

The Technical Assistance Center on Social Emotional Intervention for Young Children (TACSEI) Roadmap on "Data Decision-Making and Program-Wide Implementation of the Pyramid Model" provides programs with guidance on how to collect and use data to ensure the implementation of the Pyramid Model with fidelity and decision-making that…

Breaking the mould without breaking the system: the development and pilot of a clinical dashboard at The Prince Charles Hospital.

PubMed

Clark, Kevin W; Whiting, Elizabeth; Rowland, Jeffrey; Thompson, Leah E; Missenden, Ian; Schellein, Gerhard

2013-06-01

There is a vast array of clinical and quality data available within healthcare organisations. The availability of this data in a timely and easy to visualise way is an essential component of high-performing healthcare teams. It is recognised that good quality information is a driver of performance for clinical teams and helps ensure best possible care for patients. In 2012 the Internal Medicine Program at The Prince Charles Hospital developed a clinical dashboard that displays locally relevant information alongside relevant hospital and statewide metrics that inform daily clinical decision making. The data reported on the clinical dashboard is driven from data sourced from the electronic patient journey board in real time as well as other Queensland Health data sources. This provides clinicians with easy access to a wealth of local unit data presented in a simple graphical format that is being captured locally and arranged on a single screen so the information can be monitored at a glance. Local unit data informs daily decisions that identify and confirm patient flow problems, assist to identify root causes and enable evaluation of patient flow solutions.

Using basic geographic information systems functionality to support sustainable forest management decision making and post-decision assessments

Treesearch

Ronald E. McRoberts; R. James Barbour; Krista M. Gebert; Greg C. Liknes; Mark D. Nelson; Dacia M. Meneguzzo; et al.

2006-01-01

Sustainable management of natural resources requires informed decision making and post-decision assessments of the results of those decisions. Increasingly, both activities rely on analyses of spatial data in the forms of maps and digital data layers. Fortunately, a variety of supporting maps and data layers rapidly are becoming available. Unfortunately, however, user-...

Neural predictors of purchases

PubMed Central

Knutson, Brian; Rick, Scott; Wimmer, G. Elliott; Prelec, Drazen; Loewenstein, George

2007-01-01

Microeconomic theory maintains that purchases are driven by a combination of consumer preference and price. Using event-related FMRI, we investigated how people weigh these factors to make purchasing decisions. Consistent with neuroimaging evidence suggesting that distinct circuits anticipate gain and loss, product preference activated the nucleus accumbens (NAcc), while excessive prices activated the insula and deactivated the mesial prefrontal cortex (MPFC) prior to the purchase decision. Activity from each of these regions independently predicted immediately subsequent purchases above and beyond self-report variables. These findings suggest that activation of distinct neural circuits related to anticipatory affect precedes and supports consumers’ purchasing decisions. PMID:17196537

Effective behavioral modeling and prediction even when few exemplars are available

NASA Astrophysics Data System (ADS)

Goan, Terrance; Kartha, Neelakantan; Kaneshiro, Ryan

2006-05-01

While great progress has been made in the lowest levels of data fusion, practical advances in behavior modeling and prediction remain elusive. The most critical limitation of existing approaches is their inability to support the required knowledge modeling and continuing refinement under realistic constraints (e.g., few historic exemplars, the lack of knowledge engineering support, and the need for rapid system deployment). This paper reports on our ongoing efforts to develop Propheteer, a system which will address these shortcomings through two primary techniques. First, with Propheteer we abandon the typical consensus-driven modeling approaches that involve infrequent group decision making sessions in favor of an approach that solicits asynchronous knowledge contributions (in the form of alternative future scenarios and indicators) without burdening the user with endless certainty or probability estimates. Second, we enable knowledge contributions by personnel beyond the typical core decision making group, thereby casting light on blind spots, mitigating human biases, and helping maintain the currency of the developed behavior models. We conclude with a discussion of the many lessons learned in the development of our prototype Propheteer system.

Perturbations in Effort-Related Decision-Making Driven by Acute Stress and Corticotropin-Releasing Factor.

PubMed

Bryce, Courtney A; Floresco, Stan B

2016-07-01

Acute stress activates numerous systems in a coordinated effort to promote homeostasis, and can exert differential effects on mnemonic and cognitive functions depending on a myriad of factors. Stress can alter different forms of cost/benefit decision-making, yet the mechanisms that drive these effects, remain unclear. In the present study, we probed how corticotropin-releasing factor (CRF) may contribute to stress-induced alterations in cost/benefit decision-making, using an task where well-trained rats chose between a low effort/low reward lever (LR; two pellets) and a high effort/high reward lever (HR; four pellets), with the effort requirement increasing over a session (2, 5, 10, and 20 presses). One-hour restraint stress markedly reduced preference for the HR option, but this effect was attenuated by infusions of the CRF antagonist, alpha-helical CRF. Conversely, central CRF infusion mimicked the effect of stress on decision-making, as well as increased decision latencies and reduced response vigor. CRF infusions did not alter preference for larger vs smaller rewards, but did reduce responding for food delivered on a progressive ratio, suggesting that these treatments may amplify perceived effort costs that may be required to obtain rewards. CRF infusions into the ventral tegmental area recapitulated the effect of central CRF treatment and restraint on choice behavior, suggesting that these effects may be mediated by perturbations in dopamine transmission. These findings highlight the involvement of CRF in regulating effort-related decisions and suggest that increased CRF activity may contribute to motivational impairments and abnormal decision-making associated with stress-related psychiatric disorders such as depression.

Perturbations in Effort-Related Decision-Making Driven by Acute Stress and Corticotropin-Releasing Factor

PubMed Central

Bryce, Courtney A; Floresco, Stan B

2016-01-01

Acute stress activates numerous systems in a coordinated effort to promote homeostasis, and can exert differential effects on mnemonic and cognitive functions depending on a myriad of factors. Stress can alter different forms of cost/benefit decision-making, yet the mechanisms that drive these effects, remain unclear. In the present study, we probed how corticotropin-releasing factor (CRF) may contribute to stress-induced alterations in cost/benefit decision-making, using an task where well-trained rats chose between a low effort/low reward lever (LR; two pellets) and a high effort/high reward lever (HR; four pellets), with the effort requirement increasing over a session (2, 5, 10, and 20 presses). One-hour restraint stress markedly reduced preference for the HR option, but this effect was attenuated by infusions of the CRF antagonist, alpha-helical CRF. Conversely, central CRF infusion mimicked the effect of stress on decision-making, as well as increased decision latencies and reduced response vigor. CRF infusions did not alter preference for larger vs smaller rewards, but did reduce responding for food delivered on a progressive ratio, suggesting that these treatments may amplify perceived effort costs that may be required to obtain rewards. CRF infusions into the ventral tegmental area recapitulated the effect of central CRF treatment and restraint on choice behavior, suggesting that these effects may be mediated by perturbations in dopamine transmission. These findings highlight the involvement of CRF in regulating effort-related decisions and suggest that increased CRF activity may contribute to motivational impairments and abnormal decision-making associated with stress-related psychiatric disorders such as depression. PMID:26830960

A Web-Based Treatment Decision Support Tool for Patients With Advanced Knee Arthritis: Evaluation of User Interface and Content Design

PubMed Central

Zheng, Hua; Rosal, Milagros C; Li, Wenjun; Borg, Amy; Yang, Wenyun; Ayers, David C

2018-01-01

Background Data-driven surgical decisions will ensure proper use and timing of surgical care. We developed a Web-based patient-centered treatment decision and assessment tool to guide treatment decisions among patients with advanced knee osteoarthritis who are considering total knee replacement surgery. Objective The aim of this study was to examine user experience and acceptance of the Web-based treatment decision support tool among older adults. Methods User-centered formative and summative evaluations were conducted for the tool. A sample of 28 patients who were considering total knee replacement participated in the study. Participants’ responses to the user interface design, the clarity of information, as well as usefulness, satisfaction, and acceptance of the tool were collected through qualitative (ie, individual patient interviews) and quantitative (ie, standardized Computer System Usability Questionnaire) methods. Results Participants were older adults with a mean age of 63 (SD 11) years. Three-quarters of them had no technical questions using the tool. User interface design recommendations included larger fonts, bigger buttons, less colors, simpler navigation without extra “next page” click, less mouse movement, and clearer illustrations with simple graphs. Color-coded bar charts and outcome-specific graphs with positive action were easiest for them to understand the outcomes data. Questionnaire data revealed high satisfaction with the tool usefulness and interface quality, and also showed ease of use of the tool, regardless of age or educational status. Conclusions We evaluated the usability of a patient-centered decision support tool designed for advanced knee arthritis patients to facilitate their knee osteoarthritis treatment decision making. The lessons learned can inform other decision support tools to improve interface and content design for older patients’ use. PMID:29712620

An integrated theory of attention and decision making in visual signal detection.

PubMed

Smith, Philip L; Ratcliff, Roger

2009-04-01

The simplest attentional task, detecting a cued stimulus in an otherwise empty visual field, produces complex patterns of performance. Attentional cues interact with backward masks and with spatial uncertainty, and there is a dissociation in the effects of these variables on accuracy and on response time. A computational theory of performance in this task is described. The theory links visual encoding, masking, spatial attention, visual short-term memory (VSTM), and perceptual decision making in an integrated dynamic framework. The theory assumes that decisions are made by a diffusion process driven by a neurally plausible, shunting VSTM. The VSTM trace encodes the transient outputs of early visual filters in a durable form that is preserved for the time needed to make a decision. Attention increases the efficiency of VSTM encoding, either by increasing the rate of trace formation or by reducing the delay before trace formation begins. The theory provides a detailed, quantitative account of attentional effects in spatial cuing tasks at the level of response accuracy and the response time distributions. (c) 2009 APA, all rights reserved

Effects of emotional preferences on value-based decision making are mediated by mentalizing not reward networks

PubMed Central

Evans, Simon; Fleming, Stephen M.; Dolan, Raymond J.; Averbeck, Bruno B.

2012-01-01

Real-world decision-making often involves social considerations. Consequently, the social value of stimuli can induce preferences in choice behavior. However, it is unknown how financial and social values are integrated in the brain. Here, we investigated how smiling and angry face stimuli interacted with financial reward feedback in a stochastically-rewarded decision-making task. Subjects reliably preferred the smiling faces despite equivalent reward feedback, demonstrating a socially driven bias. We fit a Bayesian reinforcement learning model to factor the effects of financial rewards and emotion preferences in individual subjects, and regressed model predictions on the trial-by-trial fMRI signal. Activity in the sub-callosal cingulate and the ventral striatum, both involved in reward learning, correlated with financial reward feedback, whereas the differential contribution of social value activated dorsal temporo-parietal junction and dorsal anterior cingulate cortex, previously proposed as components of a mentalizing network. We conclude that the impact of social stimuli on value-based decision processes is mediated by effects in brain regions partially separable from classical reward circuitry. PMID:20946058

Fathers and Daughters: Using a Multivariate Analysis through Multiple Regression to Help Predict a Daughter's Self-Esteem as Experienced by College-Age Women in Rural Nebraska

ERIC Educational Resources Information Center

Beshaler, Mary E.

2010-01-01

Throughout her life, a woman makes decisions about behaviors, relationships, academic accomplishments, and achievements. What propels women to make these choices may be driven by an image of self. This feeling of self-worth or self-esteem is developed early in life with the help of her primary caregivers as found in her biological mother and…

A model-driven privacy compliance decision support for medical data sharing in Europe.

PubMed

Boussi Rahmouni, H; Solomonides, T; Casassa Mont, M; Shiu, S; Rahmouni, M

2011-01-01

Clinical practitioners and medical researchers often have to share health data with other colleagues across Europe. Privacy compliance in this context is very important but challenging. Automated privacy guidelines are a practical way of increasing users' awareness of privacy obligations and help eliminating unintentional breaches of privacy. In this paper we present an ontology-plus-rules based approach to privacy decision support for the sharing of patient data across European platforms. We use ontologies to model the required domain and context information about data sharing and privacy requirements. In addition, we use a set of Semantic Web Rule Language rules to reason about legal privacy requirements that are applicable to a specific context of data disclosure. We make the complete set invocable through the use of a semantic web application acting as an interactive privacy guideline system can then invoke the full model in order to provide decision support. When asked, the system will generate privacy reports applicable to a specific case of data disclosure described by the user. Also reports showing guidelines per Member State may be obtained. The advantage of this approach lies in the expressiveness and extensibility of the modelling and inference languages adopted and the ability they confer to reason with complex requirements interpreted from high level regulations. However, the system cannot at this stage fully simulate the role of an ethics committee or review board.

A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables.

PubMed

Clark, Renee M; Besterfield-Sacre, Mary E

2009-03-01

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity, and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.

Iowa pavement asset management decision-making framework.

DOT National Transportation Integrated Search

2015-10-01

Most local agencies in Iowa currently make their pavement treatment decisions based on their limited experience due primarily to : lack of a systematic decision-making framework and a decision-aid tool. The lack of objective condition assessment data...

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model.

PubMed

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO 2 leaks and associated concentrations from geological CO 2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO 2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO 2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO 2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems. Copyright © 2017 Elsevier B.V. All rights reserved.

It's Not Just About More Data: Translation of Science and Decision Support Evaluation for Climate Impact Indicators

NASA Astrophysics Data System (ADS)

Kenney, M. A.

2014-12-01

The U.S. Global Change Research Program is currently considering establishing a National Climate Indicators System, which would be a set of physical, ecological, and societal indicators that would communicate key aspects of climate changes, impacts, vulnerabilities, and preparedness to inform mitigation and adaptation decisions. Thus, over the past several years 150+ scientists and practitioners representing a range of expertise from the climate system to natural systems to human sectors have developed a set of indicator recommendations that could be used as a first step to establishing such an indicator system. These recommendations have been implemented into a pilot system, with the goal of working with stakeholder communities to evaluate the understandability of individual indicators and learn how users are combining indicators for their own understanding or decision needs through this multiple Federal agency decision support platform. This prototype system provides the perfect test bed for evaluating the translation of scientific data - observations, remote sensing, and citizen science data -- and data products, such as indicators, for decision-making audiences. Often translation of scientific information into decision support products is developed and improved given intuition and feedback. Though this can be useful in many cases, more rigorous testing using social science methodologies would provide greater assurance that the data products are useful for the intended audiences. I will present some initial research using surveys to assess the understandability of indicators and whether that understanding is influenced by one's attitude toward climate change. Such information is critical to assess whether products developed for scientists by scientists have been appropriately translated for non-scientists, thus assuring that the data will have some value for the intended audience. Such survey information will provide a data driven approach to further develop and improve the National Climate Indicators System and could be applied to improve other decision support systems.

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

NASA Technical Reports Server (NTRS)

Johnson, Lee F.; Maneta, Marco P.; Kimball, John S.

2016-01-01

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

A satellite data-driven, client-server decision support application for agricultural water resources management

NASA Astrophysics Data System (ADS)

Maneta, M. P.; Johnson, L.; Kimball, J. S.

2016-12-01

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in atypical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight `app` that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Intuition: A Needed Component of Leadership for Decision-making in Today’s Technology Driven Air Force

DTIC Science & Technology

2009-12-15

technology‘s influence. She states, ―We treat technology as a family member…‖. Email replaced the Post Office with instant communication worldwide. We can...how or why the conclusion was reached in a rational sense. As Rowan states, ―Not being able to articulate a hazy, indistinct, subliminal impression...decisions and act independently, Kennan was not able to communicate his message and idea to senior leadership for years after his intuition led him

The Adaptability of Career Decision-Making Profiles: Associations with Self-Efficacy, Emotional Difficulties, and Decision Status

ERIC Educational Resources Information Center

Gadassi, Reuma; Gati, Itamar; Wagman-Rolnick, Halleli

2013-01-01

The present study investigated a new model for characterizing the way individuals make career decisions (career decision-making profiles [CDMP]). Using data from 285 students in a preacademic program, the present study assessed the association of the CDMP's dimensions with the Emotional and Personality-related Career decision-making Difficulties…

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

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

Teeguarden, Justin G.; Tan, Yu-Mei; Edwards, Stephen W.

Driven by major scientific advances in analytical methods, biomonitoring, and computational exposure assessment, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deployment of an organizational and predictive framework for exposure science analogous to the computationally enabled “systems approaches” used in the biological sciences is a necessary step in this evolution. Here we propose the aggregate exposure pathway (AEP) concept as the natural and complementary companion in the exposure sciences to the adverse outcome pathway (AOP) conceptmore » in the toxicological sciences. The AEP framework offers an intuitive approach to successful organization of exposure science data within individual units of prediction common to the field, setting the stage for exposure forecasting. Looking farther ahead, we envision direct linkages between aggregate exposure pathway and adverse outcome pathways, completing the source to outcome continuum and setting the stage for more efficient integration of exposure science and toxicity testing information. Together these frameworks form and inform a decision making framework with the flexibility for risk-based, hazard-based or exposure-based decisions.« less

Shared Decision-Making for Nursing Practice: An Integrative Review

PubMed Central

Truglio-Londrigan, Marie; Slyer, Jason T.

2018-01-01

Background: Shared decision-making has received national and international interest by providers, educators, researchers, and policy makers. The literature on shared decision-making is extensive, dealing with the individual components of shared decision-making rather than a comprehensive process. This view of shared decision-making leaves healthcare providers to wonder how to integrate shared decision-making into practice. Objective: To understand shared decision-making as a comprehensive process from the perspective of the patient and provider in all healthcare settings. Methods: An integrative review was conducted applying a systematic approach involving a literature search, data evaluation, and data analysis. The search included articles from PubMed, CINAHL, the Cochrane Central Register of Controlled Trials, and PsycINFO from 1970 through 2016. Articles included quantitative experimental and non-experimental designs, qualitative, and theoretical articles about shared decision-making between all healthcare providers and patients in all healthcare settings. Results: Fifty-two papers were included in this integrative review. Three categories emerged from the synthesis: (a) communication/ relationship building; (b) working towards a shared decision; and (c) action for shared decision-making. Each major theme contained sub-themes represented in the proposed visual representation for shared decision-making. Conclusion: A comprehensive understanding of shared decision-making between the nurse and the patient was identified. A visual representation offers a guide that depicts shared decision-making as a process taking place during a healthcare encounter with implications for the continuation of shared decisions over time offering patients an opportunity to return to the nurse for reconsiderations of past shared decisions. PMID:29456779

The Mother's Autonomy in Decision Making (MADM) scale: Patient-led development and psychometric testing of a new instrument to evaluate experience of maternity care.

PubMed

Vedam, Saraswathi; Stoll, Kathrin; Martin, Kelsey; Rubashkin, Nicholas; Partridge, Sarah; Thordarson, Dana; Jolicoeur, Ganga

2017-01-01

To develop and validate a new instrument that assesses women's autonomy and role in decision making during maternity care. Through a community-based participatory research process, service users designed, content validated, and administered a cross-sectional quantitative survey, including 31 items on the experience of decision-making. Pregnancy experiences (n = 2514) were reported by 1672 women who saw a single type of primary maternity care provider in British Columbia. They described care by a midwife, family physician or obstetrician during 1, 2 or 3 maternity care cycles. We conducted psychometric testing in three separate samples. We assessed reliability, item-to-total correlations, and the factor structure of the The Mothers' Autonomy in Decision Making (MADM) scale. We report MADM scores by care provider type, length of prenatal appointments, preferences for role in decision-making, and satisfaction with experience of decision-making. The MADM scale measures a single construct: autonomy in decision-making during maternity care. Cronbach alphas for the scale exceeded 0.90 for all samples and all provider groups. All item-to-total correlations were replicable across three samples and exceeded 0.7. Eigenvalue and scree plots exhibited a clear 90-degree angle, and factor analysis generated a one factor scale. MADM median scores were highest among women who were cared for by midwives, and 10 or more points lower for those who saw physicians. Increased time for prenatal appointments was associated with higher scale scores, and there were significant differences between providers with respect to average time spent in prenatal appointments. Midwifery care was associated with higher MADM scores, even during short prenatal appointments (<15 minutes). Among women who preferred to lead decisions around their care (90.8%), and who were dissatisfied with their experience of decision making, MADM scores were very low (median 14). Women with physician carers were consistently more likely to report dissatisfaction with their involvement in decision making. The Mothers Autonomy in Decision Making (MADM) scale is a reliable instrument for assessment of the experience of decision making during maternity care. This new scale was developed and content validated by community members representing various populations of childbearing women in BC including women from vulnerable populations. MADM measures women's ability to lead decision making, whether they are given enough time to consider their options, and whether their choices are respected. Women who experienced midwifery care reported greater autonomy than women under physician care, when engaging in decision-making around maternity care options. Differences in models of care, professional education, regulatory standards, and compensation for prenatal visits between midwives and physicians likely affect the time available for these discussions and prioritization of a shared decision making process. The MADM scale reflects person-driven priorities, and reliably assesses interactions with maternity providers related to a person's ability to lead decision-making over the course of maternity care.

Believer-Skeptic Meets Actor-Critic: Rethinking the Role of Basal Ganglia Pathways during Decision-Making and Reinforcement Learning

PubMed Central

Dunovan, Kyle; Verstynen, Timothy

2016-01-01

The flexibility of behavioral control is a testament to the brain's capacity for dynamically resolving uncertainty during goal-directed actions. This ability to select actions and learn from immediate feedback is driven by the dynamics of basal ganglia (BG) pathways. A growing body of empirical evidence conflicts with the traditional view that these pathways act as independent levers for facilitating (i.e., direct pathway) or suppressing (i.e., indirect pathway) motor output, suggesting instead that they engage in a dynamic competition during action decisions that computationally captures action uncertainty. Here we discuss the utility of encoding action uncertainty as a dynamic competition between opposing control pathways and provide evidence that this simple mechanism may have powerful implications for bridging neurocomputational theories of decision making and reinforcement learning. PMID:27047328

Believer-Skeptic Meets Actor-Critic: Rethinking the Role of Basal Ganglia Pathways during Decision-Making and Reinforcement Learning.

PubMed

Dunovan, Kyle; Verstynen, Timothy

2016-01-01

The flexibility of behavioral control is a testament to the brain's capacity for dynamically resolving uncertainty during goal-directed actions. This ability to select actions and learn from immediate feedback is driven by the dynamics of basal ganglia (BG) pathways. A growing body of empirical evidence conflicts with the traditional view that these pathways act as independent levers for facilitating (i.e., direct pathway) or suppressing (i.e., indirect pathway) motor output, suggesting instead that they engage in a dynamic competition during action decisions that computationally captures action uncertainty. Here we discuss the utility of encoding action uncertainty as a dynamic competition between opposing control pathways and provide evidence that this simple mechanism may have powerful implications for bridging neurocomputational theories of decision making and reinforcement learning.

Improving IT Portfolio Management Decision Confidence Using Multi-Criteria Decision Making and Hypervariate Display Techniques

ERIC Educational Resources Information Center

Landmesser, John Andrew

2014-01-01

Information technology (IT) investment decision makers are required to process large volumes of complex data. An existing body of knowledge relevant to IT portfolio management (PfM), decision analysis, visual comprehension of large volumes of information, and IT investment decision making suggest Multi-Criteria Decision Making (MCDM) and…

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

Evaluating the Effectiveness of Science for Decision-Making: Water Managers and Tree- Ring Data in the Western United States

NASA Astrophysics Data System (ADS)

Rice, J. L.; Woodhouse, C.; Lukas, J.

2008-12-01

Current climate variability, potential impacts of climate change, and limited resources in the face of growing demand are increasingly prompting water managers in the western United States to consider and use data from climate-related research in water resource planning. Much of these data are produced by stakeholder- driven science programs, such as NOAA's Regional Integrated Science Assessments (RISAs), but there have been few efforts to evaluate the effectiveness of these science-to-application efforts. Over the past several years, researchers with the Western Water Assessment (WWA) RISA have been providing tree-ring reconstructions of streamflow to water managers in Colorado and other western states, and presenting technical workshops explaining the applications of these tree-ring data for water management and planning. Using in-depth interviews and a survey questionnaire, we have assessed the effectiveness and outcomes of these engagements, addressing (1) the factors that have prompted water managers to seek out tree-ring data, (2) how paleoclimate data has been made relevant and accessible for water resource planning, and (3) how tree-ring data and information have been utilized by water managers and other workshop participants. We also provide an assessment of challenges and opportunities that exist in the translation of climate science for decision-making, including how tree-ring data are interpreted in the context of water planning paradigms, issues of credibility and acceptance of tree ring data, and what data needs exist in different planning environments. These findings have broader application in improving and evaluating science-policy interactions related to climate and climate change.

Towards an integrated approach to natural hazards risk assessment using GIS: with reference to bushfires.

PubMed

Chen, Keping; Blong, Russell; Jacobson, Carol

2003-04-01

This paper develops a GIS-based integrated approach to risk assessment in natural hazards, with reference to bushfires. The challenges for undertaking this approach have three components: data integration, risk assessment tasks, and risk decision-making. First, data integration in GIS is a fundamental step for subsequent risk assessment tasks and risk decision-making. A series of spatial data integration issues within GIS such as geographical scales and data models are addressed. Particularly, the integration of both physical environmental data and socioeconomic data is examined with an example linking remotely sensed data and areal census data in GIS. Second, specific risk assessment tasks, such as hazard behavior simulation and vulnerability assessment, should be undertaken in order to understand complex hazard risks and provide support for risk decision-making. For risk assessment tasks involving heterogeneous data sources, the selection of spatial analysis units is important. Third, risk decision-making concerns spatial preferences and/or patterns, and a multicriteria evaluation (MCE)-GIS typology for risk decision-making is presented that incorporates three perspectives: spatial data types, data models, and methods development. Both conventional MCE methods and artificial intelligence-based methods with GIS are identified to facilitate spatial risk decision-making in a rational and interpretable way. Finally, the paper concludes that the integrated approach can be used to assist risk management of natural hazards, in theory and in practice.

Impact of nutrition on social decision making.

PubMed

Strang, Sabrina; Hoeber, Christina; Uhl, Olaf; Koletzko, Berthold; Münte, Thomas F; Lehnert, Hendrik; Dolan, Raymond J; Schmid, Sebastian M; Park, Soyoung Q

2017-06-20

Food intake is essential for maintaining homeostasis, which is necessary for survival in all species. However, food intake also impacts multiple biochemical processes that influence our behavior. Here, we investigate the causal relationship between macronutrient composition, its bodily biochemical impact, and a modulation of human social decision making. Across two studies, we show that breakfasts with different macronutrient compositions modulated human social behavior. Breakfasts with a high-carbohydrate/protein ratio increased social punishment behavior in response to norm violations compared with that in response to a low carbohydrate/protein meal. We show that these macronutrient-induced behavioral changes in social decision making are causally related to a lowering of plasma tyrosine levels. The findings indicate that, in a limited sense, "we are what we eat" and provide a perspective on a nutrition-driven modulation of cognition. The findings have implications for education, economics, and public policy, and emphasize that the importance of a balanced diet may extend beyond the mere physical benefits of adequate nutrition.

Impact of nutrition on social decision making

PubMed Central

Strang, Sabrina; Hoeber, Christina; Uhl, Olaf; Koletzko, Berthold; Münte, Thomas F.; Lehnert, Hendrik; Dolan, Raymond J.; Schmid, Sebastian M.; Park, Soyoung Q.

2017-01-01

Food intake is essential for maintaining homeostasis, which is necessary for survival in all species. However, food intake also impacts multiple biochemical processes that influence our behavior. Here, we investigate the causal relationship between macronutrient composition, its bodily biochemical impact, and a modulation of human social decision making. Across two studies, we show that breakfasts with different macronutrient compositions modulated human social behavior. Breakfasts with a high-carbohydrate/protein ratio increased social punishment behavior in response to norm violations compared with that in response to a low carbohydrate/protein meal. We show that these macronutrient-induced behavioral changes in social decision making are causally related to a lowering of plasma tyrosine levels. The findings indicate that, in a limited sense, “we are what we eat” and provide a perspective on a nutrition-driven modulation of cognition. The findings have implications for education, economics, and public policy, and emphasize that the importance of a balanced diet may extend beyond the mere physical benefits of adequate nutrition. PMID:28607064

Optimal policy for value-based decision-making.

PubMed

Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

2016-08-18

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.

Optimal policy for value-based decision-making

PubMed Central

Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

2016-01-01

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down. PMID:27535638

Exploring Techniques of Developing Writing Skill in IELTS Preparatory Courses: A Data-Driven Study

ERIC Educational Resources Information Center

Ostovar-Namaghi, Seyyed Ali; Safaee, Seyyed Esmail

2017-01-01

Being driven by the hypothetico-deductive mode of inquiry, previous studies have tested the effectiveness of theory-driven interventions under controlled experimental conditions to come up with universally applicable generalizations. To make a case in the opposite direction, this data-driven study aims at uncovering techniques and strategies…

Decision-Making Phenomena Described by Expert Nurses Working in Urban Community Health Settings.

ERIC Educational Resources Information Center

Watkins, Mary P.

1998-01-01

Expert community health nurses (n=28) described crucial clinical situations. Content analysis revealed that decision making was both rational and intuitive. Eight themes were identified: decision-making focus, type, purpose, decision-maker characteristics, sequencing of events, data collection methods, facilitators/barriers, and decision-making…

Decision tools in health care: focus on the problem, not the solution.

PubMed

Liu, Joseph; Wyatt, Jeremy C; Altman, Douglas G

2006-01-20

Systematic reviews or randomised-controlled trials usually help to establish the effectiveness of drugs and other health technologies, but are rarely sufficient by themselves to ensure actual clinical use of the technology. The process from innovation to routine clinical use is complex. Numerous computerised decision support systems (DSS) have been developed, but many fail to be taken up into actual use. Some developers construct technologically advanced systems with little relevance to the real world. Others did not determine whether a clinical need exists. With NHS investing 5 billion pounds sterling in computer systems, also occurring in other countries, there is an urgent need to shift from a technology-driven approach to one that identifies and employs the most cost-effective method to manage knowledge, regardless of the technology. The generic term, 'decision tool' (DT), is therefore suggested to demonstrate that these aids, which seem different technically, are conceptually the same from a clinical viewpoint. Many computerised DSSs failed for various reasons, for example, they were not based on best available knowledge; there was insufficient emphasis on their need for high quality clinical data; their development was technology-led; or evaluation methods were misapplied. We argue that DSSs and other computer-based, paper-based and even mechanical decision aids are members of a wider family of decision tools. A DT is an active knowledge resource that uses patient data to generate case specific advice, which supports decision making about individual patients by health professionals, the patients themselves or others concerned about them. The identification of DTs as a consistent and important category of health technology should encourage the sharing of lessons between DT developers and users and reduce the frequency of decision tool projects focusing only on technology. The focus of evaluation should become more clinical, with the impact of computer-based DTs being evaluated against other computer, paper- or mechanical tools, to identify the most cost effective tool for each clinical problem. We suggested the generic term 'decision tool' to demonstrate that decision-making aids, such as computerised DSSs, paper algorithms, and reminders are conceptually the same, so the methods to evaluate them should be the same.

Value(s)-Driven Decision-Making: The Ethics Work of English Headteachers within Discourses of Constraint

ERIC Educational Resources Information Center

Hammersley-Fletcher, Linda

2015-01-01

This article considers the experiences and perceptions of practising English headteachers and the tensions that they face when juggling government prescription and government initiatives, which may be antagonistic to their educational values and beliefs. Managerial control over teachers work has been particularly acute and destructive to…

Completing the Link between Exposure Science and Toxicology for Improved Environmental Health Decision Making: The Aggregate Exposure Pathway Framework

EPA Science Inventory

Driven by major scientific advances in analytical methods, biomonitoring, computation, and a newly articulated vision for a greater impact in public health, the field of exposure science is undergoing a rapid transition from a field of observation to a field of prediction. Deploy...

Vulnerable patients' perceptions of health care quality and quality data.

PubMed

Raven, Maria Catherine; Gillespie, Colleen C; DiBennardo, Rebecca; Van Busum, Kristin; Elbel, Brian

2012-01-01

Little is known about how patients served by safety-net hospitals utilize and respond to hospital quality data. To understand how vulnerable, lower income patients make health care decisions and define quality of care and whether hospital quality data factor into such decisions and definitions. Mixed quantitative and qualitative methods were used to gather primary data from patients at an urban, tertiary-care safety-net hospital. The study hospital is a member of the first public hospital system to voluntarily post hospital quality data online for public access. Patients were recruited from outpatient and inpatient clinics. Surveys were used to collect data on participants' sociodemographic characteristics, health literacy, health care experiences, and satisfaction variables. Focus groups were used to explore a representative sample of 24 patients' health care decision making and views of quality. Data from focus group transcripts were iteratively coded and analyzed by the authors. Focus group participants were similar to the broader diverse, low-income clinic. Participants reported exercising choice in making decisions about where to seek health care. Multiple sources influenced decision-making processes including participants' own beliefs and values, social influences, and prior experiences. Hospital quality data were notably absent as a source of influence in health care decision making for this population largely because participants were unaware of its existence. Participants' views of hospital quality were influenced by the quality and efficiency of services provided (with an emphasis on the doctor-patient relationship) and patient centeredness. When presented with it, patients appreciated the hospital quality data and, with guidance, were interested in incorporating it into health care decision making. Results suggest directions for optimizing the presentation, content, and availability of hospital quality data. Future research will explore how similar populations form and make choices based on presentation of hospital quality data.

A Web-Based Tool to Support Data-Based Early Intervention Decision Making

ERIC Educational Resources Information Center

Buzhardt, Jay; Greenwood, Charles; Walker, Dale; Carta, Judith; Terry, Barbara; Garrett, Matthew

2010-01-01

Progress monitoring and data-based intervention decision making have become key components of providing evidence-based early childhood special education services. Unfortunately, there is a lack of tools to support early childhood service providers' decision-making efforts. The authors describe a Web-based system that guides service providers…

Data Informed Decision Making--Perspectives of Oklahoma Superintendents

ERIC Educational Resources Information Center

Kettles, Thomas D.

2017-01-01

This descriptive, multiple case study was designed to convey a clear portrayal of the DIDM practice of six superintendents and to provide a description of what these superintendents employ during their decision making process. The ability of local education leaders to strategically influence the use of data for decision making has a large effect…

District decision-making for health in low-income settings: a systematic literature review.

PubMed

Wickremasinghe, Deepthi; Hashmi, Iram Ejaz; Schellenberg, Joanna; Avan, Bilal Iqbal

2016-09-01

Health management information systems (HMIS) produce large amounts of data about health service provision and population health, and provide opportunities for data-based decision-making in decentralized health systems. Yet the data are little-used locally. A well-defined approach to district-level decision-making using health data would help better meet the needs of the local population. In this second of four papers on district decision-making for health in low-income settings, our aim was to explore ways in which district administrators and health managers in low- and lower-middle-income countries use health data to make decisions, to describe the decision-making tools they used and identify challenges encountered when using these tools. A systematic literature review, following PRISMA guidelines, was undertaken. Experts were consulted about key sources of information. A search strategy was developed for 14 online databases of peer reviewed and grey literature. The resources were screened independently by two reviewers using pre-defined inclusion criteria. The 14 papers included were assessed for the quality of reported evidence and a descriptive evidence synthesis of the review findings was undertaken. We found 12 examples of tools to assist district-level decision-making, all of which included two key stages-identification of priorities, and development of an action plan to address them. Of those tools with more steps, four included steps to review or monitor the action plan agreed, suggesting the use of HMIS data. In eight papers HMIS data were used for prioritization. Challenges to decision-making processes fell into three main categories: the availability and quality of health and health facility data; human dynamics and financial constraints. Our findings suggest that evidence is available about a limited range of processes that include the use of data for decision-making at district level. Standardization and pre-testing in diverse settings would increase the potential that these tools could be used more widely. © The Author 2016. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities.

PubMed

Hallgren, Kevin A; Bauer, Amy M; Atkins, David C

2017-06-01

Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains. © 2017 Wiley Periodicals, Inc.

Consideration of Insulin Pumps or Continuous Glucose Monitors by Adolescents With Type 1 Diabetes and Their Parents: Stakeholder Engagement in the Design of Web-Based Decision Aids.

PubMed

Wysocki, Tim; Hirschfeld, Fiona; Miller, Louis; Izenberg, Neil; Dowshen, Steven A; Taylor, Alex; Milkes, Amy; Shinseki, Michelle T; Bejarano, Carolina; Kozikowski, Chelsea; Kowal, Karen; Starr-Ashton, Penny; Ross, Judith L; Kummer, Mark; Carakushansky, Mauri; Lyness, D'Arcy; Brinkman, William; Pierce, Jessica; Fiks, Alexander; Christofferson, Jennifer; Rafalko, Jessica; Lawson, Margaret L

2016-08-01

This article describes the stakeholder-driven design, development, and testing of web-based, multimedia decision aids for youth with type 1 diabetes who are considering the insulin pump or continuous glucose monitoring and their parents. This is the initial phase of work designed to develop and evaluate the efficacy of these decision aids in promoting improved decision-making engagement with use of a selected device. Qualitative interviews of 36 parents and adolescents who had previously faced these decisions and 12 health care providers defined the content, format and structure of the decision aids. Experts in children's health media helped the research team to plan, create, and refine multimedia content and its presentation. A web development firm helped organize the content into a user-friendly interface and enabled tracking of decision aid utilization. Throughout, members of the research team, adolescents, parents, and 3 expert consultants offered perspectives about the website content, structure, and function until the design was complete. With the decision aid websites completed, the next phase of the project is a randomized controlled trial of usual clinical practice alone or augmented by use of the decision aid websites. Stakeholder-driven development of multimedia, web-based decision aids requires meticulous attention to detail but can yield exceptional resources for adolescents and parents contemplating major changes to their diabetes regimens. © 2016 The Author(s).

Linking data to decision-making: applying qualitative data analysis methods and software to identify mechanisms for using outcomes data.

PubMed

Patel, Vaishali N; Riley, Anne W

2007-10-01

A multiple case study was conducted to examine how staff in child out-of-home care programs used data from an Outcomes Management System (OMS) and other sources to inform decision-making. Data collection consisted of thirty-seven semi-structured interviews with clinicians, managers, and directors from two treatment foster care programs and two residential treatment centers, and individuals involved with developing the OMS; and observations of clinical and quality management meetings. Case study and grounded theory methodology guided analyses. The application of qualitative data analysis software is described. Results show that although staff rarely used data from the OMS, they did rely on other sources of systematically collected information to inform clinical, quality management, and program decisions. Analyses of how staff used these data suggest that improving the utility of OMS will involve encouraging staff to participate in data-based decision-making, and designing and implementing OMS in a manner that reflects how decision-making processes operate.

Examining the Role of the Human Hippocampus in Approach-Avoidance Decision Making Using a Novel Conflict Paradigm and Multivariate Functional Magnetic Resonance Imaging.

PubMed

O'Neil, Edward B; Newsome, Rachel N; Li, Iris H N; Thavabalasingam, Sathesan; Ito, Rutsuko; Lee, Andy C H

2015-11-11

Rodent models of anxiety have implicated the ventral hippocampus in approach-avoidance conflict processing. Few studies have, however, examined whether the human hippocampus plays a similar role. We developed a novel decision-making paradigm to examine neural activity when participants made approach/avoidance decisions under conditions of high or absent approach-avoidance conflict. Critically, our task required participants to learn the associated reward/punishment values of previously neutral stimuli and controlled for mnemonic and spatial processing demands, both important issues given approach-avoidance behavior in humans is less tied to predation and foraging compared to rodents. Participants played a points-based game where they first attempted to maximize their score by determining which of a series of previously neutral image pairs should be approached or avoided. During functional magnetic resonance imaging, participants were then presented with novel pairings of these images. These pairings consisted of images of congruent or opposing learned valences, the latter creating conditions of high approach-avoidance conflict. A data-driven partial least squares multivariate analysis revealed two reliable patterns of activity, each revealing differential activity in the anterior hippocampus, the homolog of the rodent ventral hippocampus. The first was associated with greater hippocampal involvement during trials with high as opposed to no approach-avoidance conflict, regardless of approach or avoidance behavior. The second pattern encompassed greater hippocampal activity in a more anterior aspect during approach compared to avoid responses, for conflict and no-conflict conditions. Multivoxel pattern classification analyses yielded converging findings, underlining a role of the anterior hippocampus in approach-avoidance conflict decision making. Approach-avoidance conflict has been linked to anxiety and occurs when a stimulus or situation is associated with reward and punishment. Although rodent work has implicated the hippocampus in approach-avoidance conflict processing, there is limited data on whether this role applies to learned, as opposed to innate, incentive values, and whether the human hippocampus plays a similar role. Using functional neuroimaging with a novel decision-making task that controlled for perceptual and mnemonic processing, we found that the human hippocampus was significantly active when approach-avoidance conflict was present for stimuli with learned incentive values. These findings demonstrate a role for the human hippocampus in approach-avoidance decision making that cannot be explained easily by hippocampal-dependent long-term memory or spatial cognition. Copyright © 2015 the authors 0270-6474/15/3515040-11$15.00/0.

A light-stimulated synaptic device based on graphene hybrid phototransistor

NASA Astrophysics Data System (ADS)

Qin, Shuchao; Wang, Fengqiu; Liu, Yujie; Wan, Qing; Wang, Xinran; Xu, Yongbing; Shi, Yi; Wang, Xiaomu; Zhang, Rong

2017-09-01

Neuromorphic chips refer to an unconventional computing architecture that is modelled on biological brains. They are increasingly employed for processing sensory data for machine vision, context cognition, and decision making. Despite rapid advances, neuromorphic computing has remained largely an electronic technology, making it a challenge to access the superior computing features provided by photons, or to directly process vision data that has increasing importance to artificial intelligence. Here we report a novel light-stimulated synaptic device based on a graphene-carbon nanotube hybrid phototransistor. Significantly, the device can respond to optical stimuli in a highly neuron-like fashion and exhibits flexible tuning of both short- and long-term plasticity. These features combined with the spatiotemporal processability make our device a capable counterpart to today’s electrically-driven artificial synapses, with superior reconfigurable capabilities. In addition, our device allows for generic optical spike processing, which provides a foundation for more sophisticated computing. The silicon-compatible, multifunctional photosensitive synapse opens up a new opportunity for neural networks enabled by photonics and extends current neuromorphic systems in terms of system complexities and functionalities.

Instrumentation: Software-Driven Instrumentation: The New Wave.

ERIC Educational Resources Information Center

Salit, M. L.; Parsons, M. L.

1985-01-01

Software-driven instrumentation makes measurements that demand a computer as an integral part of either control, data acquisition, or data reduction. The structure of such instrumentation, hardware requirements, and software requirements are discussed. Examples of software-driven instrumentation (such as wavelength-modulated continuum source…

Integrated climate and land change research to improve decision-making and resource management in Southern Africa: The SASSCAL approach

NASA Astrophysics Data System (ADS)

Helmschrot, J.; Olwoch, J. M.

2017-12-01

The ability of countries in southern Africa to jointly respond to climate challenges with scientifically informed and evidence-based actions and policy decisions remains low due to limited scientific research capacity and infrastructure. The Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL; www.sasscal.org) addresses this gap by implementing a high-level framework to guide research and innovation investments in climate change and adaptive land management interventions in Southern Africa. With a strong climate service component as cross-cutting topic, SASSCAL's focus is to improve the understanding of climate and land management change impacts on the natural and socio-economic environment in Southern Africa. The paper presents a variety of SASSCAL driven activities which contribute to better understand climate and long-term environmental change dynamics at various temporal and spatial scales in Southern Afrika and how these activities are linked to support research and decision-making to optimize agricultural practices as well as sustainable environmental and water resources management. To provide consistent and reliable climate information for Southern Africa, SASSCAL offers various climate services ranging from real-time climate observation across the region utilizing the SASSCAL WeatherNet to regional climate change analysis and modelling efforts at seasonal-to-decadal timescales using climate data from various sources. SASSCAL also offers the current state of the environment in terms of recent data on changes in the environment that are necessary for setting appropriate adaptation strategies . The paper will further demonstrate how these services are utilized for interdisciplinary research on the impact of climate change on natural resources and socio-economic development in the SASSCAL countries and how this knowledge can be effectively used to mitigate and adapt to climate change by informed decision-making from farm to regional level.

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew

PubMed Central

Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C.; Rinaldo, Andrea

2018-01-01

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated. PMID:29768401

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew.

PubMed

Pasetto, Damiano; Finger, Flavio; Camacho, Anton; Grandesso, Francesco; Cohuet, Sandra; Lemaitre, Joseph C; Azman, Andrew S; Luquero, Francisco J; Bertuzzo, Enrico; Rinaldo, Andrea

2018-05-01

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.

Improving Breast Cancer Surgical Treatment Decision Making: The iCanDecide Randomized Clinical Trial.

PubMed

Hawley, Sarah T; Li, Yun; An, Lawrence C; Resnicow, Kenneth; Janz, Nancy K; Sabel, Michael S; Ward, Kevin C; Fagerlin, Angela; Morrow, Monica; Jagsi, Reshma; Hofer, Timothy P; Katz, Steven J

2018-03-01

Purpose This study was conducted to determine the effect of iCanDecide, an interactive and tailored breast cancer treatment decision tool, on the rate of high-quality patient decisions-both informed and values concordant-regarding locoregional breast cancer treatment and on patient appraisal of decision making. Methods We conducted a randomized clinical trial of newly diagnosed patients with early-stage breast cancer making locoregional treatment decisions. From 22 surgical practices, 537 patients were recruited and randomly assigned online to the iCanDecide interactive and tailored Web site (intervention) or the iCanDecide static Web site (control). Participants completed a baseline survey and were mailed a follow-up survey 4 to 5 weeks after enrollment to assess the primary outcome of a high-quality decision, which consisted of two components, high knowledge and values-concordant treatment, and secondary outcomes (decision preparation, deliberation, and subjective decision quality). Results Patients in the intervention arm had higher odds of making a high-quality decision than did those in the control arm (odds ratio, 2.00; 95% CI, 1.37 to 2.92; P = .0004), which was driven primarily by differences in the rates of high knowledge between groups. The majority of patients in both arms made values-concordant treatment decisions (78.6% in the intervention arm and 81.4% in the control arm). More patients in the intervention arm had high decision preparation (estimate, 0.18; 95% CI, 0.02 to 0.34; P = .027), but there were no significant differences in the other decision appraisal outcomes. The effect of the intervention was similar for women who were leaning strongly toward a treatment option at enrollment compared with those who were not. Conclusion The tailored and interactive iCanDecide Web site, which focused on knowledge building and values clarification, positively affected high-quality decisions largely by improving knowledge compared with static online information. To be effective, future patient-facing decision tools should be integrated into the clinical workflow to improve decision making.

Light- and water-use efficiency model synergy: a revised look at crop yield estimation for agricultural decision-making

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K. P.

2015-12-01

Large-area crop yield models (LACMs) are commonly employed to address climate-driven changes in crop yield and inform policy makers concerned with climate change adaptation. Production efficiency models (PEMs), a class of LACMs that rely on the conservative response of carbon assimilation to incoming solar radiation absorbed by a crop contingent on environmental conditions, have increasingly been used over large areas with remote sensing spectral information to improve the spatial resolution of crop yield estimates and address important data gaps. Here, we present a new PEM that combines model principles from the remote sensing-based crop yield and evapotranspiration (ET) model literature. One of the major limitations of PEMs is that they are evaluated using data restricted in both space and time. To overcome this obstacle, we first validated the model using 2009-2014 eddy covariance flux tower Gross Primary Production data in a rice field in the Central Valley of California- a critical agro-ecosystem of the United States. This evaluation yielded a Willmot's D and mean absolute error of 0.81 and 5.24 g CO2/d, respectively, using CO2, leaf area, temperature, and moisture constraints from the MOD16 ET model, Priestley-Taylor ET model, and the Global Production Efficiency Model (GLOPEM). A Monte Carlo simulation revealed that the model was most sensitive to the Enhanced Vegetation Index (EVI) input, followed by Photosynthetically Active Radiation, vapor pressure deficit, and air temperature. The model will now be evaluated using 30 x 30m (Landsat resolution) biomass transects developed in 2011 and 2012 from spectroradiometric and other non-destructive in situ metrics for several cotton, maize, and rice fields across the Central Valley. Finally, the model will be driven by Daymet and MODIS data over the entire State of California and compared with county-level crop yield statistics. It is anticipated that the new model will facilitate agro-climatic decision-making in various regions across the globe and with different remote sensing inputs, given its interpretability, low data requirement, flexibility, and high correlation with in situ data.

Many faces of rationality: Implications of the great rationality debate for clinical decision‐making

PubMed Central

Elqayam, Shira

2017-01-01

Abstract Given that more than 30% of healthcare costs are wasted on inappropriate care, suboptimal care is increasingly connected to the quality of medical decisions. It has been argued that personal decisions are the leading cause of death, and 80% of healthcare expenditures result from physicians' decisions. Therefore, improving healthcare necessitates improving medical decisions, ie, making decisions (more) rational. Drawing on writings from The Great Rationality Debate from the fields of philosophy, economics, and psychology, we identify core ingredients of rationality commonly encountered across various theoretical models. Rationality is typically classified under umbrella of normative (addressing the question how people “should” or “ought to” make their decisions) and descriptive theories of decision‐making (which portray how people actually make their decisions). Normative theories of rational thought of relevance to medicine include epistemic theories that direct practice of evidence‐based medicine and expected utility theory, which provides the basis for widely used clinical decision analyses. Descriptive theories of rationality of direct relevance to medical decision‐making include bounded rationality, argumentative theory of reasoning, adaptive rationality, dual processing model of rationality, regret‐based rationality, pragmatic/substantive rationality, and meta‐rationality. For the first time, we provide a review of wide range of theories and models of rationality. We showed that what is “rational” behaviour under one rationality theory may be irrational under the other theory. We also showed that context is of paramount importance to rationality and that no one model of rationality can possibly fit all contexts. We suggest that in context‐poor situations, such as policy decision‐making, normative theories based on expected utility informed by best research evidence may provide the optimal approach to medical decision‐making, whereas in the context‐rich circumstances other types of rationality, informed by human cognitive architecture and driven by intuition and emotions such as the aim to minimize regret, may provide better solution to the problem at hand. The choice of theory under which we operate is important as it determines both policy and our individual decision‐making. PMID:28730671

Initial Results from an Energy-Aware Airborne Dynamic, Data-Driven Application System Performing Sampling in Coherent Boundary-Layer Structures

NASA Astrophysics Data System (ADS)

Frew, E.; Argrow, B. M.; Houston, A. L.; Weiss, C.

2014-12-01

The energy-aware airborne dynamic, data-driven application system (EA-DDDAS) performs persistent sampling in complex atmospheric conditions by exploiting wind energy using the dynamic data-driven application system paradigm. The main challenge for future airborne sampling missions is operation with tight integration of physical and computational resources over wireless communication networks, in complex atmospheric conditions. The physical resources considered here include sensor platforms, particularly mobile Doppler radar and unmanned aircraft, the complex conditions in which they operate, and the region of interest. Autonomous operation requires distributed computational effort connected by layered wireless communication. Onboard decision-making and coordination algorithms can be enhanced by atmospheric models that assimilate input from physics-based models and wind fields derived from multiple sources. These models are generally too complex to be run onboard the aircraft, so they need to be executed in ground vehicles in the field, and connected over broadband or other wireless links back to the field. Finally, the wind field environment drives strong interaction between the computational and physical systems, both as a challenge to autonomous path planning algorithms and as a novel energy source that can be exploited to improve system range and endurance. Implementation details of a complete EA-DDDAS will be provided, along with preliminary flight test results targeting coherent boundary-layer structures.

Contextual influences on animal decision-making: Significance for behavior-based wildlife conservation and management.

PubMed

Owen, Megan A; Swaisgood, Ronald R; Blumstein, Daniel T

2017-01-01

Survival and successful reproduction require animals to make critical decisions amidst a naturally dynamic environmental and social background (i.e. "context"). However, human activities have pervasively, and rapidly, extended contextual variation into evolutionarily novel territory, potentially rendering evolved animal decision-making mechanisms and strategies maladaptive. We suggest that explicitly focusing on animal decision-making (ADM), by integrating and applying findings from studies of sensory ecology, cognitive psychology, behavioral economics and eco-evolutionary strategies, may enhance our understanding of, and our ability to predict how, human-driven changes in the environment and population demography will influence animal populations. Fundamentally, the decisions animals make involve evolved mechanisms, and behaviors emerge from the combined action of sensory integration, cognitive mechanisms and strategic rules of thumb, and any of these processes may have a disproportionate influence on behavior. Although there is extensive literature exploring ADM, it generally reflects a canalized, discipline-specific approach that lacks a unified conceptual framework. As a result, there has been limited application of ADM theory and research findings into predictive models that can enhance management outcomes, even though it is likely that the relative resilience of species to rapid environmental change is fundamentally a result of how ADM is linked to contextual variation. Here, we focus on how context influences ADM, and highlight ideas and results that may be most applicable to conservation biology. © 2016 International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Building an understanding of water use innovation adoption processes through farmer-driven experimentation

NASA Astrophysics Data System (ADS)

Sturdy, Jody D.; Jewitt, Graham P. W.; Lorentz, Simon A.

Smallholder farmers in Southern Africa are faced with the challenge of securing their livelihoods within the context of a wide variety of biophysical and socio-economic constraints. Agriculture is inherently risky, particularly in regions prone to drought or dry spells, and risk-averse farmers may be viewed by researchers or extension agents as reluctant to invest in agricultural innovations that have potential to improve their livelihoods. However, farmers themselves are more interested in personal livelihood security than any other stakeholder and it is the farmers’ perceptions of needs, investment options and risks that drive their decision-making process. A holistic approach to agricultural innovation development and extension is needed to address both socio-economic and biophysical dynamics that influence adoption and dissemination of innovations. This paper, presents a methodology for involving farmers from the Bergville district of South Africa in the process of innovation development through facilitation of farmer-driven gardening experiments. Facilitating farmer-driven experimentation allows farmers to methodically assess the value of innovations they choose to study while providing researchers with a venue for learning about socio-economic as well as biophysical influences on farmers’ decisions. With this knowledge, researchers can focus on developing innovations that are socially and economically appropriate and therefore, more readily adoptable. The participatory process gave farmers the tools they needed to make informed decisions through critical thinking and analysis and improved their confidence in explaining the function of innovations to others. Researchers were able to use farmers’ manually collected data and observations to supplement laboratory generated and electronically recorded information about soil water dynamics to understand water balances associated with different garden bed designs, and to investigate whether trench beds, drip irrigation and water harvesting with run-on ditches tended to improve water use efficiency. Wetting front detectors (WFD) were shown to have some potential as management tools for farmers, provided certain limitations are addressed, while drip irrigation was found to be impractical because the available drip kits were prone to malfunction and farmers believed they did not provide enough water to the plants. Farmers participating in a series of monthly, hands-on workshops that encouraged individual experimentation tended to adopt and sustain use of many introduced garden innovations. Farmers who were also seriously involved in a formalized research and experimentation process at their own homesteads became more proficient with gardening systems in general, through continual trial-and-error comparisons and making decisions based on observations, than those who were not involved. This suggests that the practice of on-going experimentation, once established, reaches beyond the limits of facilitation by researchers or extension agents, into the realm of sustainable change and livelihood improvement through adoption, adaptation and dissemination of agricultural innovations.

Impaired decision-making and selective cortical frontal thinning in Cushing's syndrome.

PubMed

Crespo, Iris; Esther, Granell-Moreno; Santos, Alicia; Valassi, Elena; Yolanda, Vives-Gilabert; De Juan-Delago, Manel; Webb, Susan M; Gómez-Ansón, Beatriz; Resmini, Eugenia

2014-12-01

Cushing's syndrome (CS) is caused by a glucocorticoid excess. This hypercortisolism can damage the prefrontal cortex, known to be important in decision-making. Our aim was to evaluate decision-making in CS and to explore cortical thickness. Thirty-five patients with CS (27 cured, eight medically treated) and thirty-five matched controls were evaluated using Iowa gambling task (IGT) and 3 Tesla magnetic resonance imaging (MRI) to assess cortical thickness. The IGT evaluates decision-making, including strategy and learning during the test. Cortical thickness was determined on MRI using freesurfer software tools, including a whole-brain analysis. There were no differences between medically treated and cured CS patients. They presented an altered decision-making strategy compared to controls, choosing a lower number of the safer cards (P < 0·05). They showed more difficulties than controls to learn the correct profiles of wins and losses for each card group (P < 0·05). In whole-brain analysis, patients with CS showed decreased cortical thickness in the left superior frontal cortex, left precentral cortex, left insular cortex, left and right rostral anterior cingulate cortex, and right caudal middle frontal cortex compared to controls (P < 0·001). Patients with CS failed to learn advantageous strategies and their behaviour was driven by short-term reward and long-term punishment, indicating learning problems because they did not use previous experience as a feedback factor to regulate their choices. These alterations in decision-making and the decreased cortical thickness in frontal areas suggest that chronic hypercortisolism promotes brain changes which are not completely reversible after endocrine remission. © 2014 John Wiley & Sons Ltd.

Automated Decision-Making and Big Data: Concerns for People With Mental Illness.

PubMed

Monteith, Scott; Glenn, Tasha

2016-12-01

Automated decision-making by computer algorithms based on data from our behaviors is fundamental to the digital economy. Automated decisions impact everyone, occurring routinely in education, employment, health care, credit, and government services. Technologies that generate tracking data, including smartphones, credit cards, websites, social media, and sensors, offer unprecedented benefits. However, people are vulnerable to errors and biases in the underlying data and algorithms, especially those with mental illness. Algorithms based on big data from seemingly unrelated sources may create obstacles to community integration. Voluntary online self-disclosure and constant tracking blur traditional concepts of public versus private data, medical versus non-medical data, and human versus automated decision-making. In contrast to sharing sensitive information with a physician in a confidential relationship, there may be numerous readers of information revealed online; data may be sold repeatedly; used in proprietary algorithms; and are effectively permanent. Technological changes challenge traditional norms affecting privacy and decision-making, and continued discussions on new approaches to provide privacy protections are needed.

Resurfacing the care in nursing by telephone: lessons from ambulatory oncology.

PubMed

Wilson, Rosemary; Hubert, John

2002-01-01

The practice of providing telephone mediated advice and assistance is often described as "telephone triage" in relevant literature. The decision-making processes required for priority-setting and the provision of advice have been found to be complex and multifaceted. Conceptualization of this valuable patient care activity as a linear "triage" function serves to make invisible the nursing care provided. This article explores the current practice of providing telephone mediated advice and assistance in the following 2 distinct nursing care settings: emergency departments and ambulatory oncology centers. Examination of this activity in these 2 settings provides a forum to discuss and critique legally and fiscally driven prescriptive protocol use to inform decision-making. The effectiveness of experiential knowledge coupled with the strengths of nurse-patient relationships suggests that a need exists to highlight the caring aspects of telephone mediated assistance.

Risk perception & strategic decision making :general insights, a framework, and specific application to electricity generation using nuclear energy.

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

Brewer, Jeffrey D.

The objective of this report is to promote increased understanding of decision making processes and hopefully to enable improved decision making regarding high-consequence, highly sophisticated technological systems. This report brings together insights regarding risk perception and decision making across domains ranging from nuclear power technology safety, cognitive psychology, economics, science education, public policy, and neural science (to name a few). It forms them into a unique, coherent, concise framework, and list of strategies to aid in decision making. It is suggested that all decision makers, whether ordinary citizens, academics, or political leaders, ought to cultivate their abilities to separate themore » wheat from the chaff in these types of decision making instances. The wheat includes proper data sources and helpful human decision making heuristics; these should be sought. The chaff includes ''unhelpful biases'' that hinder proper interpretation of available data and lead people unwittingly toward inappropriate decision making ''strategies''; obviously, these should be avoided. It is further proposed that successfully accomplishing the wheat vs. chaff separation is very difficult, yet tenable. This report hopes to expose and facilitate navigation away from decision-making traps which often ensnare the unwary. Furthermore, it is emphasized that one's personal decision making biases can be examined, and tools can be provided allowing better means to generate, evaluate, and select among decision options. Many examples in this report are tailored to the energy domain (esp. nuclear power for electricity generation). The decision making framework and approach presented here are applicable to any high-consequence, highly sophisticated technological system.« less

Management Data for Selection Decisions in Building Library Collections.

ERIC Educational Resources Information Center

Hamaker, Charles A.

1992-01-01

Discusses the use of library management data, particularly circulation data, in making selection decisions for library collection development based on experiences at Louisiana State University. Development of a collection based on actual use rather than perceived research needs is considered, and the decision-making process for serials…

Why do patients engage in medical tourism?

PubMed

Runnels, Vivien; Carrera, P M

2012-12-01

Medical tourism is commonly perceived and popularly depicted as an economic issue, both at the system and individual levels. The decision to engage in medical tourism, however, is more complex, driven by patients' unmet need, the nature of services sought and the manner by which treatment is accessed. In order to beneficially employ the opportunities medical tourism offers, and address and contain possible threats and harms, an informed decision is crucial. This paper aims to enhance the current knowledge on medical tourism by isolating the focal content of the decisions that patients make. Based on the existing literature, it proposes a sequential decision-making process in opting for or against medical care abroad, and engaging in medical tourism, including considerations of the required treatments, location of treatment, and quality and safety issues attendant to seeking care. Accordingly, it comments on the imperative of access to health information and the current regulatory environment which impact on this increasingly popular and complex form of accessing and providing medical care. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

The Relationship between Gender, Sexual Attitudes, Attitudes towards Gender and High School Counselors' Ethical Decision-Making regarding Adolescent Sexuality

ERIC Educational Resources Information Center

Martin-Donald, Kimberly A.

2010-01-01

This study investigated relationships between high school counselors' ethical decision-making, gender, attitudes towards gender, and sexual attitudes. Of the 161 respondents, only 157 participants' data sets were included in the data set. Participants completed the Ethical Decision-Making Questionnaire, The Brief Sexual Attitudes Scale (Hendrick,…

Age differences in the effect of framing on risky choice: A meta-analysis

PubMed Central

Best, Ryan; Charness, Neil

2015-01-01

The framing of decision scenarios in terms of potential gains versus losses has been shown to influence choice preferences between sure and risky options. Normative cognitive changes associated with aging have been known to affect decision-making, which has led to a number of studies investigating the influence of aging on the effect of framing. Mata, Josef, Samanez-Larkin, and Hertwig (2011) systematically reviewed the available literature using a meta-analytic approach, but did not include tests of homogeneity nor subsequent moderator variable analyses. The current review serves to extend the previous analysis to include such tests as well as update the pool of studies available for analysis. Results for both positively and negatively framed conditions were reviewed using two meta-analyses encompassing data collected from 3,232 subjects across 18 studies. Deviating from the previous results, the current analysis finds a tendency for younger adults to choose the risky option more often than older adults for positively framed items. Moderator variable analyses find this effect to likely be driven by the specific decision scenario, showing a significant effect with younger adults choosing the risky option more often in small-amount financial and large-amount mortality-based scenarios. For negatively framed items, the current review found no overall age difference in risky decision making, confirming the results from the prior meta-analysis. Moderator variable analyses conducted to address heterogeneity found younger adults to be more likely than older adults to choose the risky option for negatively framed high-amount mortality-based decision scenarios. Practical implications for older adults are discussed. PMID:26098168

Age differences in the effect of framing on risky choice: A meta-analysis.

PubMed

Best, Ryan; Charness, Neil

2015-09-01

The framing of decision scenarios in terms of potential gains versus losses has been shown to influence choice preferences between sure and risky options. Normative cognitive changes associated with aging have been known to affect decision making, which has led to a number of studies investigating the influence of aging on the effect of framing. Mata, Josef, Samanez-Larkin, and Hertwig (2011) systematically reviewed the available literature using a meta-analytic approach, but did not include tests of homogeneity or subsequent moderator variable analyses. The current review serves to extend the previous analysis to include such tests as well as update the pool of studies available for analysis. Results for both positively and negatively framed conditions were reviewed using 2 meta-analyses encompassing data collected from 3,232 subjects across 18 studies. Deviating from the previous results, the current analysis found a tendency for younger adults to choose the risky option more often than older adults for positively framed items. Moderator variable analyses found this effect likely to be driven by the specific decision scenario, showing a significant effect, with younger adults choosing the risky option more often in small-amount financial and large-amount mortality-based scenarios. For negatively framed items, the current review found no overall age difference in risky decision making, confirming the results from the prior meta-analysis. Moderator variable analyses conducted to address heterogeneity found younger adults to be more likely than older adults to choose the risky option for negatively framed high-amount mortality-based decision scenarios. Practical implications for older adults are discussed. (c) 2015 APA, all rights reserved).

Balance Sheets Versus Decision Dashboards to Support Patient Treatment Choices: A Comparative Analysis.

PubMed

Dolan, James G; Veazie, Peter J

2015-12-01

Growing recognition of the importance of involving patients in preference-driven healthcare decisions has highlighted the need to develop practical strategies to implement patient-centered shared decision-making. The use of tabular balance sheets to support clinical decision-making is well established. More recent evidence suggests that graphic, interactive decision dashboards can help people derive deeper a understanding of information within a specific decision context. We therefore conducted a non-randomized trial comparing the effects of adding an interactive dashboard to a static tabular balance sheet on patient decision-making. The study population consisted of members of the ResearchMatch registry who volunteered to participate in a study of medical decision-making. Two separate surveys were conducted: one in the control group and one in the intervention group. All participants were instructed to imagine they were newly diagnosed with a chronic illness and were asked to choose between three hypothetical drug treatments, which varied with regard to effectiveness, side effects, and out-of-pocket cost. Both groups made an initial treatment choice after reviewing a balance sheet. After a brief "washout" period, members of the control group made a second treatment choice after reviewing the balance sheet again, while intervention group members made a second treatment choice after reviewing an interactive decision dashboard containing the same information. After both choices, participants rated their degree of confidence in their choice on a 1 to 10 scale. Members of the dashboard intervention group were more likely to change their choice of preferred drug (10.2 versus 7.5%; p = 0.054) and had a larger increase in decision confidence than the control group (0.67 versus 0.075; p < 0.03). There were no statistically significant between-group differences in decisional conflict or decision aid acceptability. These findings suggest that clinical decision dashboards may be an effective point-of-care decision-support tool. Further research to explore this possibility is warranted.

Perceived Maternal Behavioral Control, Infant Behavior, and Milk Supply: A Qualitative Study.

PubMed

Peacock-Chambers, Elizabeth; Dicks, Kaitlin; Sarathy, Leela; Brown, Allison A; Boynton-Jarrett, Renée

Disparities persist in breastfeeding exclusivity and duration despite increases in breastfeeding initiation. The objective of this study was to examine factors that influence maternal decision making surrounding infant feeding practices over time in a diverse inner-city population. We conducted a prospective qualitative study with 20 mothers recruited from 2 urban primary care clinics. Participants completed open-ended interviews and demographic questionnaires in English or Spanish administered at approximately 2 weeks and 6 months postpartum. Transcripts were analyzed using a combined technique of inductive (data-driven) and deductive (theory-driven, based on the Theory of Planned Behavior) thematic analysis using 3 independent coders and iterative discussion to reach consensus. All women initiated breastfeeding, and 65% reported perceived insufficient milk (PIM). An association between PIM and behavioral control emerged as the overarching theme impacting early breastfeeding cessation and evolved over time. Early postpartum, PIM evoked maternal distress-strong emotional responses to infant crying and need to control infant behaviors. Later, mothers accepted a perceived lack of control over milk supply with minimal distress or as a natural process. Decisions to stop breastfeeding occurred through an iterative process, informed by trials of various strategies and observations of subsequent changes in infant behavior, strongly influenced by competing psychosocial demands. Infant feeding decisions evolve over time and are influenced by perceptions of control over infant behavior and milk supply. Tailored anticipatory guidance is needed to provide time-sensitive strategies to cope with challenging infant behaviors and promote maternal agency over breastfeeding in low-income populations.

Quantum-like model of brain's functioning: decision making from decoherence.

PubMed

Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu; Basieva, Irina; Khrennikov, Andrei

2011-07-21

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in a complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices (representing mental states). This equilibrium state determines Alice's mixed (i.e., probabilistic) strategy. We use a master equation in which quantum physics describes the process of decoherence as the result of interaction with environment. Thus our model is a model of thinking through decoherence of the initially pure mental state. Decoherence is induced by the interaction with memory and the external mental environment. We study (numerically) the dynamics of quantum entropy of Alice's mental state in the process of decision making. We also consider classical entropy corresponding to Alice's choices. We introduce a measure of Alice's diffidence as the difference between classical and quantum entropies of Alice's mental state. We see that (at least in our model example) diffidence decreases (approaching zero) in the process of decision making. Finally, we discuss the problem of neuronal realization of quantum-like dynamics in the brain; especially roles played by lateral prefrontal cortex or/and orbitofrontal cortex. Copyright © 2011 Elsevier Ltd. All rights reserved.

The Mother’s Autonomy in Decision Making (MADM) scale: Patient-led development and psychometric testing of a new instrument to evaluate experience of maternity care

PubMed Central

Vedam, Saraswathi; Stoll, Kathrin; Martin, Kelsey; Rubashkin, Nicholas; Partridge, Sarah; Thordarson, Dana; Jolicoeur, Ganga

2017-01-01

Shared decision making (SDM) is core to person-centered care and is associated with improved health outcomes. Despite this, there are no validated scales measuring women’s agency and ability to lead decision making during maternity care. Objective To develop and validate a new instrument that assesses women’s autonomy and role in decision making during maternity care. Design Through a community-based participatory research process, service users designed, content validated, and administered a cross-sectional quantitative survey, including 31 items on the experience of decision-making. Setting and participants Pregnancy experiences (n = 2514) were reported by 1672 women who saw a single type of primary maternity care provider in British Columbia. They described care by a midwife, family physician or obstetrician during 1, 2 or 3 maternity care cycles. We conducted psychometric testing in three separate samples. Main outcome measures We assessed reliability, item-to-total correlations, and the factor structure of the The Mothers’ Autonomy in Decision Making (MADM) scale. We report MADM scores by care provider type, length of prenatal appointments, preferences for role in decision-making, and satisfaction with experience of decision-making. Results The MADM scale measures a single construct: autonomy in decision-making during maternity care. Cronbach alphas for the scale exceeded 0.90 for all samples and all provider groups. All item-to-total correlations were replicable across three samples and exceeded 0.7. Eigenvalue and scree plots exhibited a clear 90-degree angle, and factor analysis generated a one factor scale. MADM median scores were highest among women who were cared for by midwives, and 10 or more points lower for those who saw physicians. Increased time for prenatal appointments was associated with higher scale scores, and there were significant differences between providers with respect to average time spent in prenatal appointments. Midwifery care was associated with higher MADM scores, even during short prenatal appointments (<15 minutes). Among women who preferred to lead decisions around their care (90.8%), and who were dissatisfied with their experience of decision making, MADM scores were very low (median 14). Women with physician carers were consistently more likely to report dissatisfaction with their involvement in decision making. Discussion The Mothers Autonomy in Decision Making (MADM) scale is a reliable instrument for assessment of the experience of decision making during maternity care. This new scale was developed and content validated by community members representing various populations of childbearing women in BC including women from vulnerable populations. MADM measures women’s ability to lead decision making, whether they are given enough time to consider their options, and whether their choices are respected. Women who experienced midwifery care reported greater autonomy than women under physician care, when engaging in decision-making around maternity care options. Differences in models of care, professional education, regulatory standards, and compensation for prenatal visits between midwives and physicians likely affect the time available for these discussions and prioritization of a shared decision making process. Conclusion The MADM scale reflects person-driven priorities, and reliably assesses interactions with maternity providers related to a person’s ability to lead decision-making over the course of maternity care. PMID:28231285

Role of Ideas and Ideologies in Evidence-Based Health Policy

PubMed Central

Prinja, S

2010-01-01

Policy making in health is largely thought to be driven by three ‘I’s namely ideas, interests and institutions. Recent years have seen a shift in approach with increasing reliance being placed on role of evidence for policy making. The present article ascertains the role of ideas and ideologies in shaping evidence which is used to aid in policy decisions. The article discusses different theories of research-policy interface and the relative freedom of research-based evidence from the influence of ideas. Examples from developed and developed countries are cited to illustrate the contentions made. The article highlights the complexity of the process of evidence-based policy making, in a world driven by existing political, social and cultural ideologies. Consideration of this knowledge is paramount where more efforts are being made to bridge the gap between the ‘two worlds’ of researchers and policy makers to make evidence-based policy as also for policy analysts. PMID:23112991

Visual anticipation biases conscious decision making but not bottom-up visual processing.

PubMed

Mathews, Zenon; Cetnarski, Ryszard; Verschure, Paul F M J

2014-01-01

Prediction plays a key role in control of attention but it is not clear which aspects of prediction are most prominent in conscious experience. An evolving view on the brain is that it can be seen as a prediction machine that optimizes its ability to predict states of the world and the self through the top-down propagation of predictions and the bottom-up presentation of prediction errors. There are competing views though on whether prediction or prediction errors dominate the formation of conscious experience. Yet, the dynamic effects of prediction on perception, decision making and consciousness have been difficult to assess and to model. We propose a novel mathematical framework and a psychophysical paradigm that allows us to assess both the hierarchical structuring of perceptual consciousness, its content and the impact of predictions and/or errors on conscious experience, attention and decision-making. Using a displacement detection task combined with reverse correlation, we reveal signatures of the usage of prediction at three different levels of perceptual processing: bottom-up fast saccades, top-down driven slow saccades and consciousnes decisions. Our results suggest that the brain employs multiple parallel mechanism at different levels of perceptual processing in order to shape effective sensory consciousness within a predicted perceptual scene. We further observe that bottom-up sensory and top-down predictive processes can be dissociated through cognitive load. We propose a probabilistic data association model from dynamical systems theory to model the predictive multi-scale bias in perceptual processing that we observe and its role in the formation of conscious experience. We propose that these results support the hypothesis that consciousness provides a time-delayed description of a task that is used to prospectively optimize real time control structures, rather than being engaged in the real-time control of behavior itself.

Qualitative Interviews Exploring Palliative Care Perspectives of Latinos on Dialysis.

PubMed

Cervantes, Lilia; Jones, Jacqueline; Linas, Stuart; Fischer, Stacy

2017-05-08

Compared with non-Latino whites with advanced illness, Latinos are less likely to have an advance directive or to die with hospice services. To improve palliative care disparities, international ESRD guidelines call for increased research on culturally responsive communication of advance care planning (ACP). The objective of our study was to explore the preferences of Latino patients receiving dialysis regarding symptom management and ACP. Qualitative study design using semistructured face-to-face interviews of 20 Latinos on hemodialysis between February and July of 2015. Data were analyzed using thematic analysis. Four themes were identified: Avoiding harms of medication (fear of addiction and damage to bodies, effective distractions, reliance on traditional remedies, fatalism: the sense that one's illness is deserved punishment); barriers and facilitators to ACP: faith, family, and home (family group decision-making, family reluctance to have ACP conversations, flexible decision-making conversations at home with family, ACP conversations incorporating trust and linguistic congruency, family-first and faith-driven decisions); enhancing wellbeing day-to-day (supportive relationships, improved understanding of illness leads to adherence, recognizing new self-value, maintaining a positive outlook); and distressing aspects of living with their illness (dietary restriction is culturally isolating and challenging for families, logistic challenges and socioeconomic disadvantage compounded by health literacy and language barriers, required rapid adjustments to chronic illness, demanding dialysis schedule). Latinos described unique cultural preferences such as avoidance of medications for symptom alleviation and a preference to have family group decision-making and ACP conversations at home. Understanding and integrating cultural values and preferences into palliative care offers the potential to improve disparities and achieve quality patient-centered care for Latinos with advanced illness. Copyright © 2017 by the American Society of Nephrology.

Qualitative Interviews Exploring Palliative Care Perspectives of Latinos on Dialysis

PubMed Central

Jones, Jacqueline; Linas, Stuart; Fischer, Stacy

2017-01-01

Background and objectives Compared with non-Latino whites with advanced illness, Latinos are less likely to have an advance directive or to die with hospice services. To improve palliative care disparities, international ESRD guidelines call for increased research on culturally responsive communication of advance care planning (ACP). The objective of our study was to explore the preferences of Latino patients receiving dialysis regarding symptom management and ACP. Design, setting, participants, & measurements Qualitative study design using semistructured face-to-face interviews of 20 Latinos on hemodialysis between February and July of 2015. Data were analyzed using thematic analysis. Results Four themes were identified: Avoiding harms of medication (fear of addiction and damage to bodies, effective distractions, reliance on traditional remedies, fatalism: the sense that one’s illness is deserved punishment); barriers and facilitators to ACP: faith, family, and home (family group decision-making, family reluctance to have ACP conversations, flexible decision-making conversations at home with family, ACP conversations incorporating trust and linguistic congruency, family-first and faith-driven decisions); enhancing wellbeing day-to-day (supportive relationships, improved understanding of illness leads to adherence, recognizing new self-value, maintaining a positive outlook); and distressing aspects of living with their illness (dietary restriction is culturally isolating and challenging for families, logistic challenges and socioeconomic disadvantage compounded by health literacy and language barriers, required rapid adjustments to chronic illness, demanding dialysis schedule). Conclusions Latinos described unique cultural preferences such as avoidance of medications for symptom alleviation and a preference to have family group decision-making and ACP conversations at home. Understanding and integrating cultural values and preferences into palliative care offers the potential to improve disparities and achieve quality patient-centered care for Latinos with advanced illness. PMID:28404600

The Neural Basis of Risky Choice with Affective Outcomes

PubMed Central

Suter, Renata S.; Pachur, Thorsten; Hertwig, Ralph; Endestad, Tor; Biele, Guido

2015-01-01

Both normative and many descriptive theories of decision making under risk are based on the notion that outcomes are weighted by their probability, with subsequent maximization of the (subjective) expected outcome. Numerous investigations from psychology, economics, and neuroscience have produced evidence consistent with this notion. However, this research has typically investigated choices involving relatively affect-poor, monetary outcomes. We compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems. Computational modeling of behavioral data and model-based neuroimaging analyses provide converging evidence for substantial differences in the respective decision mechanisms. Relative to affect-poor choices, affect-rich choices yielded a more strongly curved probability weighting function of cumulative prospect theory, thus signaling that the psychological impact of probabilities is strongly diminished for affect-rich outcomes. Examining task-dependent brain activation, we identified a region-by-condition interaction indicating qualitative differences of activation between affect-rich and affect-poor choices. Moreover, brain activation in regions that were more active during affect-poor choices (e.g., the supramarginal gyrus) correlated with individual trial-by-trial decision weights, indicating that these regions reflect processing of probabilities. Formal reverse inference Neurosynth meta-analyses suggested that whereas affect-poor choices seem to be based on brain mechanisms for calculative processes, affect-rich choices are driven by the representation of outcomes’ emotional value and autobiographical memories associated with them. These results provide evidence that the traditional notion of expectation maximization may not apply in the context of outcomes laden with affective responses, and that understanding the brain mechanisms of decision making requires the domain of the decision to be taken into account. PMID:25830918

The neural basis of risky choice with affective outcomes.

PubMed

Suter, Renata S; Pachur, Thorsten; Hertwig, Ralph; Endestad, Tor; Biele, Guido

2015-01-01

Both normative and many descriptive theories of decision making under risk are based on the notion that outcomes are weighted by their probability, with subsequent maximization of the (subjective) expected outcome. Numerous investigations from psychology, economics, and neuroscience have produced evidence consistent with this notion. However, this research has typically investigated choices involving relatively affect-poor, monetary outcomes. We compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems. Computational modeling of behavioral data and model-based neuroimaging analyses provide converging evidence for substantial differences in the respective decision mechanisms. Relative to affect-poor choices, affect-rich choices yielded a more strongly curved probability weighting function of cumulative prospect theory, thus signaling that the psychological impact of probabilities is strongly diminished for affect-rich outcomes. Examining task-dependent brain activation, we identified a region-by-condition interaction indicating qualitative differences of activation between affect-rich and affect-poor choices. Moreover, brain activation in regions that were more active during affect-poor choices (e.g., the supramarginal gyrus) correlated with individual trial-by-trial decision weights, indicating that these regions reflect processing of probabilities. Formal reverse inference Neurosynth meta-analyses suggested that whereas affect-poor choices seem to be based on brain mechanisms for calculative processes, affect-rich choices are driven by the representation of outcomes' emotional value and autobiographical memories associated with them. These results provide evidence that the traditional notion of expectation maximization may not apply in the context of outcomes laden with affective responses, and that understanding the brain mechanisms of decision making requires the domain of the decision to be taken into account.

Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias

NASA Astrophysics Data System (ADS)

Urai, Anne E.; Braun, Anke; Donner, Tobias H.

2017-03-01

While judging their sensory environments, decision-makers seem to use the uncertainty about their choices to guide adjustments of their subsequent behaviour. One possible source of these behavioural adjustments is arousal: decision uncertainty might drive the brain's arousal systems, which control global brain state and might thereby shape subsequent decision-making. Here, we measure pupil diameter, a proxy for central arousal state, in human observers performing a perceptual choice task of varying difficulty. Pupil dilation, after choice but before external feedback, reflects three hallmark signatures of decision uncertainty derived from a computational model. This increase in pupil-linked arousal boosts observers' tendency to alternate their choice on the subsequent trial. We conclude that decision uncertainty drives rapid changes in pupil-linked arousal state, which shape the serial correlation structure of ongoing choice behaviour.

Health decision making: lynchpin of evidence-based practice.

PubMed

Spring, Bonnie

2008-01-01

Health decision making is both the lynchpin and the least developed aspect of evidence-based practice. The evidence-based practice process requires integrating the evidence with consideration of practical resources and patient preferences and doing so via a process that is genuinely collaborative. Yet, the literature is largely silent about how to accomplish integrative, shared decision making. for evidence-based practice are discussed for 2 theories of clinician decision making (expected utility and fuzzy trace) and 2 theories of patient health decision making (transtheoretical model and reasoned action). Three suggestions are offered. First, it would be advantageous to have theory-based algorithms that weight and integrate the 3 data strands (evidence, resources, preferences) in different decisional contexts. Second, patients, not providers, make the decisions of greatest impact on public health, and those decisions are behavioral. Consequently, theory explicating how provider-patient collaboration can influence patient lifestyle decisions made miles from the provider's office is greatly needed. Third, although the preponderance of data on complex decisions supports a computational approach, such an approach to evidence-based practice is too impractical to be widely applied at present. More troublesomely, until patients come to trust decisions made computationally more than they trust their providers' intuitions, patient adherence will remain problematic. A good theory of integrative, collaborative health decision making remains needed.

Health Decision Making: Lynchpin of Evidence-Based Practice

PubMed Central

Spring, Bonnie

2008-01-01

Health decision making is both the lynchpin and the least developed aspect of evidence-based practice. The evidence-based practice process requires integrating the evidence with consideration of practical resources and patient preferences and doing so via a process that is genuinely collaborative. Yet, the literature is largely silent about how to accomplish integrative, shared decision making. Implications for evidence-based practice are discussed for 2 theories of clinician decision making (expected utility and fuzzy trace) and 2 theories of patient health decision making (transtheoretical model and reasoned action). Three suggestions are offered. First, it would be advantageous to have theory-based algorithms that weight and integrate the 3 data strands (evidence, resources, preferences) in different decisional contexts. Second, patients, not providers, make the decisions of greatest impact on public health, and those decisions are behavioral. Consequently, theory explicating how provider-patient collaboration can influence patient lifestyle decisions made miles from the provider's office is greatly needed. Third, although the preponderance of data on complex decisions supports a computational approach, such an approach to evidence-based practice is too impractical to be widely applied at present. More troublesomely, until patients come to trust decisions made computationally more than they trust their providers’ intuitions, patient adherence will remain problematic. A good theory of integrative, collaborative health decision making remains needed. PMID:19015288

The Role of Storytelling in Understanding Children's Moral/Ethic Decision-Making

ERIC Educational Resources Information Center

Hunter, Cheryl; Eder, Donna

2010-01-01

As students advance in their education, the use of stories and specifically the process of storytelling often wane from the central mode of learning to be replaced with more didactic methods and content-driven applications. However, the use of stories has remained a central component of moral/ethics education and continues to be used as a…

University Governance, Leadership and Management in a Decade of Diversification and Uncertainty

ERIC Educational Resources Information Center

Shattock, Michael

2013-01-01

The last decade has seen an acceleration of change in the way British universities have been governed, led and managed. This has substantially been driven by the instability of the external environment, which has encouraged a greater centralisation of decision-making leading to less governance and more management, but it is also a consequence of…

Investigating the Preservice Primary School, Mathematics and Science Teachers' STEM Awareness

ERIC Educational Resources Information Center

Bakirci, Hasan; Karisan, Dilek

2018-01-01

Today's life requires individuals to be prepared for complex world environment, to make complex decisions, and to have critical thinking skills related to everyday life issues at hand. STEM education is thought to be the glorious solution to thrive in a global knowledge driven world. Teachers are key elements for successful STEM education. Present…

Junior Faculty of Color in the Corporate University: Implications of Neoliberalism and Neoconservatism on Research, Teaching and Service

ERIC Educational Resources Information Center

Osei-Kofi, Nana

2012-01-01

In higher education today, an overwhelming acceptance of neoliberal and neoconservative ideologies that advance corporate logics of efficiency, competition and profit maximization is commonplace. Market-driven logics and neoconservative ideals shape decision-making about what is taught, how material is taught, who teaches, who does research, who…

Hot Topics: Cities, Energy Use, and Local Energy Decision Making | State,

Science.gov Websites

Making Hot Topics: Cities, Energy Use, and Local Energy Decision Making August 08, 2015 by Alexandra United States, read Alexandra's paper titled City-Level Energy Decision Making: Data Use in Energy Local, and Tribal Governments | NREL Hot Topics: Cities, Energy Use, and Local Energy Decision

Charting the expansion of strategic exploratory behavior during adolescence.

PubMed

Somerville, Leah H; Sasse, Stephanie F; Garrad, Megan C; Drysdale, Andrew T; Abi Akar, Nadine; Insel, Catherine; Wilson, Robert C

2017-02-01

Although models of exploratory decision making implicate a suite of strategies that guide the pursuit of information, the developmental emergence of these strategies remains poorly understood. This study takes an interdisciplinary perspective, merging computational decision making and developmental approaches to characterize age-related shifts in exploratory strategy from adolescence to young adulthood. Participants were 149 12-28-year-olds who completed a computational explore-exploit paradigm that manipulated reward value, information value, and decision horizon (i.e., the utility that information holds for future choices). Strategic directed exploration, defined as information seeking selective for long time horizons, emerged during adolescence and maintained its level through early adulthood. This age difference was partially driven by adolescents valuing immediate reward over new information. Strategic random exploration, defined as stochastic choice behavior selective for long time horizons, was invoked at comparable levels over the age range, and predicted individual differences in attitudes toward risk taking in daily life within the adolescent portion of the sample. Collectively, these findings reveal an expansion of the diversity of strategic exploration over development, implicate distinct mechanisms for directed and random exploratory strategies, and suggest novel mechanisms for adolescent-typical shifts in decision making. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The Influence of Televised Food Commercials on Children's Food Choices: Evidence from Ventromedial Prefrontal Cortex Activations.

PubMed

Bruce, Amanda S; Pruitt, Stephen W; Ha, Oh-Ryeong; Cherry, J Bradley C; Smith, Timothy R; Bruce, Jared M; Lim, Seung-Lark

2016-10-01

To investigate how food commercials influence children's food choices. Twenty-three children ages 8-14 years provided taste and health ratings for 60 food items. Subsequently, these children were scanned with the use of functional magnetic resonance imaging while making food choices (ie, "eat" or "not eat") after watching food and nonfood television commercials. Our results show that watching food commercials changes the way children consider the importance of taste when making food choices. Children did not use health values for their food choices, indicating children's decisions were largely driven by hedonic, immediate rewards (ie, "tastiness"); however, children placed significantly more importance on taste after watching food commercials compared with nonfood commercials. This change was accompanied by faster decision times during food commercial trials. The ventromedial prefrontal cortex, a reward valuation brain region, showed increased activity during food choices after watching food commercials compared with after watching nonfood commercials. Overall, our results suggest watching food commercials before making food choices may bias children's decisions based solely on taste, and that food marketing may systematically alter the psychological and neurobiologic mechanisms of children's food decisions. Copyright © 2016 Elsevier Inc. All rights reserved.

The Influence of Televised Food Commercials on Children's Food Choices: Evidence from Ventromedial Prefrontal Cortex Activations

PubMed Central

Bruce, Amanda S.; Pruitt, Stephen W.; Ha, Oh-Ryeong; Cherry, J. Bradley C.; Smith, Timothy R.; Bruce, Jared M.; Lim, Seung-Lark

2016-01-01

Objective To investigate how food commercials influence children's food choices. Study design Twenty-three children ages 8-14 years provided taste and health ratings for 60 food items. Subsequently, these children were scanned with the use of functional magnetic resonance imaging while making food choices (ie, “eat” or “not eat”) after watching food and nonfood television commercials. Results Our results show that watching food commercials changes the way children consider the importance of taste when making food choices. Children did not use health values for their food choices, indicating children's decisions were largely driven by hedonic, immediate rewards (ie, “tastiness”); however, children placed significantly more importance on taste after watching food commercials compared with nonfood commercials. This change was accompanied by faster decision times during food commercial trials. The ventromedial prefrontal cortex, a reward valuation brain region, showed increased activity during food choices after watching food commercials compared with after watching nonfood commercials. Conclusion Overall, our results suggest watching food commercials before making food choices may bias children's decisions based solely on taste, and that food marketing may systematically alter the psychological and neurobiologic mechanisms of children's food decisions. PMID:27526621

Adolescent psychological development, parenting styles, and pediatric decision making.

PubMed

Partridge, Brian C

2010-10-01

The United Nations Convention on the Rights of the Child risks harm to adolescents insofar as it encourages not only poor decision making by adolescents but also parenting styles that will have an adverse impact on the development of mature decision-making capacities in them. The empirical psychological and neurophysiological data weigh against augmenting and expression of the rights of children. Indeed, the data suggest grounds for expanding parental authority, not limiting its scope. At the very least, any adequate appreciation of the moral claims regarding the authority of parents with respect to the decision-making capacity of adolescents needs to be set within an understanding of the psychological and neurophysiological facts regarding the development of adolescent decision-making capacity.

Effects of an educational programme on shared decision-making among Korean nurses.

PubMed

Jo, Kae-Hwa; An, Gyeong-Ju

2015-12-01

This study was conducted to examine the effects of an educational programme on shared decision-making on end-of-life care performance, moral sensitivity and attitude towards shared decision-making among Korean nurses. A quasi-experimental study with a non-equivalent control group pretest-posttest design was used. Forty-one clinical nurses were recruited as participants from two different university hospitals located in Daegu, Korea. Twenty nurses in the control group received no intervention, and 21 nurses in the experimental group received the educational programme on shared decision-making. Data were collected with a questionnaire covering end-of-life care performance, moral sensitivity and attitude towards shared decision-making. Analysis of the data was done with the chi-square test, t-test and Fisher's exact test using SPSS/Win 17.0 (SPSS, Inc., Chicago, IL, USA). The experimental group showed significantly higher scores in moral sensitivity and attitude towards shared decision-making after the intervention compared with the control group. This study suggests that the educational programme on shared decision-making was effective in increasing the moral sensitivity and attitude towards shared decision-making among Korean nurses. © 2014 Wiley Publishing Asia Pty Ltd.

Alaska | State, Local, and Tribal Governments | NREL

Science.gov Websites

Alaska Advancing Energy Solutions in Alaska NREL provides objective, data-driven support to aid decision makers in Alaska as they take actions to deploy sustainable energy technologies, prepare for a clean-energy-driven economic transition, and reduce energy burdens in their jurisdictions. NREL's

An innovative approach to addressing childhood obesity: a knowledge-based infrastructure for supporting multi-stakeholder partnership decision-making in Quebec, Canada.

PubMed

Addy, Nii Antiaye; Shaban-Nejad, Arash; Buckeridge, David L; Dubé, Laurette

2015-01-23

Multi-stakeholder partnerships (MSPs) have become a widespread means for deploying policies in a whole of society strategy to address the complex problem of childhood obesity. However, decision-making in MSPs is fraught with challenges, as decision-makers are faced with complexity, and have to reconcile disparate conceptualizations of knowledge across multiple sectors with diverse sets of indicators and data. These challenges can be addressed by supporting MSPs with innovative tools for obtaining, organizing and using data to inform decision-making. The purpose of this paper is to describe and analyze the development of a knowledge-based infrastructure to support MSP decision-making processes. The paper emerged from a study to define specifications for a knowledge-based infrastructure to provide decision support for community-level MSPs in the Canadian province of Quebec. As part of the study, a process assessment was conducted to understand the needs of communities as they collect, organize, and analyze data to make decisions about their priorities. The result of this process is a "portrait", which is an epidemiological profile of health and nutrition in their community. Portraits inform strategic planning and development of interventions, and are used to assess the impact of interventions. Our key findings indicate ambiguities and disagreement among MSP decision-makers regarding causal relationships between actions and outcomes, and the relevant data needed for making decisions. MSP decision-makers expressed a desire for easy-to-use tools that facilitate the collection, organization, synthesis, and analysis of data, to enable decision-making in a timely manner. Findings inform conceptual modeling and ontological analysis to capture the domain knowledge and specify relationships between actions and outcomes. This modeling and analysis provide the foundation for an ontology, encoded using OWL 2 Web Ontology Language. The ontology is developed to provide semantic support for the MSP process, defining objectives, strategies, actions, indicators, and data sources. In the future, software interacting with the ontology can facilitate interactive browsing by decision-makers in the MSP in the form of concepts, instances, relationships, and axioms. Our ontology also facilitates the integration and interpretation of community data, and can help in managing semantic interoperability between different knowledge sources. Future work will focus on defining specifications for the development of a database of indicators and an information system to help decision-makers to view, analyze and organize indicators for their community. This work should improve MSP decision-making in the development of interventions to address childhood obesity.

An Innovative Approach to Addressing Childhood Obesity: A Knowledge-Based Infrastructure for Supporting Multi-Stakeholder Partnership Decision-Making in Quebec, Canada

PubMed Central

Addy, Nii Antiaye; Shaban-Nejad, Arash; Buckeridge, David L.; Dubé, Laurette

2015-01-01

Multi-stakeholder partnerships (MSPs) have become a widespread means for deploying policies in a whole of society strategy to address the complex problem of childhood obesity. However, decision-making in MSPs is fraught with challenges, as decision-makers are faced with complexity, and have to reconcile disparate conceptualizations of knowledge across multiple sectors with diverse sets of indicators and data. These challenges can be addressed by supporting MSPs with innovative tools for obtaining, organizing and using data to inform decision-making. The purpose of this paper is to describe and analyze the development of a knowledge-based infrastructure to support MSP decision-making processes. The paper emerged from a study to define specifications for a knowledge-based infrastructure to provide decision support for community-level MSPs in the Canadian province of Quebec. As part of the study, a process assessment was conducted to understand the needs of communities as they collect, organize, and analyze data to make decisions about their priorities. The result of this process is a “portrait”, which is an epidemiological profile of health and nutrition in their community. Portraits inform strategic planning and development of interventions, and are used to assess the impact of interventions. Our key findings indicate ambiguities and disagreement among MSP decision-makers regarding causal relationships between actions and outcomes, and the relevant data needed for making decisions. MSP decision-makers expressed a desire for easy-to-use tools that facilitate the collection, organization, synthesis, and analysis of data, to enable decision-making in a timely manner. Findings inform conceptual modeling and ontological analysis to capture the domain knowledge and specify relationships between actions and outcomes. This modeling and analysis provide the foundation for an ontology, encoded using OWL 2 Web Ontology Language. The ontology is developed to provide semantic support for the MSP process, defining objectives, strategies, actions, indicators, and data sources. In the future, software interacting with the ontology can facilitate interactive browsing by decision-makers in the MSP in the form of concepts, instances, relationships, and axioms. Our ontology also facilitates the integration and interpretation of community data, and can help in managing semantic interoperability between different knowledge sources. Future work will focus on defining specifications for the development of a database of indicators and an information system to help decision-makers to view, analyze and organize indicators for their community. This work should improve MSP decision-making in the development of interventions to address childhood obesity. PMID:25625409

Clinician and cancer patient views on patient participation in treatment decision-making: a quantitative and qualitative exploration

PubMed Central

Pieterse, A H; Baas-Thijssen, M C M; Marijnen, C A M; Stiggelbout, A M

2008-01-01

Patient participation in treatment decision-making is being increasingly advocated, although cancer treatments are often guideline-driven. Trade-offs between benefits and side effects underlying guidelines are made by clinicians. Evidence suggests that clinicians are inaccurate at predicting patient values. The aim was to assess what role oncologists and cancer patients prefer in deciding about treatment, and how they view patient participation in treatment decision-making. Seventy disease-free cancer patients and 60 oncologists (surgical, radiation, and medical) were interviewed about their role preferences using the Control Preferences Scale (CPS) and about their views on patient participation using closed- and open-ended questions. Almost all participants preferred treatment decisions to be the outcome of a shared process. Clinicians viewed participation more often as reaching an agreement, whereas 23% of patients defined participation exclusively as being informed. Of the participants, ⩾81% thought not all patients are able to participate and ⩾74% thought clinicians are not always able to weigh the pros and cons of treatment for patients, especially not quality as compared with length of life. Clinicians seemed reluctant to share probability information on the likely impact of adjuvant treatment. Clinicians should acknowledge the legitimacy of patients' values in treatment decisions. Guidelines should recommend elicitation of patient values at specific decision points. PMID:18781148

Insights from Smart Meters. Ramp-up, dependability, and short-term persistence of savings from Home Energy Reports

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

Todd, Annika; Perry, Michael; Smith, Brian

Smart meters, smart thermostats, and other new technologies provide previously unavailable high-frequency and location-specific energy usage data. Many utilities are now able to capture real-time, customer specific hourly interval usage data for a large proportion of their residential and small commercial customers. These vast, constantly growing streams of rich data (or, “big data”) have the potential to provide novel insights into key policy questions about how people make energy decisions. The richness and granularity of these data enable many types of creative and cutting-edge analytics. Technically sophisticated and rigorous statistical techniques can be used to pull useful insights out ofmore » this high-frequency, human-focused data. In this series, we call this “behavior analytics.” This kind of analytics has the potential to provide tremendous value to a wide range of energy programs. For example, disaggregated and heterogeneous information about actual energy use allows energy efficiency (EE) and/or demand response (DR) program implementers to target specific programs to specific households; enables evaluation, measurement and verification (EM&V) of energy efficiency programs to be performed on a much shorter time horizon than was previously possible; and may provide better insights into the energy and peak hour savings associated with EE and DR programs (e.g., behavior-based (BB) programs). The goal of this series is to enable evidence-based and data-driven decision making by policy makers and industry stakeholders, including program planners, program administrators, utilities, state regulatory agencies, and evaluators. We focus on research findings that are immediately relevant.« less

Neuroscientific Model of Motivational Process

PubMed Central

Kim, Sung-il

2013-01-01

Considering the neuroscientific findings on reward, learning, value, decision-making, and cognitive control, motivation can be parsed into three sub processes, a process of generating motivation, a process of maintaining motivation, and a process of regulating motivation. I propose a tentative neuroscientific model of motivational processes which consists of three distinct but continuous sub processes, namely reward-driven approach, value-based decision-making, and goal-directed control. Reward-driven approach is the process in which motivation is generated by reward anticipation and selective approach behaviors toward reward. This process recruits the ventral striatum (reward area) in which basic stimulus-action association is formed, and is classified as an automatic motivation to which relatively less attention is assigned. By contrast, value-based decision-making is the process of evaluating various outcomes of actions, learning through positive prediction error, and calculating the value continuously. The striatum and the orbitofrontal cortex (valuation area) play crucial roles in sustaining motivation. Lastly, the goal-directed control is the process of regulating motivation through cognitive control to achieve goals. This consciously controlled motivation is associated with higher-level cognitive functions such as planning, retaining the goal, monitoring the performance, and regulating action. The anterior cingulate cortex (attention area) and the dorsolateral prefrontal cortex (cognitive control area) are the main neural circuits related to regulation of motivation. These three sub processes interact with each other by sending reward prediction error signals through dopaminergic pathway from the striatum and to the prefrontal cortex. The neuroscientific model of motivational process suggests several educational implications with regard to the generation, maintenance, and regulation of motivation to learn in the learning environment. PMID:23459598

Neuroscientific model of motivational process.

PubMed

Kim, Sung-Il

2013-01-01

Considering the neuroscientific findings on reward, learning, value, decision-making, and cognitive control, motivation can be parsed into three sub processes, a process of generating motivation, a process of maintaining motivation, and a process of regulating motivation. I propose a tentative neuroscientific model of motivational processes which consists of three distinct but continuous sub processes, namely reward-driven approach, value-based decision-making, and goal-directed control. Reward-driven approach is the process in which motivation is generated by reward anticipation and selective approach behaviors toward reward. This process recruits the ventral striatum (reward area) in which basic stimulus-action association is formed, and is classified as an automatic motivation to which relatively less attention is assigned. By contrast, value-based decision-making is the process of evaluating various outcomes of actions, learning through positive prediction error, and calculating the value continuously. The striatum and the orbitofrontal cortex (valuation area) play crucial roles in sustaining motivation. Lastly, the goal-directed control is the process of regulating motivation through cognitive control to achieve goals. This consciously controlled motivation is associated with higher-level cognitive functions such as planning, retaining the goal, monitoring the performance, and regulating action. The anterior cingulate cortex (attention area) and the dorsolateral prefrontal cortex (cognitive control area) are the main neural circuits related to regulation of motivation. These three sub processes interact with each other by sending reward prediction error signals through dopaminergic pathway from the striatum and to the prefrontal cortex. The neuroscientific model of motivational process suggests several educational implications with regard to the generation, maintenance, and regulation of motivation to learn in the learning environment.