Sample records for overbooking
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Holtshousen, W S J; Coetzee, E
2012-09-01
An analysis of annual reports revealed that on average 20% of patient appointments with oral hygienists in the Department of Health in the Pretoria region were not utilised due to patient noncompliance (i.e. broken appointments). Many solutions have been considered to address the high rate of noncompliance and the resulting idle chair capacity. One solution selected to overcome some of the negative consequences of broken appointments was deliberate overbooking. The aim of our study was to determine the effect of overbooking on idle dental chair capacity by measuring the utilisation rate over a three month period (July to September) after 25% overbooking was introduced in the Pretoria region. A statistical analysis was conducted on our results to determine an overbooking rate that would ensure full utilisation of the available dental chair capacity. The available time units over the three month study period amounted to 1365, allocated to 1427 patients resulting in an overal overbooking rate of 4.54%. The overall utilisation rate was found to be 79.2%. The calculated regression line estimated that there would be full utilisation of dental chair capacity at an overbooking rate of 26.7%. Overbooking at the levels applied in this study had a minimal overall effect on idle dental chair capacity. Our results confirm the need for careful planning and management in addressing noncompliance. In a manner similar to the clinical situation, organisational development requires a correct diagnosis in order that an appropriate and effective intervention may be designed.
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Preventing patient absenteeism: validation of a predictive overbooking model.
Reid, Mark W; Cohen, Samuel; Wang, Hank; Kaung, Aung; Patel, Anish; Tashjian, Vartan; Williams, Demetrius L; Martinez, Bibiana; Spiegel, Brennan M R
2015-12-01
To develop a model that identifies patients at high risk for missing scheduled appointments ("no-shows" and cancellations) and to project the impact of predictive overbooking in a gastrointestinal endoscopy clinic-an exemplar resource-intensive environment with a high no-show rate. We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic. We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical "1 patient, 1 slot" scheduling. Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = -5.67; 95% CI, -6.48 to -4.87; P < .0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows. Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.
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Hanauer, D.A.
2014-01-01
Summary Background Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. Objective To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. Methods Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. Results From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. Conclusions This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient’s show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity. PMID:25298821
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Using overbooking to manage no-shows in an Italian healthcare center.
Parente, Chiara Anna; Salvatore, Domenico; Gallo, Giampiero Maria; Cipollini, Fabrizio
2018-03-15
In almost all healthcare systems, no-shows (scheduled appointments missed without any notice from patients) have a negative impact on waiting lists, costs and resource utilization, impairing the quality and quantity of cares that could be provided, as well as the revenues from the corresponding activity. Overbooking is a tool healthcare providers can resort to reduce the impact of no-shows. We develop an overbooking algorithm, and we assess its effectiveness using two methods: an analysis of the data coming from a practical implementation in an healthcare center; a simulation experiment to check the robustness and the potential of the strategy under different conditions. The data of the study, which includes personal and administrative information of patients, together with their scheduled and attended examinations, was taken from the electronic database of a big outpatient center. The attention was focused on the Magnetic Resonance (MR) ward because it uses expensive equipment, its services need long execution times, and the center has actually used it to implement an overbooking strategy aimed at reducing the impact of no-shows. We propose a statistical model for the patient's show/no-show behavior and we evaluate the ensuing overbooking procedure implemented in the MR ward. Finally, a simulation study investigates the effects of the overbooking strategy under different scenarios. The first contribution is a list of variables to identify the factors performing the best to predict no-shows. We classified the variables in three groups: "Patient's intrinsic factors", "Exogenous factors" and "Factors associated with the examination". The second contribution is a predictive model of no-shows, which is estimated on context-specific data using the variables just discussed. Such a model represents a fundamental ingredient of the overbooking strategy we propose to reduce the negative effects of no-shows. The third contribution is the assessment of that strategy by means of a simulation study under different scenarios in terms of number of resources and no-show rates. The same overbooking strategy was also implemented in practice (giving the opportunity to consider it as a quasi-experiment) to reduce the negative impact caused by non attendance in the MR ward. Both the quasi-experiment and the simulation study demonstrated that the strategy improved the center's productivity and reduced idle time of resources, although it increased slightly the patient's waiting time and the staff's overtime. This represents an evidence that overbooking can be suitable to improve the management of healthcare centers without adversely affecting their costs and the quality of cares offered. We shown that a well designed overbooking procedure can improve the management of medical centers, in terms of a significant increase of revenue, while keeping patient's waiting time and overtime under control. This was demonstrated by the results of a quasi-experiment (practical implementation of the strategy in the MR ward) and a simulation study (under different scenarios). Such positive results took advantage from a predictive model of no-show carefully designed around the medical center data.
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Estimating the cost of no-shows and evaluating the effects of mitigation strategies.
Berg, Bjorn P; Murr, Michael; Chermak, David; Woodall, Jonathan; Pignone, Michael; Sandler, Robert S; Denton, Brian T
2013-11-01
To measure the cost of nonattendance ("no-shows") and benefit of overbooking and interventions to reduce no-shows for an outpatient endoscopy suite. We used a discrete-event simulation model to determine improved overbooking scheduling policies and examine the effect of no-shows on procedure utilization and expected net gain, defined as the difference in expected revenue based on Centers for Medicare & Medicaid Services reimbursement rates and variable costs based on the sum of patient waiting time and provider and staff overtime. No-show rates were estimated from historical attendance (18% on average, with a sensitivity range of 12%-24%). We then evaluated the effectiveness of scheduling additional patients and the effect of no-show reduction interventions on the expected net gain. The base schedule booked 24 patients per day. The daily expected net gain with perfect attendance is $4433.32. The daily loss attributed to the base case no-show rate of 18% is $725.42 (16.4% of net gain), ranging from $472.14 to $1019.29 (10.7%-23.0% of net gain). Implementing no-show interventions reduced net loss by $166.61 to $463.09 (3.8%-10.5% of net gain). The overbooking policy of 9 additional patients per day resulted in no loss in expected net gain when compared with the reference scenario. No-shows can significantly decrease the expected net gain of outpatient procedure centers. Overbooking can help mitigate the impact of no-shows on a suite's expected net gain and has a lower expected cost of implementation to the provider than intervention strategies.
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Tuli, Sanjeev Y; Thompson, Lindsay A; Ryan, Kathleen A; Srinivas, Ganga L; Fillipps, Donald J; Young, Christopher M; Tuli, Sonal S
2010-06-01
To evaluate the impact of advanced access scheduling in a pediatric residency clinic on resident and patient satisfaction, medical education, practice quality, and efficiency. Residents were assigned to either the advanced access template (10 appointments available to patients and 2 physician overbooks) or the prior template (5 available and 8 overbooks). Outcomes included resident and patient satisfaction, appointment availability, and continuity of care and clinic costs. Patient satisfaction improved in 7 areas (P < .001). Residents in either template did not report an impact on medical education experiences. Significant increases were realized with appointment availability and the number of patients seen. Continuity also increased as the overflow/acute visits decreased (P < .001). Overall costs per visit decreased 22%. Because of the significant improvements in access, continuity, and efficiency, all residents were switched to the advanced access template after completion of the study. Improvement in access to the primary physician has a significant impact on patient satisfaction with health care delivery. This model optimizes the limited time that residents have in continuity clinic, and it has implications for health care delivery quality improvement.
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14 CFR 250.11 - Public disclosure of deliberate overbooking and boarding procedures.
Code of Federal Regulations, 2010 CFR
2010-01-01
... agent employed by such air carrier or foreign air carrier to sell tickets to passengers, a sign located..., although other consumer protections may be available. Check with your airline or your travel agent. (b... shall be printed in a type face contrasting with that of the rest of the notice. (c) It shall be the...
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Pandit, Jaideep J; Tavare, Aniket
2011-07-01
It is important that a surgical list is planned to utilise as much of the scheduled time as possible while not over-running, because this can lead to cancellation of operations. We wished to assess whether, theoretically, the known duration of individual operations could be used quantitatively to predict the likely duration of the operating list. In a university hospital setting, we first assessed the extent to which the current ad-hoc method of operating list planning was able to match the scheduled operating list times for 153 consecutive historical lists. Using receiver operating curve analysis, we assessed the ability of an alternative method to predict operating list duration for the same operating lists. This method uses a simple formula: the sum of individual operation times and a pooled standard deviation of these times. We used the operating list duration estimated from this formula to generate a probability that the operating list would finish within its scheduled time. Finally, we applied the simple formula prospectively to 150 operating lists, 'shadowing' the current ad-hoc method, to confirm the predictive ability of the formula. The ad-hoc method was very poor at planning: 50% of historical operating lists were under-booked and 37% over-booked. In contrast, the simple formula predicted the correct outcome (under-run or over-run) for 76% of these operating lists. The calculated probability that a planned series of operations will over-run or under-run was found useful in developing an algorithm to adjust the planned cases optimally. In the prospective series, 65% of operating lists were over-booked and 10% were under-booked. The formula predicted the correct outcome for 84% of operating lists. A simple quantitative method of estimating operating list duration for a series of operations leads to an algorithm (readily created on an Excel spreadsheet, http://links.lww.com/EJA/A19) that can potentially improve operating list planning.
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Improving Health Care Accessibility: Strategies and Recommendations.
Almorsy, Lamia; Khalifa, Mohamed
2016-01-01
Access time refers to the interval between requesting and actual outpatient appointment. It reflects healthcare accessibility and has a great influence on patient treatment and satisfaction. King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia studied the accessibility to outpatient services in order to develop useful strategies and recommendations for improvement. Utilized, unutilized and no-show appointments were analyzed. It is crucial to manage no-shows and short notice appointment cancellations by preparing a waiting list for those patients who can be called in to an appointment on the same day using an open access policy. An overlapping appointment scheduling model can be useful to minimize patient waiting time and doctor idle time in addition to the sensible use of appointment overbooking that can significantly improve productivity.
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Considerations for Using an Incremental Scheduler for Human Exploration Task Scheduling
NASA Technical Reports Server (NTRS)
Jaap, John; Phillips, Shaun
2005-01-01
As humankind embarks on longer space missions farther from home, the requirements and environments for scheduling the activities performed on these missions are changing. As we begin to prepare for these missions it is appropriate to evaluate the merits and applicability of the different types of scheduling engines. Scheduling engines temporally arrange tasks onto a timeline so that all constraints and objectives are met and resources are not overbooked. Scheduling engines used to schedule space missions fall into three general categories: batch, mixed-initiative, and incremental. This paper presents an assessment of the engine types, a discussion of the impact of human exploration of the moon and Mars on planning and scheduling, and the applicability of the different types of scheduling engines. This paper will pursue the hypothesis that incremental scheduling engines may have a place in the new environment; they have the potential to reduce cost, to improve the satisfaction of those who execute or benefit from a particular timeline (the customers), and to allow astronauts to plan their own tasks.
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The Scatter Search Based Algorithm to Revenue Management Problem in Broadcasting Companies
NASA Astrophysics Data System (ADS)
Pishdad, Arezoo; Sharifyazdi, Mehdi; Karimpour, Reza
2009-09-01
The problem under question in this paper which is faced by broadcasting companies is how to benefit from a limited advertising space. This problem is due to the stochastic behavior of customers (advertiser) in different fare classes. To address this issue we propose a mathematical constrained nonlinear multi period model which incorporates cancellation and overbooking. The objective function is to maximize the total expected revenue and our numerical method performs it by determining the sales limits for each class of customer to present the revenue management control policy. Scheduling the advertising spots in breaks is another area of concern and we consider it as a constraint in our model. In this paper an algorithm based on Scatter search is developed to acquire a good feasible solution. This method uses simulation over customer arrival and in a continuous finite time horizon [0, T]. Several sensitivity analyses are conducted in computational result for depicting the effectiveness of proposed method. It also provides insight into better results of considering revenue management (control policy) compared to "no sales limit" policy in which sooner demand will served first.
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A Risk Management Method for the Operation of a Supply-Chain without Storage:
NASA Astrophysics Data System (ADS)
Kobayashi, Yasuhiro; Manabe, Yuuji; Nakata, Norimasa; Kusaka, Satoshi
A business risk management method has been developed for a supply-chain without a storage function under demand uncertainty. Power supply players in the deregulated power market face the need to develop the best policies for power supply from self-production and reserved purchases to balance demand, which is predictable with error. The proposed method maximizes profit from the operation of the supply-chain under probabilistic demand uncertainty on the basis of a probabilistic programming approach. Piece-wise linear functions are employed to formulate the impact of under-booked or over-booked purchases on the supply cost, and constraints on over-demand probability are introduced to limit over-demand frequency on the basis of the demand probability distribution. The developed method has been experimentally applied to the supply policy of a power-supply-chain, the operation of which is based on a 3-stage pricing purchase contract and on 28 time zones. The characteristics of the obtained optimal supply policy are successfully captured in the numerical results, which suggest the applicability of the proposed method.
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Incremental Scheduling Engines for Human Exploration of the Cosmos
NASA Technical Reports Server (NTRS)
Jaap, John; Phillips, Shaun
2005-01-01
As humankind embarks on longer space missions farther from home, the requirements and environments for scheduling the activities performed on these missions are changing. As we begin to prepare for these missions it is appropriate to evaluate the merits and applicability of the different types of scheduling engines. Scheduling engines temporally arrange tasks onto a timeline so that all constraints and objectives are met and resources are not overbooked. Scheduling engines used to schedule space missions fall into three general categories: batch, mixed-initiative, and incremental. This paper presents an assessment of the engine types, a discussion of the impact of human exploration of the moon and Mars on planning and scheduling, and the applicability of the different types of scheduling engines. This paper will pursue the hypothesis that incremental scheduling engines may have a place in the new environment; they have the potential to reduce cost, to improve the satisfaction of those who execute or benefit from a particular timeline (the customers), and to allow astronauts to plan their own tasks and those of their companion robots.
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Incremental Scheduling Engines: Cost Savings through Automation
NASA Technical Reports Server (NTRS)
Jaap, John; Phillips, Shaun
2005-01-01
As humankind embarks on longer space missions farther from home, the requirements and environments for scheduling the activities performed on these missions are changing. As we begin to prepare for these missions it is appropriate to evaluate the merits and applicability of the different types of scheduling engines. Scheduling engines temporally arrange tasks onto a timeline so that all constraints and ob.jectives are met and resources are not over-booked. Scheduling engines used to schedule space missions fall into three general categories: batch, mixed-initiative, and incremental. This paper, presents an assessment of the engine types, a discussion of the impact of human exploration of the moon and Mars on planning and scheduling, and the applicability of the different types of scheduling engines. This paper will pursue the hypothesis that incremental scheduling engines may have a place in the new environment; they have the potential to reduce cost, to improve the satisfaction of those who execute or benefit from a particular timeline (the customers), and to allow astronauts to plan their own tasks and those of their companion robots.
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Dynamic Scheduling for Veterans Health Administration Patients using Geospatial Dynamic Overbooking.
Adams, Stephen; Scherer, William T; White, K Preston; Payne, Jason; Hernandez, Oved; Gerber, Mathew S; Whitehead, N Peter
2017-10-12
The Veterans Health Administration (VHA) is plagued by abnormally high no-show and cancellation rates that reduce the productivity and efficiency of its medical outpatient clinics. We address this issue by developing a dynamic scheduling system that utilizes mobile computing via geo-location data to estimate the likelihood of a patient arriving on time for a scheduled appointment. These likelihoods are used to update the clinic's schedule in real time. When a patient's arrival probability falls below a given threshold, the patient's appointment is canceled. This appointment is immediately reassigned to another patient drawn from a pool of patients who are actively seeking an appointment. The replacement patients are prioritized using their arrival probability. Real-world data were not available for this study, so synthetic patient data were generated to test the feasibility of the design. The method for predicting the arrival probability was verified on a real set of taxicab data. This study demonstrates that dynamic scheduling using geo-location data can reduce the number of unused appointments with minimal risk of double booking resulting from incorrect predictions. We acknowledge that there could be privacy concerns with regards to government possession of one's location and offer strategies for alleviating these concerns in our conclusion.
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Barriers and facilitators of surgical care in rural Uganda: a mixed methods study.
Nwanna-Nzewunwa, Obieze C; Ajiko, Mary-Margaret; Kirya, Fred; Epodoi, Joseph; Kabagenyi, Fiona; Batibwe, Emmanuel; Feldhaus, Isabelle; Juillard, Catherine; Dicker, Rochelle
2016-07-01
Surgical care delivery is poorly understood in resource-limited settings. To effectively move toward universal health coverage, there is a critical need to understand surgical care delivery in developing countries. This study aims to identify the barriers and facilitators of surgical care delivery at Soroti Regional Referral Hospital in Uganda. In this mixed methods study, we (1) applied the Surgeons OverSeas' Personnel, Infrastructure, Procedures, Equipment, and Supplies tool to assess surgical capacity; (2) retrospectively reviewed inpatient records; (3) conducted four semistructured focus group discussions with 18 purposively sampled providers involved in perioperative care; and (4) observed the perioperative process of care using a time and motion approach. Descriptive statistics were generated from quantitative data. Qualitative data were thematically analyzed. The Personnel, Infrastructure, Procedures, Equipment, and Supplies survey revealed severe deficiencies in workforce (P-score = 14) and infrastructure (I-score = 5). Equipment, supplies, and procedures were generally available. Male and female wards were overbooked 83% and 60% of the time, respectively. Providers identified lack of space, patient overload, and superfluous patients' attendants as barriers to surgical care. Workforce challenges were tackled using teamwork and task sharing. Inadequate equipment and processes were addressed using improvisations. All observed subjects (n = 31) received interventions. The median decision-to-intervention time was 2.5 h (Interquartile Range [IQR], 0.4, 21.4). However, 48% of subjects experienced delays. Median decision-to-intervention delay was 14.8 h (IQR, 0.9, 26.6). Despite severe workforce and physical infrastructural deficiencies at Soroti Regional Referral Hospital, providers are adjusting and innovating to deliver surgical care. Copyright © 2016 Elsevier Inc. All rights reserved.
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Improving patient flow at a family health clinic.
Bard, Jonathan F; Shu, Zhichao; Morrice, Douglas J; Wang, Dongyang Ester; Poursani, Ramin; Leykum, Luci
2016-06-01
This paper presents an analysis of a residency primary care clinic whose majority of patients are underserved. The clinic is operated by the health system for Bexar County and staffed primarily with physicians in a three-year Family Medicine residency program at The University of Texas School of Medicine in San Antonio. The objective of the study was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals. The first steps in developing the model were to map the administrative and diagnostic processes and to collect time-stamped data and fit probability distributions to each. In conjunction with the initialization and validation steps, various regressions were performed to determine if any relationships existed between individual providers and patient types, length of stay, and the difference between discharge time and appointment time. The latter two statistics along with resource utilization and closing time were the primary metrics used to evaluate system performance.The results showed that up to an 8.5 % reduction in patient length of stay is achievable without noticeably affecting the other metrics by carefully adjusting appointment times. Reducing the no-show rate from its current value of 21.8 % or overbooking, however, is likely to overwhelm the system's resources and lead to excessive congestion and overtime. Another major finding was that the providers are the limiting factor in improving patient flow. With an average utilization rate above 90 % there is little prospect in shortening the total patient time in the clinic without reducing the providers' average assessment time. Finally, several suggestions are offered to ensure fairness when dealing with out-of-order arrivals.