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Open accessJournal ArticleDOI: 10.1080/01605682.2019.1700186

A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach

04 Mar 2021-Journal of the Operational Research Society (Taylor & Francis)-Vol. 72, Iss: 3, pp 485-500
Abstract: The increasing pressures on the healthcare system in the UK are well documented. The solution lies in making best use of existing resources (e.g. beds), as additional funding is not available. Incr...

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Topics: Decision support system (63%), Resource allocation (62%), Management information systems (57%) ... show more
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6 results found


Open accessJournal ArticleDOI: 10.1155/2020/8857553
Abstract: Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure.

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7 Citations


Open accessJournal ArticleDOI: 10.5267/J.JPM.2020.8.002
01 Aug 2020-
Abstract: Emergency department (ED) overcrowding is a common issue in emergency medicine of Canada Previous studies indicate that adding a physician in triage (PIT) can increase accuracy and efficiency in the initial process of patient evaluations However, the PIT concept should be thoroughly researched before its widespread implementation can be recommended This paper introduces the evaluation of impact of PIT on ED patient wait times and length of stay (LOS) using simulation modeling A discrete-event simulation model of ED is built to simulate and predict the effect of PIT intervention The model performance is validated using current-state ED flow metrics to quantitatively test multiple alternatives for ED improvements Results show that the PIT implementation can reduce the ED patient LOS by an average of 34% and Waiting to be Seen time by 49% across all scenarios studied The proposed method can be applied to improve the operation efficiency of healthcare systems in the current pandemic, COVID-19 (C) 2020 by the authors;licensee Growing Science, Canada

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Topics: Triage (57%)

3 Citations


Journal ArticleDOI: 10.1002/HPM.3118
Reda M. Lebcir1, Rifat Atun2Institutions (2)
Abstract: © 2021 John Wiley & Sons Ltd. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1002/hpm.3118

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1 Citations


Open accessJournal ArticleDOI: 10.32732/JMO.2020.12.2.100
15 Dec 2020-
Abstract: Overcrowding is a common problem in hospital emergency departments (EDs) where the ED service cannot meet care demands within reasonable time frames. This paper introduces a quantitative approach using computer simulation modeling for hospital decision makers to explore trade-offs between efficiency, workload and capacity of EDs. A computer simulation model is built based on the ED of a local hospital to improvement efficiency of the ED patient flow. Bottlenecks of the emergency care process are detected using the built model. The ED performance is examined by applying alternative strategies to reduce patient waiting time and length of stay. The proposed method can be applied to improve the operation efficiency of healthcare systems in the current pandemic, COVID -19.

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Topics: Overcrowding (53%)

1 Citations


Journal ArticleDOI: 10.1016/J.SIMPAT.2021.102420
Abstract: A multi-level framework that combines a simulation and optimization approach is proposed to evaluate the performance of complex systems triggered by real-time IoT-fed “service requests”. The idea behind the conceptual framework is centered on the use of simulation to mimic system organization, rules and behavior, whereas optimization is used to search for the allocation of both personnel and equipment to pursue optimality with respect to resource availability. Focusing on a healthcare assistance network for the elder, the framework is tailored to determine the staff requirements for home assistance of local elderly populations based on different priorities of service requests and geographical areas covered by the service. The framework is verified by using NEMSIS (National Emergency Medical Services Information System) data, i.e. the national database that is used to store emergency medical services data from the U.S. States and Territories. Ad hoc scenarios are analyzed and evaluated in an illustrative use case. Numerical results show how, upon the arrival flow of non-deterministic patient calls, the framework allows to verify the goodness of strategic, tactical and operational decisions of healthy living for the elder.

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References
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27 results found


Journal ArticleDOI: 10.1016/J.IJFORECAST.2006.03.001
Rob J. Hyndman1, Anne B. Koehler2Institutions (2)
Abstract: We discuss and compare measures of accuracy of univariate time series forecasts. The methods used in the M-competition as well as the M3-competition, and many of the measures recommended by previous authors on this topic, are found to be degenerate in commonly occurring situations. Instead, we propose that the mean absolute scaled error become the standard measure for comparing forecast accuracy across multiple time series.

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Topics: Forecast verification (60%), Forecast skill (57%), Univariate (53%)

3,116 Citations


Journal ArticleDOI: 10.1016/J.EJOR.2008.10.025
Mohamed A. Ahmed1, Talal M. Alkhamis1Institutions (1)
Abstract: This paper integrates simulation with optimization to design a decision support tool for the operation of an emergency department unit at a governmental hospital in Kuwait. The hospital provides a set of services for different categories of patients. We present a methodology that uses system simulation combined with optimization to determine the optimal number of doctors, lab technicians and nurses required to maximize patient throughput and to reduce patient time in the system subject to budget restrictions. The major objective of this decision supporting tool is to evaluate the impact of various staffing levels on service efficiency. Experimental results show that by using current hospital resources, the optimization simulation model generates optimal staffing allocation that would allow 28% increase in patient throughput and an average of 40% reduction in patients’ waiting time.

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Topics: Staffing (56%), Decision support system (50%)

296 Citations


Journal ArticleDOI: 10.1057/PALGRAVE.JORS.2601278
Paul Robert Harper1, A. K. Shahani1Institutions (1)
Abstract: The internal dynamics of a hospital represent a complex non-linear structure. Planning and management of bed capacities must be evaluated within an environment of uncertainty, variability and limited resources. A common approach is to plan and manage capacities based on simple deterministic spreadsheet calculations. This paper demonstrates that these calculations typically do not provide the appropriate information and result in underestimating true bed requirements. More sophisticated, flexible and necessarily detailed capacity models are needed. The development and use of such a simulation model is presented in this paper. The modelling work, in conjunction with a major UK NHS Trust, considers various types of patient flows, at the individual patient level, and resulting bed needs over time. The consequence of changes in capacity planning policies and management of existing capacities can be readily examined. The work has highlighted the need for evaluating hospital bed capacities in light of both bed occupancies and refused admission rates. The relationship between occupancy and refusals is complex and often overlooked by hospital managers.

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Topics: Hospital bed (59%), Capacity planning (53%)

291 Citations


Journal ArticleDOI: 10.1016/J.IJPE.2008.11.021
Abstract: The purpose of this paper is to propose and compare several optimization methods for elective surgery planning when operating room (OR) capacity is shared by elective and emergency surgery. The planning problem is considered as a stochastic optimization problem in order to minimize expected overtime costs and patients’ related costs. An “almost” exact method combining Monte Carlo simulation and mixed integer programming is presented, and its convergence properties are investigated. Several heuristic and meta-heuristic methods are then proposed. Numerical experimentations are conducted to compare the performance of different optimization methods.

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Topics: Stochastic optimization (66%), Global optimization (61%), Stochastic programming (59%) ... show more

138 Citations


Journal ArticleDOI: 10.1016/J.EJOR.2007.10.029
Abstract: This paper describes a detailed simulation model for healthcare planning in a medical assessment unit (MAU) of a general hospital belonging to the national health service (NHS), UK. The MAU is established to improve the quality of care given to acute medical patients on admission, and to provide the organisational means of rapid assessment and investigation in order to avoid unnecessary admissions. The simulation model enables different scenarios to be tested to eliminate bottlenecks in order to achieve optimal clinical workflow. The link between goal programming (GP) and simulation for efficient resource planning is explored. A GP model is developed for trade-off analysis of the results obtained from the simulation. The implications of MAU management preferences to various objectives are presented.

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Topics: Goal programming (54%)

100 Citations


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