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Elalouf A, Wachtel G. Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies. OPERATIONS RESEARCH FORUM 2022. [PMCID: PMC8716576 DOI: 10.1007/s43069-021-00114-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Problems related to patient scheduling and queueing in emergency departments are gaining increasing attention in theory, in the fields of operations research and emergency and healthcare services, and in practice. This paper aims to provide an extensive review of studies addressing queueing-related problems explicitly related to emergency departments. We have reviewed 229 articles and books spanning seven decades and have sought to organize the information they contain in a manner that is accessible and useful to researchers seeking to gain knowledge on specific aspects of such problems. We begin by presenting a historical overview of applications of queueing theory to healthcare-related problems. We subsequently elaborate on managerial approaches used to enhance efficiency in emergency departments. These approaches include bed management, fast-track, dynamic resource allocation, grouping/prioritization of patients, and triage approaches. Finally, we discuss scientific methodologies used to analyze and optimize these approaches: algorithms, priority models, queueing models, simulation, and statistical approaches.
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Lamé G, Crowe S, Barclay M. "What's the evidence?"-Towards more empirical evaluations of the impact of OR interventions in healthcare. Health Syst (Basingstoke) 2020; 11:59-67. [PMID: 35127059 PMCID: PMC8812794 DOI: 10.1080/20476965.2020.1857663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 11/24/2020] [Indexed: 11/20/2022] Open
Abstract
Despite an increasing number of papers reporting applications of operational research (OR) to problems in healthcare, there remains little empirical evidence of OR improving healthcare delivery in practice. Without such evidence it is harder both to justify the usefulness of OR to a healthcare audience and to learn and continuously improve our approaches. To progress, we need to build the evidence-base on whether and how OR improves healthcare delivery through careful empirical evaluation. This position paper reviews evaluation standards in healthcare improvement research and dispels some common myths about evaluation. It highlights the current lack of robust evaluation of healthcare OR and makes the case for addressing this. It then proposes possible ways for building better empirical evaluations of OR interventions in healthcare.
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Affiliation(s)
- Guillaume Lamé
- The Healthcare Improvement Studies Institute (THIS Institute), University of Cambridge, Cambridge, UK
- Laboratoire Génie Industriel, Université Paris-Saclay, CentraleSupélec, Gif-sur-Yvette, France
| | - Sonya Crowe
- Clinical Operational Research Unit, University College London, London, UK
| | - Matthew Barclay
- The Healthcare Improvement Studies Institute (THIS Institute), University of Cambridge, Cambridge, UK
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Bouzon Nagem Assad D, Spiegel T. Improving emergency department resource planning: a multiple case study. Health Syst (Basingstoke) 2019; 9:2-30. [DOI: 10.1080/20476965.2019.1680260] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 10/10/2019] [Indexed: 10/25/2022] Open
Affiliation(s)
- Daniel Bouzon Nagem Assad
- Departamento de Engenharia Industrial, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
- Department of Organization Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, Madrid, Spain
| | - Thaís Spiegel
- Departamento de Engenharia Industrial, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
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Crema M, Verbano C. Simulation modelling and lean management in healthcare: first evidences and research agenda. TOTAL QUALITY MANAGEMENT & BUSINESS EXCELLENCE 2019. [DOI: 10.1080/14783363.2019.1572504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Maria Crema
- Department of Management and Engineering, University of Padova, Vicenza, Italy
| | - Chiara Verbano
- Department of Management and Engineering, University of Padova, Vicenza, Italy
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Long KM, Meadows GN. Simulation Modeling for Psychiatric Service Planning: Protocol for a Mixed-Methods Study. JMIR Res Protoc 2018; 7:e11119. [PMID: 30470679 PMCID: PMC6301811 DOI: 10.2196/11119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 08/16/2018] [Accepted: 08/17/2018] [Indexed: 11/15/2022] Open
Abstract
Background Mental health service managers must take into account multiple factors when making decisions about the best way to deliver care to clients across increasingly larger service areas. This task is made more difficult by the lack of evidence and tools historically available to inform these decisions. In recent decades, the increasing availability of epidemiological and service use data for mental illness has solved the problem of evidence, but there still exists a challenge to make these data easily accessible and understandable for managers. Objective This study aims to develop a simulation modeling tool to allow managers to explore various service configurations in virtual reality, enabling predictions to be made about the cost and quality of care. Methods This is a longitudinal, mixed-methods case study, comprising overlapping intervention and evaluation phases. In partnership with senior managers of a mental health program, the researchers will develop a series of simulation models in Arena to address key strategic issues facing the service. Thematic and content analyses of semistructured interviews, meeting observations, and document analysis will be used to evaluate the process of model implementation and the outcomes for both researchers and managers. The study is being conducted in Australia. Results Data collection has been ongoing since late 2013. To date, 3 prototype simulation models have been developed and presented to senior managers, and 18 evaluation interviews have been conducted. The project is expected to conclude in late 2018. Conclusions Findings of this study have the potential to shape decision making in mental health service delivery, by providing key examples of how to integrate patient data using simulation modeling. In addition, the results will provide key insights into how researchers and consultants can effectively implement simulation modeling in real-world health care organizations. International Registered Report Identifier (IRRID) RR1-10.2196/11119
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Affiliation(s)
- Katrina M Long
- Southern Synergy, Department of Psychiatry, Monash University, Dandenong, Australia
| | - Graham N Meadows
- Southern Synergy, Department of Psychiatry, Monash University, Dandenong, Australia
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Monash Health, Melbourne, Australia
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Kasaie P, David Kelton W, Ancona RM, Ward MJ, Froehle CM, Lyons MS. Lessons Learned From the Development and Parameterization of a Computer Simulation Model to Evaluate Task Modification for Health Care Providers. Acad Emerg Med 2018; 25:238-249. [PMID: 28925587 DOI: 10.1111/acem.13314] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 09/05/2017] [Accepted: 09/06/2017] [Indexed: 11/30/2022]
Abstract
Computer simulation is a highly advantageous method for understanding and improving health care operations with a wide variety of possible applications. Most computer simulation studies in emergency medicine have sought to improve allocation of resources to meet demand or to assess the impact of hospital and other system policies on emergency department (ED) throughput. These models have enabled essential discoveries that can be used to improve the general structure and functioning of EDs. Theoretically, computer simulation could also be used to examine the impact of adding or modifying specific provider tasks. Doing so involves a number of unique considerations, particularly in the complex environment of acute care settings. In this paper, we describe conceptual advances and lessons learned during the design, parameterization, and validation of a computer simulation model constructed to evaluate changes in ED provider activity. We illustrate these concepts using examples from a study focused on the operational effects of HIV screening implementation in the ED. Presentation of our experience should emphasize the potential for application of computer simulation to study changes in health care provider activity and facilitate the progress of future investigators in this field.
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Affiliation(s)
- Parastu Kasaie
- Bloomberg School of Public Health; Department of Health, Behavior and Society; Johns Hopkins University; Baltimore MD
| | - W. David Kelton
- Department of Operations; Business Analytics & Information Systems; Carl H. Lindner College of Business; University of Cincinnati; Cincinnati OH
| | - Rachel M. Ancona
- Department of Emergency Medicine; College of Medicine; University of Cincinnati; Cincinnati OH
| | - Michael J. Ward
- Department of Emergency Medicine; Vanderbilt University Medical Center; Nashville TN
| | - Craig M. Froehle
- Department of Operations; Business Analytics & Information Systems; Carl H. Lindner College of Business; University of Cincinnati; Cincinnati OH
- Department of Emergency Medicine; College of Medicine; University of Cincinnati; Cincinnati OH
| | - Michael S. Lyons
- Department of Emergency Medicine; College of Medicine; University of Cincinnati; Cincinnati OH
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Rapid diagnoses at the breast center of Jeroen Bosch Hospital: a case study invoking queueing theory and discrete event simulation. Health Syst (Basingstoke) 2017. [DOI: 10.1057/s41306-016-0013-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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Salleh S, Thokala P, Brennan A, Hughes R, Booth A. Simulation Modelling in Healthcare: An Umbrella Review of Systematic Literature Reviews. PHARMACOECONOMICS 2017; 35:937-949. [PMID: 28560492 DOI: 10.1007/s40273-017-0523-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND Numerous studies examine simulation modelling in healthcare. These studies present a bewildering array of simulation techniques and applications, making it challenging to characterise the literature. OBJECTIVE The aim of this paper is to provide an overview of the level of activity of simulation modelling in healthcare and the key themes. METHODS We performed an umbrella review of systematic literature reviews of simulation modelling in healthcare. Searches were conducted of academic databases (JSTOR, Scopus, PubMed, IEEE, SAGE, ACM, Wiley Online Library, ScienceDirect) and grey literature sources, enhanced by citation searches. The articles were included if they performed a systematic review of simulation modelling techniques in healthcare. After quality assessment of all included articles, data were extracted on numbers of studies included in each review, types of applications, techniques used for simulation modelling, data sources and simulation software. RESULTS The search strategy yielded a total of 117 potential articles. Following sifting, 37 heterogeneous reviews were included. Most reviews achieved moderate quality rating on a modified AMSTAR (A Measurement Tool used to Assess systematic Reviews) checklist. All the review articles described the types of applications used for simulation modelling; 15 reviews described techniques used for simulation modelling; three reviews described data sources used for simulation modelling; and six reviews described software used for simulation modelling. The remaining reviews either did not report or did not provide enough detail for the data to be extracted. CONCLUSION Simulation modelling techniques have been used for a wide range of applications in healthcare, with a variety of software tools and data sources. The number of reviews published in recent years suggest an increased interest in simulation modelling in healthcare.
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Affiliation(s)
- Syed Salleh
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
| | - Praveen Thokala
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Alan Brennan
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Ruby Hughes
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Andrew Booth
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
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Lenin RB, Lowery CL, Hitt WC, Manning NA, Lowery P, Eswaran H. Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses. BMC Health Serv Res 2015; 15:387. [PMID: 26376782 PMCID: PMC4572647 DOI: 10.1186/s12913-015-1007-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Accepted: 08/17/2015] [Indexed: 11/10/2022] Open
Abstract
Background The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing little progress despite these time-intensive interventions, we sought an alternative way to intervene the clinic without affecting the normal clinic operations. Aim The aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic. Methods We developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out “what-if” analyses. Results The best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p<.001), 30.31 % (p<.001), and 15.12 % (p<.001) improvement in patients’ average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5pm. Conclusions A discrete-event simulation model is developed, validated, and used to carry out “what-if” scenarios to identify the optimal time between appointments and number of nurses. Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions. The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics.
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Affiliation(s)
- R B Lenin
- Department of Mathematics, University of Central Arkansas, 201 Donaghey Avenue, Conway, 72035, Arkansas, USA.
| | - Curtis L Lowery
- Department of OB/GYN, University of Arkansas for Medical Sciences, 4301 West Markham Street, Little Rock, 72205, Arkansas, USA.
| | - Wilbur C Hitt
- Department of OB/GYN, University of Arkansas for Medical Sciences, 4301 West Markham Street, Little Rock, 72205, Arkansas, USA.
| | - Nirvana A Manning
- Department of OB/GYN, University of Arkansas for Medical Sciences, 4301 West Markham Street, Little Rock, 72205, Arkansas, USA.
| | - Peter Lowery
- College of Medicine, University of Arkansas for Medical Sciences, 4301 West Markham Street, Little Rock, 72205, Arkansas, USA.
| | - Hari Eswaran
- Department of OB/GYN, University of Arkansas for Medical Sciences, 4301 West Markham Street, Little Rock, 72205, Arkansas, USA.
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Fazlollahtabar H, Tehranchian AM. Integration between Mathematical Programming and Fuzzy Logic to Optimize Consumers Behavior. INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS IN THE SERVICE SECTOR 2014. [DOI: 10.4018/ijisss.2014070105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The concept of utility is used as a decision tool for consumers to choose a commodity. Analyzing consumer behavior is complicated due to the qualitative nature of the utility. Hence, this paper investigates a quantitative method to evaluate the utility of consumers. The methodology is based on linguistic expressions of fuzzy logic. Initially, the authors identify different parameters being effective on utility of a consumer. Then, using fuzzy linguistic expressions the quantitative value of utility are determined. Also some statistical analyses are set to investigate the effective parameters on the utility. Consequently, the optimization is done applying mathematical nonlinear programming. Some analysis is performed as sensitivity study. A case study is conducted to verify the applicability and effectiveness of the proposed methodology.
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Villa S, Prenestini A, Giusepi I. A framework to analyze hospital-wide patient flow logistics: evidence from an Italian comparative study. Health Policy 2014; 115:196-205. [PMID: 24461212 DOI: 10.1016/j.healthpol.2013.12.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Revised: 12/23/2013] [Accepted: 12/24/2013] [Indexed: 11/17/2022]
Abstract
Through a comparative study of six Italian hospitals, the paper develops and tests a framework to analyze hospital-wide patient flow performance. The framework adopts a system-wide approach to patient flow management and is structured around three different levels: (1) the hospital, (2) the pipelines (possible patient journeys within the hospital) and (3) the production units (physical spaces, such as operating rooms, where service delivery takes places). The focus groups and the data analysis conducted within the study support that the model is a useful tool to investigate hospital-wide implications of patient flows. The paper provides also evidence about the causes of hospital patient flow problems. Particularly, while shortage of capacity does not seem to be a relevant driver, our data shows that patient flow variability caused by inadequate allocation of capacity does represent a key problem. Results also show that the lack of coordination between different pipelines and production units is critical. Finally, the problem of overlapping between elective and unscheduled cases can be solved by setting aside a certain level of capacity for unexpected peaks.
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Affiliation(s)
- Stefano Villa
- Department of Management, Catholic University, Rome, Italy; CERISMAS, Research Centre in Health Care Management, Catholic University, Milano, Italy.
| | - Anna Prenestini
- CERGAS, Center for Research on Health and Social Care Management, Bocconi University, Milano, Italy; SDA Bocconi, School of Management, Via Roentgen, 1, 20136 Milano, Italy.
| | - Isabella Giusepi
- CERGAS, Center for Research on Health and Social Care Management, Bocconi University, Milano, Italy.
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Garg L, McClean SI, Barton M, Meenan BJ, Fullerton K. Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems. ACTA ACUST UNITED AC 2012. [DOI: 10.1109/tsmca.2012.2210211] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Rosmulder RW, Krabbendam JJ, Kerkhoff AHM, Houser CM, Luitse JSK. Computer Simulation Within Action Research: A Promising Combination for Improving Healthcare Delivery? SYSTEMIC PRACTICE AND ACTION RESEARCH 2011. [DOI: 10.1007/s11213-011-9191-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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