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A Framework for Operational Modelling of Hospital Resources
Abstract
The provision of hospital resources, such as beds, operating theatres and nurses, is a matter of considerable public and political concern and has been the subject of widespread debate [1,2,31. The political element of healthcare emphasises the need for objective methods and tools to inform the debate and provide a better foundation for decision-making. There is considerable scope for operational models to be widely used for this purpose. An appreciation of the dynamics governing a hospital system, and the flow of patients through it, point towards the need for sophisticated capacity models reflecting the complexity, uncertainty, variability and limited resources. Working alongside managers and clinicians from participating hospitals, this paper proposes a generic framework for modelling of hospital resources in the light of perceived user-needs and real-life hospital processes. The proposed framework incorporates the need for patient classification techniques to be adopted, which forms a key differentiator between this approach and other attempts to produce practical capacity planning and management tools. Statistically and clinically meaningful patient groupings may then be fed into developed simulation models and individual patients from each group passed through the particular hospital system of concern. The effectiveness of the framework is demonstrated through the development and use of an integrated hospital capacity tool.
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“Currently, most hospitals use simple heuristics to make short-term forecasts of numbers of emergency admissions, which are based on rolling averages for each day of the week10.”
From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [10]
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A comprehensive simulation for wait time reduction and capacity planning applied in general surgery
Vanberkel, P. T., & Blake, J. T. (2007) · Health Care Management Science · doi:10.1007/s10729-007-9035-6 · Cited by 193
“For larger, more robust models, Harper [11] suggests a framework that focuses on the importance of the creation of statistically and clinically meaningful patient groups, mathematically correct models, and outputs which provide the necessary information for end-users.”
BackgroundStochastic bed balancing of an obstetrics hospital
Cochran, J. K., & Bharti, A. (2006) · Health Care Management Science · doi:10.1007/s10729-006-6278-6 · Cited by 165
Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions
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“Consider the framework described by Harper [3] where there are various components or stages of analyses combining a preliminary statistical analysis with a further data investigation using methods such as classification and regression tree analysis (CART) and modelling techniques on patient length of stay.”
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A multivariate time series approach to modeling and forecasting demand in the emergency department
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A guide for building hospital simulation models
Günal, M. M. (2012) · Health Systems · doi:10.1057/hs.2012.8 · Cited by 142
“Examples of DES hospital models include Harper (2002) and Gunal & Pidd (2011).”
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Hospital capacity planning for disaster emergency management
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“%) employed discrete-event Table 6 Score of publications that described a computer simulation of patient flow in surgical care Publication ID Description of simulation study Description of simulation experiment Service Patient Policy Purpose Evaluation approach Modeling approach Simulation time Case mix Care process C…”
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Saha, E., & Ray, P. K. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2019.106051 · Cited by 110
Ravaghi, H., Alidoost, S., Mannion, R., & Bélorgeot, V. D. (2020) · BMC Health Services Research · doi:10.1186/s12913-020-5023-z · Cited by 105
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“These issues usually get more in-depth consideration within dedicated solutions from operational research and management science areas [63,64] or discrete-event simulation [65–67].”
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“Harper (2002) already emphasized the need for patient classification techniques for a better management of hospital resources.”
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“…small, such as specialist departments, but been less successful in EDs. majority of OR/MS specialists have used generalized distributions to describe arrival rates, lengths of stay and treat-ment times in simulation and optimization models (Bagust et , 1999; Hoffenberg et al , 2001; Harper, 2002).”
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Self-citationReducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation
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“Harper (2002) and Pitt (1997) have addressed some of these issues, but other challenges, such as creating clinically relevant groupings to define parameters within the model, can be difficult (Isken and Rajagop-alan 2002).”
BackgroundMachine learning for real-time aggregated prediction of hospital admission for emergency patients
King, Z., Farrington, J., Utley, M., Kung, E., Elkhodair, S., Harris, S., Sekula, R., Gillham, J., Li, K., & Crowe, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00649-y · Cited by 87
“Currently, most hospitals use simple heuristics to make short-term forecasts of numbers of emergency admissions, which are based on rolling averages for each day of the week 11 .”
Fletcher, A., & Worthington, D. (2009) · Health Care Management Science · doi:10.1007/s10729-009-9108-9 · Cited by 86
“Harper [105] presents a framework for modelling whole hospitals.”
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“Prior efforts at solving this problem for the entire hospital have relied on simulations to achieve accurate census approximations, making optimization difficult (see Helm et al. (2011), Hancock and Walter (1979), Harper (2002)).”
MethodA simple method to optimize hospital beds capacity
Nguyen, J., Six, P., Antonioli, D., Glemain, P., Potel, G., Lombrail, P., & Beux, P. L. (2004) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2004.09.001 · Cited by 80
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“In the literature, many studies examine the use of joint resources and patient flow in a subsection of a clinical pathway, such as the consultation suite [9, 14–16] or the surgery suite [4, 8].”
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Aversano, N., Rossi, F. M., & Tartaglia Polcini, P. (2017) · System dynamics for performance management · doi:10.1007/978-3-319-57018-1 · Cited by 85
“, Outcome-Based Performance Management in the Public Sector, System Dynamics for Performance Management 2, DOI 10.1007/978-3-319-57018-1_20 397 (Grossmann and Scala 2002; Harper 2002), e.g., by one or a few budget figures that define the total cost for one department.”
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Proudlove, N. C., Black, S. I., & Fletcher, A. (2006) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2602252 · Cited by 76
“A custom-built analysis and simulation tool has been applied to tactical planning of bed resources at specialty level in a number of NHS hospitals (Harper, 2002; Harper and Shahani, 2002).”
MethodA queueing network model to analyze the impact of parallelization of care on patient cycle time
Jiang, L., & Giachetti, R. E. (2007) · Health Care Management Science · doi:10.1007/s10729-007-9040-9 · Cited by 76
Case mix planning in hospitals: a review and future agenda
Hof, S., Fügener, A., Schoenfelder, J., & Brunner, J. O. (2015) · Health Care Management Science · doi:10.1007/s10729-015-9342-2 · Cited by 67
“Simulation [31] and system dynamics [8] can be complementarily used for the evaluation of stochastic hospital processes.”
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Modelling the size and skill-mix of hospital nursing teams
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“Engaging all levels of staff in the decision-making process, including lower-grade nurses, was seen as critical in achieving model credibility, and was based on the author’s previous experiences and suggested approach (Harper and Pitt, 2004). For the occupied bed method, more straightforward parameters are required. T…”
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A mixed integer programming approach to the patient admission scheduling problem
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Improving patient flow in an obstetric unit
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Improving hospital bed utilisation through simulation and optimisation
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BackgroundIntegrated Scheduling and Capacity Planning with Considerations for Patients’ Length‐of‐Stays
Liu, N., Truong, V., Wang, X., & Anderson, B. R. (2019) · Production and Operations Management · doi:10.1111/poms.13012 · Cited by 61
A Three-Level Strategy for the Design and Performance Evaluation of Hospital Departments
Fanti, M. P., Mangini, A. M., Dotoli, M., & Walter Ukovich, A. (2012) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmca.2012.2217319 · Cited by 60
“significant and numerous results in the area of HD design and management [6], [12], [13], [15], [18], [19], [21], [26], [29], [38], [46], [51].”
BackgroundOperating Room Planning and Scheduling
Demeulemeester, E. L., Beliën, J., Cardoen, B., & Samudra, M. (2013) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-1-4614-5885-2_5 · Cited by 59
“At the end of the search procedure, a set of 181 articles was identified of which 136 [2, 3, 5–16, 18–20, 22–25, 27, 28, 33–41, 43, 45, 46, 48–62, 64, 65, 67–84, 86–94, 96–100, 105–111, 113–115, 117–123, 126–132, 136–148, 150–157, 161–164, 166–168] were found to be technically oriented.”
InfluentialMcClean, S. I., Barton, M., Garg, L., & Fullerton, K. J. (2011) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/2000494.2000498 · Cited by 58
“Such heterogeneity arises from a number of sources, for example, method of admission, diagnosis, severity of illness, age, gender, treatment, [Faddy and McClean 2005; Marshall and McClean 2004; Harper 2002; Dimakou et al. 2009].”
MethodPlacing Emergency Department Crowding on the Decision Agenda
Bradley, V. M. (2005) · Journal of Emergency Nursing · doi:10.1016/j.jen.2005.04.007 · Cited by 57
Accounting for Inpatient Wards When Developing Master Surgical Schedules
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Agent-based patient admission scheduling in hospitals
Hutzschenreuter, A. K., Bosman, P. A. N., Blonk-Altena, I., van Aarle, J., & Poutré, H. L. (2008) · TU/e Research Portal · doi:10.5555/1402795.1402804 · Cited by 49
A statistical Markov chain approximation of transient hospital inpatient inventory
Broyles, J. R., Cochran, J. K., & Montgomery, D. C. (2010) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.06.021 · Cited by 47
“High model complexity decreases model quality and discourages implementation (Mackay and Lee, 2005; Harper, 2002).”
BackgroundImplementation issues of modeling healthcare problems: Misconceptions and lessons
Eldabi, T. (2009) · Winter Simulation Conference · doi:10.1109/wsc.2009.5429192 · Cited by 47
“Over the years there have been numerous attempts such as Paul and Kuljis (1995) and Harper (2002) .”
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“Other examples include, among others, Kusters and Groot [3], Harper [4], and Jittamai and Kangwansura [5].”
BackgroundModeling and simulation framework for value-based healthcare systems
Traoré, M. K., Zacharewicz, G., Duboz, R., & Zeigler, B. P. (2018) · SIMULATION · doi:10.1177/0037549718776765 · Cited by 41
“surgical unit, the average waiting time in an emergency department, or the optimal scheduling of health care activities (Harper, 2002), (Augusto & Xie, 2014), (Viana et al.”
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Blake, J. T., & Hardy, M. (2014) · Operations Research for Health Care · doi:10.1016/j.orhc.2014.05.002 · Cited by 40
Hasan, I., Bahalkeh, E., & Yih, Y. (2020) · SIMULATION · doi:10.1177/0037549720914749 · Cited by 39
A Generic Framework for Hybrid Simulation in Healthcare
Chahal, K. (2016) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 38
Real-Time Capacity Management and Patient Flow Optimization in Hospitals Using AI Methods
Munavalli, J. R., Boersma, H. J., Rao, S. V., & van Merode, G. G. (2020) · Springer eBooks · doi:10.1007/978-3-030-45240-7_3 · Cited by 38
Reducing elective general surgery cancellations at a Canadian hospital
Azari-Rad, S., Yontef, A. L., Aleman, D. M., & Urbach, D. R. (2013) · Canadian Journal of Surgery · doi:10.1503/cjs.018411 · Cited by 38
“The dynamics governing a hospital system demand capacity models that reflect the complexity, uncertainty, variability and limited resources.(5) Operating room planning and scheduling may be addressed by simulation modelling.”
BackgroundA simulation model for perioperative process improvement
Azari-Rad, S., Yontef, A. L., Aleman, D. M., & Urbach, D. R. (2014) · Operations Research for Health Care · doi:10.1016/j.orhc.2013.12.003 · Cited by 38
“Meanwhile, Harper [22] proposes a generic framework for modelling hospital resources and consider user needs and real-life processes.”
Healthcare Data Mining: Prediction Inpatient Length of Stay
Liu, P., Lei, L., Yin, J., Zhang, W., Naijun, W., & El‐Darzi, E. (2006) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2006.348528 · Cited by 38
“Data mining algorithms have also been successfully applied to predict LOS, for example see [5] and [6].”
MethodOptimal distribution of operating hours over operating rooms using probabilities
Koppka, L., Wiesche, L., Schacht, M., & Werners, B. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.12.025 · Cited by 38
Stewart, I. D., Leary, A., Khakwani, A., Borthwick, D., Tod, A., Hubbard, R., Beckett, P., & Tata, L. J. (2020) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2020.103718 · Cited by 38
“It also provides weight to the argument that stochastic, flexible frameworks to model the workforce may yield more intelligent solutions and more effective workforces ( Harper, 2002 ), providing advanced nurse practitioners more focus on clinical responsibilities.”
BackgroundRomero, H., Dellaert, N., van der Geer, S., Frunt, M., Jansen-Vullers, M. H., & Krekels, G. A. M. (2012) · Health Care Management Science · doi:10.1007/s10729-012-9213-z · Cited by 38
Demand and capacity modelling for acute services using discrete event simulation
Demir, E., Günal, M. M., & Southern, D. (2016) · Health Systems · doi:10.1057/hs.2016.1 · Cited by 37
“Patient flows are partly dependent on the process of care and decisions taken by medical staff, and partly inherent uncertainties of healthcare processes (Cote, 2000; Harper, 2002).”
BackgroundInfluentialGeneric operational models in health service operations management: A systematic review
Mahdavi, M., Malmström, T., van de Klundert, J. J., Elkhuizen, S., & Vissers, J. (2013) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2013.07.002 · Cited by 38
Tyler, J. M. B., Murch, B. J., Vasilakis, C., & Wood, R. M. (2022) · Journal of Simulation · doi:10.1080/17477778.2022.2081521 · Cited by 36
Cost‐effective workforce planning: optimising the dental team skill‐mix for England
Harper, P., Kleinman, E. R., Gallagher, J. E., & Knight, V. (2013) · Journal of Enterprise Information Management · doi:10.1108/17410391311289569 · Cited by 35
Self-citationAn intelligent real-time scheduler for out-patient clinics: A multi-agent system model
Munavalli, J. R., Rao, S. V., Aravind, S., & van Merode, G. G. (2020) · Health Informatics Journal · doi:10.1177/1460458220905380 · Cited by 34
“14 The mismatch in demand and supply results in waiting time and under-utilization.”
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Assessing the impact of uncertainty and the level of aggregation in case mix planning
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Using phase-type models to cost stroke patient care across health, social and community services
McClean, S. I., Gillespie, J., Garg, L., Barton, M., Scotney, B. W., & Kullerton, K. (2014) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.01.063 · Cited by 30
An evaluation of ‘fast track’ in A&E: a discrete event simulation approach
Maull, R., Smart, P. A., Harris, A., & Karasneh, A. A. (2009) · Service Industries Journal · doi:10.1080/02642060902749534 · Cited by 30
“Not surprisingly the mean time reduced from approximately 146 1 minutes to 115 minutes, the median had moved from 126 to 106 minutes and the standard deviation had reduced from 89 minutes to 68 minutes. However, while these results reflect dramatic improvements in the throughput of patients with low priority condition…”
BackgroundResultInfluentialA decision support simulation model for bed management in healthcare
Cudney, E. A., Baru, R. A., Guardiola, I. G., Materla, T., Cahill, W. T., Phillips, R., Mutter, B. V., Warner, D. L., & Masek, C. (2019) · International Journal of Health Care Quality Assurance · doi:10.1108/ijhcqa-10-2017-0186 · Cited by 28
Modelling catchment areas for secondary care providers: a case study
Jones, S., Wardlaw, J., Crouch, S., & Carolan, M. (2011) · Health Care Management Science · doi:10.1007/s10729-011-9154-y · Cited by 27
“they are useful planning tools because hospitals serve more than one administrative area and catchment areas can be used for resource allocation for beds, operating theatres and nurses [11] (Flowers [7, 8]).”
BackgroundApplication of Discrete-Event Simulation in Health Care: a Review
Arisha, A. (2009) · Arrow - TU Dublin (Technological University Dublin) · doi:10.21427/d7w500 · Cited by 26
“The benefits of the suggested framework are the Technical Report Dublin Institute of Technology Michael Thorwarth, Amr Arisha 11 development of a detailed integrated simulation tools f r the planning and management of hospital beds, operating theatres and workforce needs (Harper, 2002).”
MethodKortbeek, N., Braaksma, A., Smeenk, F., Bakker, P. J. M., & Boucherie, R. J. (2014) · Journal of the Operational Research Society · doi:10.1057/jors.2014.67 · Cited by 26
“Anticipation for elective surgical patients is possible as well, by taking the surgical schedule into account (Green and Nguyen, 2001; Harper, 2002; Vissers et al, 2007; Adan et al, 2009).”
BackgroundMethodInfluentialEffken, J. A., Brewer, B. B., Patil, A., Lamb, G. S., Verran, J. A., & Carley, K. M. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2005.02.003 · Cited by 25
Xie, Y., Breen, L., Cherrett, T., Zheng, D., & Allen, C. J. (2016) · Supply Chain Management An International Journal · doi:10.1108/scm-07-2015-0278 · Cited by 26
“This database will not only help consultants and managers of the MEL make informed decisions on sourcing, but also increase active interaction with suppliers and patients to improve quality of care (Harper, 2002).”
Munavalli, J. R., Rao, S. V., Aravind, S., Manjunath, U., & van Merode, G. G. (2017) · Journal of Health Management · doi:10.1177/0972063417727627 · Cited by 24
Costing hospital resources for stroke patients using phase-type models
Gillespie, J., McClean, S. I., Scotney, B. W., Garg, L., Barton, M., & Fullerton, K. J. (2011) · Health Care Management Science · doi:10.1007/s10729-011-9170-y · Cited by 24
“Due to the number of different pathways patients can take from admission to discharge, healthcare facilities can be seen as part of a complex system [18].”
BackgroundApplying the variety reduction principle to management of ancillary services
Elkhuizen, S., van Sambeek, J. R. C., Hans, E. W., Krabbendam, K., & Bakker, P. J. M. (2007) · Health Care Management Review · doi:10.1097/00004010-200701000-00006 · Cited by 24
Operating Room Scheduling Problems: A Survey and a Proposed Solution Framework
Abdelrasol, Z. Y., Harraz, N. A., & Eltawil, A. (2014) · Springer eBooks · doi:10.1007/978-94-017-9115-1_52 · Cited by 24
“[23] Harper (2002).”
A model-driven framework for multi-paradigm modeling and holistic simulation of healthcare systems
Djitog, I., Aliyu, H. O., & Traoré, M. K. (2017) · SIMULATION · doi:10.1177/0037549717744888 · Cited by 23
“[44] U U [45] U U U [46] U U [47] U U [48] U U [49] U U [50] U U [51] U U [52] U U [53] U U [54] U [55] U U [56] U U [57] U U [58] [59] U U U Djitog et al.”
An exact approach for tactical planning and patient selection for elective surgeries
Anjomshoa, H., Dumitrescu, I., Lustig, I. J., & Smith, O. J. (2018) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.01.048 · Cited by 23
An Integrated Lean Supply Chain Framework for U.S. Hospitals
Chakraborty, S., & González, J. A. (2018) · Operations and Supply Chain Management An International Journal · doi:10.31387/oscm0310206 · Cited by 24
“…example, that major hospital facilities like ICUs as well as minor equipment such as drip stands are available when required so procedures are not delayed (Alexander et al., 1996; Butler & Leong, 2000; Cook and Rasmussen, 2005; Devaraj et al., 2013; Goldstein et al., 2002; Harper, 2002; Hay, 2003).”
BackgroundLength of Stay-Based Clustering Methods for Patient Grouping
El‐Darzi, E., Abbi, R., Vasilakis, C., Gorunescu, F., Gorunescu, M., & Millard, P. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-00179-6_3 · Cited by 23
A multi-objective location-allocation model in mass casualty events response
Pouraliakbari, M., Mohammadi, M. N. S., & Mirzazadeh, A. (2017) · Journal of Modelling in Management · doi:10.1108/jm2-02-2017-0018 · Cited by 22
A Proposed Solution Framework for the Operating Room Scheduling Problems
Abdelrasol, Z. Y., Harraz, N. A., & Eltawil, A. (2013) · Cited by 22
Oakley, D., Onggo, B. S., & Worthington, D. (2019) · Health Care Management Science · doi:10.1007/s10729-019-09485-1 · Cited by 22
“[16] develops a generic framework for modelling hospital resources, and outlines a number of modelling considerations for OR/ MS practitioners working in this domain.”
BackgroundSystematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
Martín, D. R., Fernández, C. B., & Municio, Á. M. G. (2025) · Applied Sciences · doi:10.3390/app15041861 · Cited by 25
Tariverdi, M., Miller-Hooks, E. D., Kirsch, T. D., & Levin, S. (2019) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2019.1584132 · Cited by 21
Using computational modeling to transform nursing data into actionable information
Effken, J. A., Brewer, B. B., Patil, A., Lamb, G. S., Verran, J. A., & Carley, K. M. (2003) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2003.09.018 · Cited by 21
“In addition, computational modeling has been used in healthcare operations research to help managers schedule appointments more efficiently [9–11], modify workflow [12,13], project resource needs [14], and anticipate the financial and patient outcomes of programmatic changes [15–17].”
MethodModeling a Healthcare System as a Queueing Network: The Case of a Belgian HospitalPreprint
Creemers, S., & Lambrecht, M. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.1093618 · Cited by 21
“becomes available (Vissers et al., 2001; Harper, 2002; Vandaele & De Boeck, 2003a; Sethuraman”
BackgroundOrdu, M., Demir, E., Tofallis, C., & Günal, M. M. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100248 · Cited by 20
“For example [35], developed a generic framework to model level of resources (i.e., beds, staff, and operating theatre) by capturing variability, uncertainty, limited resources and complexity of a hospital.”
Chu, H., Lin, B., Yu, M.-R., & Chan, T. (2016) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph13121235 · Cited by 20
“Thus, the management of hospital resources, including beds, equipment, and clinical manpower, and so on [4], in the existing hospital networks has become a significant issue [5].”
BackgroundA Two-Stage Model to Predict Surgical Patients’ Lengths of Stay From an Electronic Patient Database
Kumar, A., & Anjomshoa, H. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2819646 · Cited by 20
“The random arrival time and the uncertainty in resource requirements of each individual are the sources of variability in demand for services [2].”
BackgroundMethodInfluentialAn Inventory Optimization Model To Support Operating Room Schedules
Rappold, J. A., Roo, B. V., Martinelly, C. D., & Riane, F. (2011) · Supply Chain Forum an International Journal · doi:10.1080/16258312.2011.11517254 · Cited by 20
“Harper (2002) provides a useful discussion and process-based framework for improving the organizational structure for the use of hospital resources.”
BackgroundA hybrid approach using forecasting and discrete-event simulation for endoscopy services
Harper, A. L., Mustafee, N., & Feeney, M. (2017) · Winter Simulation Conference · doi:10.1109/wsc.2017.8247899 · Cited by 19
A simulation-based study for managing hospital emergency department's capacity in extreme events
Xiao, N., Sharman, R., Rao, H., & Dutta, S. (2012) · International Journal of Business Excellence · doi:10.1504/ijbex.2012.044578 · Cited by 19
Metadata
- Authors
- Harper, P. R.
- Affiliations
- University of Southampton (GB)
- Corresponding author
- Harper, Paul R.
- ORCID
- Harper, Paul R.
- Venue
- Health Care Management Science
- Published
- 2002-08
- Volume
- 5
- Issue
- 3
- Pages
- 165-173
- ISSN
- 1386-9620 (ISSN-L)
- 1572-9389
- OpenAlex
- W1608285042
- Cited by
- 279
- References
- 26
- Citations by year
- 2026: 10 · 2025: 5 · 2024: 6 · 2023: 6 · 2022: 9 · 2021: 11 · 2020: 13 · 2019: 14 · 2018: 12 · 2017: 14 · 2016: 23 · 2015: 16 · 2014: 14 · 2013: 18 · 2012: 25
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Health Care Management Science (publishedVersion)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- University of Southampton
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Healthcare Operations and Scheduling Optimization
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Quality and Management
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Keywords
- hospital capacity planning, patient classification, patient flow, hospital operations, healthcare decision-making, computer simulation, hospital bed capacity, operating theatre capacity, nurse staffing
- MeSH terms
- Algorithms, Appointments and Schedules, Bed Occupancy, Computer Simulation, Decision Support Systems, Management, United Kingdom, Hospital Administration, Hospital Bed Capacity, Humans, Patients, Planning Techniques, Politics, Health Care Rationing, Models, Organizational, Efficiency, Organizational, Needs Assessment
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