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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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From Machine learning for real-time aggregated prediction of hospital admission for emergency patients (2022) · cites this paper as [10]
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“…to date indicates that hospitals struggle to manage bed capacity (Akcali, Cˆot´e, and Lin (2006)) and many plan their beds based on average demand (Harper (2002)), using deterministic spreadsheets rather than on the real life variation in patient types, arrivals and length of stay (LOS) which…”
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“Over the years several authors have developed guidelines for modellers: Harper (2002) focused on modelling hospitals, Harper and Pitt (2004) described a general framework for increasing the chance of success, and Brailsford et al. (2013) identified a set of barriers and facilitators.”
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“It is fair though to say that the majority of studies that capture the true underlying stochastic nature of such systems (Harper, 2002) traditionally fit distributions to observed service times, such as length of stay, and use these within developed analytical, or more typically, simulation models.”
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“…which is particularly challenging due to the existence of conflicting interests between the people involved in it, and the limitation of the OR’s typically high-cost resources (Bacelar-Silva et al., 2020; Cardoen et al., 2010b; Glouberman & Mintzberg, 2001; Harper, 2002; Macario et al., 1995).”
BackgroundPredictive and prescriptive analytics for multi-site modelling of frail and elderly patient services
Williams, E., Gärtner, D., & Harper, P. (2025) · Journal of the Operational Research Society · doi:10.1080/01605682.2025.2487650 · Cited by 5
“Ensuring healthcare facilities possess adequate resources like staff, equipment, and beds to meet patient demand efficiently is essential for effective healthcare operations management (Ardakani, Larimi, Nejad, Hosseini, & Zargoush, 2023; Costa et al., 2003; P. Harper, 2002).”
Self-citationUsing Modelling and Simulation to Improve Elderly Care in Ireland: A Case Study
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“The dynamic capabilities of simulation can allow a more accurate interpretation of the utilization of hospital resources to be envisaged [50], supporting hospital managers’ decisions on bed usage and patient flow through the hospital [51] by modelling problems of patient flows [52], and then using scenarios to illustr…”
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“In other words, the parameters of the binomial distribution must reflect all of the patients in a given cohort (For a discussion on defining statistically equivalent patient cohorts, see [89]).”
BackgroundWhy healthcare professionals are slow to adopt modeling and simulation
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“Facilitating appropriate hospital services to be delivered to the correct patient in a timely manner can significantly affect healthcare costs (Harper, 2002) and the quality of care (Zeng et al., 2012).”
BackgroundInput modeling for hospital simulation models using electronic messages
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“(Harper 2002) Others resort to expensive, setting-specific manual da-ta collection.”
BackgroundThe value of modelling and simulation in healthcare
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“A more accurate interpretation of the utilization of hospital resources can be envisaged using dynamic capabilities of simulation [16], which in turn supports the hospital management in their decisions on bed usage and patient flow [17].”
BackgroundUsing computational modeling to improve patient care unit safety and quality outcomes.
Effken, J. A., Brewer, B. B., Patil, A., Lamb, G. S., Verran, J. A., & Carley, K. M. (2004) · PubMed · Cited by 3
Nguyen, V. T., Sommer, A. F., Steger-Jensen, K., & Hvolby, H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44736-9_82 · Cited by 3
“The main resource types include human resources (doctors, nurses, secretaries, porters, and planning personnel), medical equipment (operation packages and medicines ), and facilities (beds and operating theatres) [19],[20].”
Using phase-type models to cost a cohort of stroke patients
McClean, S. I., Gillespie, J., Scotney, B. W., & Fullerton, K. J. (2012) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2012.6266406 · Cited by 3
A Potential Influenza Pandemic Modelled in Greek Refugee Camps
Searle, C., & Sutherland, G. P. (2020) · Journal of Refugee Studies · doi:10.1093/jrs/feaa028 · Cited by 3
Rashwan, W., Habib, H., Courtney, G., Arisha, A., & Kennelly, S. (2016) · Winter Simulation Conference · doi:10.1109/wsc.2016.7822236 · Cited by 3
“The dynamic capabilities of simulation enable more accurate interpretation of the use of hospital resources (Thorwarth, Rashwan, and Arisha 2015), supporting hospital managers in their decisions on bed usage and patient flow (Harper 2002).”
MethodWhy healthcare professionals are slow to adopt modeling and simulation
Fackler, J. C., Hankin, J., & Young, T. P. (2012) · Winter Simulation Conference · doi:10.1109/wsc.2012.6465330 · Cited by 3
Resource Allocation Decisions in the Public Sector: A Model Based on a Study of PolicingPreprint
Zhuang, Z. Y., & Collier, P. M. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1626070 · Cited by 3
Olsen, A. B. (2015) · doi:10.31274/etd-180810-4048 · Cited by 3
“Harper (2002) simulated the following rules: First Come First Served (FCFS), LPT, SPT, and longest time first followed by shortest first (LTSC).”
Ceglowski, A. S. (2017) · Figshare · doi:10.26180/4519370 · Cited by 3
Resource constraints in health care - case studies on technical, allocative and economic efficiency
Eklund, F. (2008) · Aaltodoc (Aalto University) · Cited by 3
Destrieux, L., Yemmas, Y., Williams, S., & Meur, N. L. (2024) · Annals of Vascular Surgery · doi:10.1016/j.avsg.2023.12.080 · Cited by 2
Putman, K., Anstey, A. V., Harper, P., & Knight, V. (2015) · Health Systems · doi:10.1057/hs.2015.4 · Cited by 2
Self-citationCapacity and availability management by quantitative project management in the IT service industry
Bairi, J., Manohar, B. M., & Kundu, G. K. (2012) · Asian Journal on Quality · doi:10.1108/15982681211265472 · Cited by 2
Generalized Net Model for Outpatient Care in Bulgaria
Tasseva, V., Peneva, D., Atanassov, K. T., El‐Darzi, E., Chountas, P., & Vasilakis, C. (2007) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2007.52 · Cited by 2
“the average needs, the average cost, the average duration of activities, the average patient length of stay (LOS) and the number of discharges etc.[1].”
Background
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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