The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
A Simplex Method for Function Minimization
Nelder, J. A., & Mead, R. (1965) · The Computer Journal · doi:10.1093/comjnl/7.4.308 · Cited by 28,905
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Dynamics of bed use in accommodating emergency admissions: stochastic simulation model
Bagust, A., Place, M., & Posnett, J. W. (1999) · BMJ · doi:10.1136/bmj.319.7203.155 · Cited by 485
Algorithm AS 47: Function Minimization Using a Simplex Procedure
O’Neill, R. P. (1971) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2346772 · Cited by 285
Akitt, J. W. (1977) · The Computer Journal · doi:10.1093/comjnl/20.1.84 · Cited by 164
Solving a nurse scheduling problem with knapsacks, networks and tabu search
Dowsland, K. A., & Thompson, J. M. (2000) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2600970 · Cited by 146
Statistical Modeling to Predict Elective Surgery Time
Wright, I. H., Kooperberg, C. L., Bonar, B. A., & Bashein, G. (1996) · Anesthesiology · doi:10.1097/00000542-199612000-00003 · Cited by 136
The continuing rise in emergency admissions
Capewell, S. (1996) · BMJ · doi:10.1136/bmj.312.7037.991 · Cited by 134
Bed Allocation in a Public Health Care Delivery System
Kao, E. P. C., & Tung, G. G. (1981) · Management Science · doi:10.1287/mnsc.27.5.507 · Cited by 114
Surgical unit time utilization review: Resource utilization and management implications
Gordon, T. A., Paul, S. L., Lyles, A., & Fountain, J. (1988) · Journal of Medical Systems · doi:10.1007/bf00996639 · Cited by 99
Workforce staffing and scheduling: Hospital nursing specific models
Siferd, S. P., & Benton, W. C. (1992) · European Journal of Operational Research · doi:10.1016/0377-2217(92)90075-k · Cited by 93
A simulation model for bed allocation to hospital inpatient departments
Vassilacopoulos, G. (1985) · SIMULATION · doi:10.1177/003754978504500502 · Cited by 88
Simulation modeling for hospital bed planning
Dumas, M. (1984) · SIMULATION · doi:10.1177/003754978404300201 · Cited by 68
Rosenbloom, E. S., & Goertzen, N. F. (1987) · European Journal of Operational Research · doi:10.1016/0377-2217(87)90131-7 · Cited by 67
Case-Mix Groups for VA Hospital-Based Home Care
Smith, M. E., Baker, C. R., Branch, L. G., Walls, R. C., Grimes, R. M., Karklins, J. M., Kashner, M. T., Burrage, R., Parks, A., Rogers, P., et al. (1992) · Medical Care · doi:10.1097/00005650-199201000-00001 · Cited by 62
A planning model for requirement of emergency beds
HUANG, X.-M. (1995) · Mathematical Medicine and Biology A Journal of the IMA · doi:10.1093/imammb/12.3-4.345 · Cited by 58
Wolfe, H. E., & Young, J. P. (1965) · Nursing Research · doi:10.1097/00006199-196501430-00011 · Cited by 57
Emergency medical admissions: taking stock and planning for winter
Blatchford, O., & Capewell, S. (1997) · BMJ · doi:10.1136/bmj.315.7119.1322 · Cited by 45
Classification treesA possible method for iso‐resource grouping in intensive care
Ridley, S., Jones, S., Shahani, A. K., Brampton, W., Nielsen, M., & Rowan, K. (1998) · Anaesthesia · doi:10.1046/j.1365-2044.1998.t01-1-00564.x · Cited by 41
A decision modes for shift scheduling of nurses
Siferd, S. P., & Benton, W. C. (1994) · European Journal of Operational Research · doi:10.1016/0377-2217(94)90228-3 · Cited by 38
Modeling and forecasting hospital patient movements: Univariate and multiple time series approaches
Lin, W. T. (1989) · International Journal of Forecasting · doi:10.1016/0169-2070(89)90087-3 · Cited by 38
A Mixed Integer Programming Model for Regional Planning of a Hospital Inpatient Service
Ruth, R. J. (1981) · Management Science · doi:10.1287/mnsc.27.5.521 · Cited by 32
Towards a model for hospital case-load decision support
Gove, D. J., HEWETT, D., & Shahani, A. K. (1995) · Mathematical Medicine and Biology A Journal of the IMA · doi:10.1093/imammb/12.3-4.329 · Cited by 13
Reasonable Averages that Wrong Answers
Shahani, A. K. (1981) · Teaching Statistics · doi:10.1111/j.1467-9639.1981.tb00425.x · Cited by 7
Kasperlik-Załuska (1995) · doi:10.1002/1097-0142(19950515)75:10
Research works printed but not linked: 1
- J.D. Hawkins et al., TOCHSIM manual (Southampton Centre for Operational Research, University of Southampton, UK, 1992).
Cited by
Sorted by citations
Operating room planning and scheduling: A literature review
Cardoen, B., Demeulemeester, E. L., & Beliën, J. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.04.011 · Cited by 1,188
“Although Lagergren [74] indicates that this lack of implementation in the health services seems to have improved considerably, it is hard to find statements in contributions that explicitly confirm the implementation and use of the procedures in practice (see [13,14,65] for an example).”
BackgroundOperational research in the management of the operating theatre: a survey
Guerriero, F., & Guido, R. (2010) · Health Care Management Science · doi:10.1007/s10729-010-9143-6 · Cited by 526
“MORE 1 4 [122] 7 1–2–6–9–11–17 1–3 1D–1–2W 2 4 [123] 7–8 2 20 3 1D 2 4 [124] 2 7 6 1–2–3 1–2 W 1 Different scheduling policies have been presented by Harper [122] that developed a simulation model (PROMPT, Patient and Resource Operational Management Planning Tool) for hospital resource planning.”
MethodHulshof, P. J. H., Kortbeek, N., Boucherie, R. J., Hans, E. W., & Bakker, P. J. M. (2012) · Health Systems · doi:10.1057/hs.2012.18 · Cited by 432
“sonal [112] or weekend demand [116] effects.”
BackgroundMethodInfluentialProcess mining for healthcare: Characteristics and challenges
Muñoz-Gama, J., Martin, N., Fernández-Llatas, C., Johnson, O. A., Sepúlveda, M., Helm, E., Galvez-Yanjari, V., Rojas, E., Martínez-Millana, A., Aloini, D., et al. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.103994 · Cited by 299
“Healthcare processes are complex, in part because they tend to exhibit significant variability [4,18,55].”
BackgroundA three-phase approach for operating theatre schedules
Testi, A., Tànfani, E., & Torre, G. (2007) · Health Care Management Science · doi:10.1007/s10729-007-9011-1 · Cited by 272
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
