Hospital Capacity Planning: From Measuring Stocks to Modelling flows/Planification Des Capacites Hospitalieres: De la Mesure Des Stocks a la Modelisation Des flux/Planificacion De la Capacidad Hospitalaria: Desde la Medicion De Existencias Hasta El Modelado De Flujos
Bibliographic record
Abstract
Introduction Although revenue for hospital services in many developed countries is increasingly based on measures of activity such as diagnosis-related groups, hospital capacity planning remains dominated by numbers. (1) A review of international practice found that bed capacity continues to be the preferred unit for planning hospital care in Finland, Germany, Italy, New Zealand and most Canadian provinces. Of the countries included in the review, only England and France were moving towards planning based on service volume and activity. (2,3) Bed occupancy and the ratio of beds per population remain predominant metrics in hospital capacity planning. (4-6) There are several problems associated with this approach. Most importantly, bed or bed occupancy do not provide a good measure of the services provided inside hospitals, given the wide variation in case mix and thus treatment costs of those occupying the beds, (4) nor are they suitable for predicting future demand. (7) The measure implies that the bed is the core piece of capital stock in the hospital, constraining the performance of the other assets around it. The near universal trend towards growing of day cases and shorter lengths of hospital stay further invalidates beds as a measure of capacity. The continued use of numbers also fails to consider the trade-offs and complementarities from investing in different types of health capital. Thus, while bed have the benefit of convenience, as they are one of the few indices of hospital capacity that are routinely collected, there is a growing recognition of the intrinsic limits of this measure. Neither are diagnosis-related groups an appropriate methodology for capacity planning. They are simply a way of categorizing admissions, derived from retrospective micro-costing data and combining diagnosis and any interventions. (8) This means that they can be used for calculating prices but they say little about the mix of resources that is needed. (9) There seems to be a clear need to employ other metrics and, in some places, this is happening. However, so far, this process has been poorly documented. This paper responds to a call for a focus on care pathways when designing and constructing health-care facilities. (10) We review the case for new approaches to planning hospital capacity and describe some experiences with alternative approaches by drawing on insights gained in a recent study of health capital investment in Europe undertaken by the European Observatory on Health Systems and Policies and the European Health Property Network. (11,12) The project involved a review of the literature around core themes, including capacity planning and how to translate services into assets, and analysis of a series of case studies from across Europe, including the Coxa hospital in Finland, the Rhon Klinikum Group in Germany, the Orbis and Groningen hospitals in the Netherlands, the Trondheim hospital in Norway, the John Paul II hospital in Poland, the Alzira franchise in Spain, the Karolinska hospital in Sweden, as well as regional planning in Tuscany (Italy) and Northern Ireland. Need for innovation Traditionally, hospitals were designed around specialties and departments rather than around the needs of patients. Patients often spend most of their time in hospitals waiting for something to happen, with large areas provided for this inactivity. The situation is often exacerbated by the inefficient management of admission and discharge. In the United Kingdom of Great Britain and Northern Ireland, a patient admitted on a Friday night may have a length of stay that is 25% longer than a patient admitted on a Tuesday. (13) To accommodate this phenomenon, beds and wards in effect become holding areas for work in progress and have, in the past, been planned accordingly. A consequence is that in many hospitals the flow of patients is inefficient, dislocated and disorganized. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".