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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

2010· article· es· W2615264920 sur OpenAlexaboutno aff
Bernd Rechel, Stephen Wright, James Barlow, Martin McKee

Notice bibliographique

RevueBulletin of the World Health Organization · 2010
Typearticle
Languees
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStock (firearms)PopulationHospital bedRevenueGeographyCapacity planningBusinessOperations managementMedicineFinanceEconomicsEnvironmental healthNursing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,049
Score d'incertitude au seuil0,098

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0040,003
Science ouverte0,0020,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,035
Tête enseignante GPT0,273
Écart entre enseignants0,238 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2010
Routes d'admission1
Résumé présentoui

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