A Sizing Tool for Allocation Planning of Hospital Bed Resources

Wang, Tao; Guinet, Alain; Besombes, Béatrix

doi:10.1007/978-3-642-00179-6_7

Cited by 7 publications

(1 citation statement)

References 10 publications

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“…In a tactical point of view, Cochran and Roche [2] used a methodology also based on queuing network in order to improve the capacity of emergency service according to patients' type, the random entrances, etc. Wang [3] studied the bed resources allocation taking into account hospitalization demands, bed capacity and incomes coming from the activity, while optimizing the incomes and minimizing of additional beds. In an operational point of view, Vassilacopoulos [4] used a simulation model in order to improve the bed utilization while taking into account specific operational constraints (level of emergency, bed occupancy and length of waiting list).…”

Section: Hospital Bed Managementmentioning

confidence: 99%

Strategic and Tactical Hospital Bed Management in Belgium and in France

Gossart

Meskens

Guinet

2010

IFIP Advances in Information and Communication Technology

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International audienceNowadays, lots of healthcare establishments decide to integrate a hospital bed management department, while taking into account the financial impact on the budget, the disruptions and factors influencing the bed planning as e.g. the type of care units management and the existing networks between hospitals and satellite structures. Lots of constraints will also have an impact while optimizing the available resources: accepting the emergency case and therefore delaying the planned patient, adding additional beds within a care unit and studying at the same time the collateral costs, opening or closing beds related to a specialty, etc

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Section: Hospital Bed Managementmentioning

confidence: 99%

Strategic and Tactical Hospital Bed Management in Belgium and in France

Gossart

Meskens

Guinet

2010

IFIP Advances in Information and Communication Technology

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Two-MILP models for scheduling elective surgeries within a private healthcare facility

Hachicha

Mansour

2016

Health Care Manag Sci

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This paper deals with an Integrated Elective Surgery-Scheduling Problem (IESSP) that arises in a privately operated healthcare facility. It aims to optimize the resource utilization of the entire surgery process including pre-operative, per-operative and post-operative activities. Moreover, it addresses a specific feature of private facilities where surgeons are independent service providers and may conduct their surgeries in different private healthcare facilities. Thus, the problem requires the assignment of surgery patients to hospital beds, operating rooms and recovery beds as well as their sequencing over a 1-day period while taking into account surgeons' availability constraints. We present two Mixed Integer Linear Programs (MILP) that model the IESSP as a three-stage hybrid flow-shop scheduling problem with recirculation, resource synchronization, dedicated machines, and blocking constraints. To assess the empirical performance of the proposed models, we conducted experiments on real-world data of a Tunisian private clinic: Clinique Ennasr and on randomly generated instances. Two criteria were minimised: the patients' average length of stay and the number of patients' overnight stays. The computational results show that the proposed models can solve instances with up to 44 surgical cases in a reasonable CPU time using a general-purpose MILP solver.

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A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach

Ordu

Demir

Tofallis

et al. 2020

Journal of the Operational Research Society

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The increasing pressures on the healthcare system in the UK are well documented. The solution lies in making best use of existing resources (e.g. beds), as additional funding is not available. Increasing demand and capacity shortages are experienced across all specialties and services in hospitals. Modelling at this level of detail is a necessity, as all the services are interconnected, and cannot be assumed to be independent of each other. Our review of the literature revealed two facts; First an entire hospital model is rare, and second, use of multiple OR techniques are applied more frequently in recent years. Hybrid models which combine forecasting, simulation and optimization are becoming more popular. We developed a model that linked each and every service and specialty including A&E, and outpatient and inpatient services, with the aim of, 1) forecasting demand for all the specialties, 2) capturing all the uncertainties of patient pathway within a hospital setting using discrete event simulation, and 3) developing a linear optimization model to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from simulation). These results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans, and highlight the benefits of hybrid models.

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A Sizing Tool for Allocation Planning of Hospital Bed Resources

Cited by 7 publications

References 10 publications

Strategic and Tactical Hospital Bed Management in Belgium and in France

Strategic and Tactical Hospital Bed Management in Belgium and in France

Two-MILP models for scheduling elective surgeries within a private healthcare facility

A novel healthcare resource allocation decision support tool: A forecasting-simulation-optimization approach

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