Modelling for the planning and management of bed capacities in hospitals

Harper, Paul Robert; Shahani, A. K.

doi:10.1057/palgrave/jors/2601278

Cited by 193 publications

(125 citation statements)

References 0 publications

Supporting

Mentioning

119

Contrasting

Unclassified

Order By: Relevance

“…These models usually take the form of some deterministic, spreadsheet-based calculations. [2] However, since a hospital is a complex stochastic system, simple deterministic approaches for planning and managing the system are considered to be inadequate. [3,4] A better way of assessing the system's activity is to consider the measurement of flow of patients through hospitals and other health care facilities.…”

Section: Introductionmentioning

confidence: 99%

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

Marshall

Vasilakis

El‐Darzi

2005

Health Care Manage Sci

134

View full text Add to dashboard Cite

Modelling patient flow in health care systems is considered vital in understanding the system's activity and may therefore prove to be useful in improving their functionality. A measure, extensively used, is the average length of stay which, although easy to calculate and quantify, assumes normally distributed data thus making the subsequent modelling of resources totally unsuitable. In fact, simple deterministic models are generally considered inadequate, hence the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service. Running Title: Modelling Patient Flow through Hospitals

show abstract

Section: Introductionmentioning

confidence: 99%

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

Marshall

Vasilakis

El‐Darzi

2005

Health Care Manage Sci

134

View full text Add to dashboard Cite

show abstract

“…Many authors have used simulation to solve complex hospital operations problems with compelling results [19][20][21][22][23][24]. Recent advances in capturing large data is carving the way for such advanced analytical tools.…”

Section: Simulationmentioning

confidence: 99%

Modeling the critical care pathway for cardiothoracic surgery

Bahou

Fenwick

Anderson

et al. 2017

Health Care Manag Sci

View full text Add to dashboard Cite

The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increased demand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue was limited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type's pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this technique with minor capacity reallocations resulted in more than 60% drop in cancellations.

show abstract

“…Complexity also arises from uncertainty over the needs of patients (Fone et al, 2003) and from the need to address multiple, competing objectives (Harper and Shahani, 2002).…”

Section: Development Of the Questionnairementioning

confidence: 99%

Is simulation in health different?

Tako

Robinson

2015

Journal of the Operational Research Society

View full text Add to dashboard Cite

It is often stated that health simulation is quite different and even that it is more difficult than in other sectors. But, is simulation in health really different to simulation in other sectorselsewhere? In this paper we explore this question through a survey of simulation modellers and academics. We elicit their opinions across a range of factors concerning the difficulties of health modelling against modelling in other domains. The analysis considers the responses of the whole group of respondents and the sub-group of respondents who have experience both in and outside of health modelling. The results show that, overall, there is a perception that health modelling is different and that it is more difficult across a range of factors. The implications for simulation research and practice in health are discussed.

show abstract

Modelling for the planning and management of bed capacities in hospitals

Cited by 193 publications

References 0 publications

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

Modeling the critical care pathway for cardiothoracic surgery

Is simulation in health different?

Contact Info

Product

Resources

About