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Towards a model for hospital case-load decision support

D Gove¹, D Hewett, A Shahani

¹Institute of Modelling for Healthcare, Faculty of Mathematical Studies, University of Southhampton, UK.

IMA Journal of Mathematics Applied in Medicine and Biology

|September 1, 1995

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This study introduces a generalizable hospital patient flow model using National Health Service data. The simulation model aids in optimizing bed allocation and evaluating management strategies for improved hospital operations.

Area of Science:

Healthcare Operations Research
Hospital Management Systems
Health Informatics

Background:

Efficient patient flow is crucial for hospital resource management and patient care quality.
Existing models may lack generalizability or fail to capture dynamic operational aspects.
Data-driven approaches are needed to optimize hospital patient flow.

Purpose of the Study:

To develop and present an operational model for simulating patient flow within a hospital setting.
To demonstrate the model's generalizability across different hospital environments.
To provide a tool for assessing the impact of various management strategies and patient care changes.

Main Methods:

Development of an operational model based on patient flow dynamics.

Related Experiment Videos

Utilization of data from the National Health Service (NHS) minimum dataset for generalizability.

Implementation using a simulation approach on a personal computer (PC) with Microsoft Windows.

Main Results:

The model is generalizable to any hospital utilizing the NHS minimum dataset.
The model can determine optimal bed allocation based on patient arrival patterns.
The model can simulate the effects of management strategies and changes in patient care patterns.

Conclusions:

The developed operational model provides a flexible and generalizable tool for hospital patient flow analysis.
Simulation modeling is an effective approach for evaluating hospital operational efficiency and strategic planning.
The model supports informed decision-making for resource allocation and process improvement in healthcare settings.