Improving decision making in healthcare services through the use of existing simulation modelling tools and new technologies

Abstract

Purpose

The purpose of this paper is to investigate the viability of using distributed simulation to execute large and complex healthcare simulation models which help government take informed decisions.

Design/methodology/approach

The paper compares the execution time of a standalone healthcare supply chain simulation with its distributed counterpart. Both the standalone and the distributed models are built using a commercial simulation package (CSP).

Findings

The results show that the execution time of the standalone healthcare supply chain simulation increases exponentially as the size and complexity of the system being modelled increases. On the other hand, using distributed simulation approach decreases the run time for large and complex models.

Research limitations/implications

The distributed approach of executing different parts of a single simulation model over different computers is only viable when the model: can be divided into logical parts and the exchange of information between these parts occurs at constant simulated time intervals; is sufficiently large and complicated, such that executing the model over a single processor is very time consuming.

Practical implications

Based on a feasibility study of the UK National Blood Service we demonstrate the effectiveness of distributed simulation and argue that it is a vital technique in healthcare informatics with respect to supporting decision making in large healthcare systems.

Originality/value

To the best of the knowledge, this is the first feasibility study in healthcare which shows the outcome of modelling and executing a distributed simulation using unmodified CSPs and a software/middleware for distributed simulation.

Citation

Katsaliaki, K. and Mustafee, N. (2010), "Improving decision making in healthcare services through the use of existing simulation modelling tools and new technologies", Transforming Government: People, Process and Policy, Vol. 4 No. 2, pp. 158-171. https://doi.org/10.1108/17506161011047389