Bibliographic Details
| Title: |
On simulation reuse in healthcare applications. |
| Authors: |
Zschaler, Steffen, Mustafee, Navonil, Harper, Alison, Monks, Thomas, Onggo, Bhakti Stephan, Currie, Christine S. M., Polack, Fiona |
| Source: |
Simulation; Feb2026, Vol. 102 Issue 2, p149-165, 17p |
| Subject Terms: |
Computer simulation, Health care industry, Operations research, Process optimization, Open scholarship, Simulation methods & models, Modeling languages (Computer science) |
| Abstract: |
Simulation remains a promising technology in healthcare operations research and process optimisation. However, while there have been many research projects applying simulation in this context, the level of sustained uptake in healthcare practice has been lower. We conjecture that an important reason for this is the time, cost and complexity of developing simulation models. Therefore, being able to reuse models would be key to improving uptake of simulation in healthcare. Conventional practice is that simulation models are developed from scratch for every new problem. In this paper, we review current strategies for model reuse in the healthcare context, aiming to identify complementary techniques for model reuse available to healthcare modellers and managers. Specifically, we identify three different modes of model reuse – forming a triadic framework – each prioritising a different aspect: FAIR and open-science aspects of model reuse; reusable conceptual simulation domains through modelling languages and transformations; and black-box model(-component) reuse including distributed simulation. We show how these three perspectives complement and enhance each other. We believe that developing concrete mechanisms and tools for leveraging the relationships between the three different modes of model reuse will be key to increasing the uptake of simulation modelling in healthcare practice. [ABSTRACT FROM AUTHOR] |
|
Copyright of Simulation is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |