Unlocking the potential of past research: using generative AI to reconstruct healthcare simulation models

Discrete-event simulation (DES) is widely used in healthcare Operations Research, but the models themselves are rarely shared. This limits their potential for reuse and long-term impact in the modelling and healthcare communities. This study explores the feasibility of using generative artificial intelligence (AI) to recreate published models using Free and Open Source Software (FOSS), based on the descriptions provided in an academic journal. Using a structured methodology, we successfully generated, tested and internally reproduced two DES models, including user interfaces. The reported results were replicated for one model, but not the other, likely due to missing information on distributions. These models are substantially more complex than AI-generated DES models published to date. Given the challenges we faced in prompt engineering, code generation, and model testing, we conclude that our iterative approach to model development, systematic comparison and testing, and the expertise of our team were necessary to the success of our recreated simulation models.

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Publication Details

Journal
Journal of the Operational Research Society
Published
2025-09-10
DOI
https://doi.org/10.1080/01605682.2025.2554751
Citations
4
Primary Topic
Healthcare Operations and Scheduling Optimization
Type
article
Field-Weighted Citation Impact
10.07
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article

Unlocking the potential of past research: using generative AI to reconstruct healthcare simulation models

Alison L. Harper, Thomas Monks, Amy Heather
4 citations
Journal of the Operational Research Society
Healthcare Operations and Scheduling Optimization
10.07
article

Unlocking the potential of past research: using generative AI to reconstruct healthcare simulation models

Alison L. Harper, Thomas Monks, Amy Heather
article en
4 citations

Abstract

Discrete-event simulation (DES) is widely used in healthcare Operations Research, but the models themselves are rarely shared. This limits their potential for reuse and long-term impact in the modelling and healthcare communities. This study explores the feasibility of using generative artificial intelligence (AI) to recreate published models using Free and Open Source Software (FOSS), based on the descriptions provided in an academic journal. Using a structured methodology, we successfully generated, tested and internally reproduced two DES models, including user interfaces. The reported results were replicated for one model, but not the other, likely due to missing information on distributions. These models are substantially more complex than AI-generated DES models published to date. Given the challenges we faced in prompt engineering, code generation, and model testing, we conclude that our iterative approach to model development, systematic comparison and testing, and the expertise of our team were necessary to the success of our recreated simulation models.

Journal of the Operational Research Society
University of Exeter (GB)
Openalex Percentile: Top 2%
Healthcare Operations and Scheduling Optimization
10.07
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Unlocking the potential of past research: using generative AI to reconstruct healthcare simulation models — Alison L. Harper, Thomas Monks, et al. · Journal of the Operational Research Society (2025) | TGRS Research Map | TGRS