Unlocking the Potential of Past Research: Using Generative AI to Reconstruct Healthcare Simulation Models
This addresses the issue of model sharing in healthcare operations research, though it is incremental as it builds on existing AI methods for simulation.
The study tackled the problem of limited reuse of discrete-event simulation models in healthcare by using generative AI to recreate published models from journal descriptions, successfully generating and testing two complex models with partial replication of results.
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.