Validation of a Hospital Digital Twin with Machine Learning
This addresses validation issues for digital twins in healthcare, which could help hospitals optimize processes, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the challenge of validating a hospital digital twin by developing an agent-based simulation model to determine bed turnaround time, using machine learning for validation and sensitivity analysis as a work in progress.
Recently there has been a surge of interest in developing Digital Twins of process flows in healthcare to better understand bottlenecks and areas of improvement. A key challenge is in the validation process. We describe a work in progress for a digital twin using an agent based simulation model for determining bed turnaround time for patients in hospitals. We employ a strategy using machine learning for validating the model and implementing sensitivity analysis.