A Preliminary Predictive Simulation Model for Hip and Knee Replacement Profile-Dependent Pathway Stages

Total hip and knee arthroplasty (THA/TKA) are commonly performed on elderly individuals, involving a lengthy clinical pathway that includes preoperative, operative, and rehabilitation phases. Despite efforts to improve patient satisfaction, there is a lack of personalized studies that optimize the THA/TKA pathway. Our aim is to address this gap by proposing a predictive simulation model that considers patient-specific factors to enhance patient satisfaction and organizational efficiency. To achieve this, we propose using process mining techniques to analyze the French national healthcare database and distinguish between standard care phases and patient-dependent phases. We then apply machine learning algorithms to predict specific stages of care. The insights gained from these analyses are used to compare and test predicted patient pathways and their performances using a hybrid simulation model.