Process mining to investigate the relationship between clinical antecedents and injury risk, severity and return to play in professional sports

Abstract

ObjectiveThis paper presents an exploratory case study focusing on the applicability and value of process mining in a professional sports healthcare setting. We explore whether process mining can be retrospectively applied to readily available data at a professional sports club (Football Club Barcelona) and whether it can be used to obtain insights related to care flows.DesignOur study used discovery process mining to detect patterns and trends in athletes’ Post-Pre-Participation Medical Evaluation injury route, encompassing five phases for analysis and interpretation.ResultsWe examined preprocessed data in event log format to determine the injury status of athletes in respective baseline groups (healthy or pathological). Our analysis found a link between thigh muscle injuries and later ankle joint problems. The process model found three loops with recurring injuries, the most common of which were thigh muscle injuries. There were no differences in injury rates or the median number of days to return to play between the healthy and pathological groups.ConclusionsThis study explored the applicability and value of process mining in a professional sports healthcare setting. We established that process mining can be retrospectively applied to readily available data at a professional sports club and that this approach can be used to obtain insights related to sports healthcare flows.