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

Ramon Pi-Rusiñol, Evert Verhagen, Miriam Blanch, Gil Rodas Font

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE: This 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.

DESIGN: Our 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.

RESULTS: We 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.

CONCLUSIONS: This 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.

Original languageEnglish
Article numbere001890
Number of pages7
JournalBMJ Open Sport & Exercise Medicine
Volume10
Issue number2
DOIs
Publication statusPublished - 2024

Bibliographical note

© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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