doi:10.1007/s10626-025-00419-4engLe Moigne, ManonMahulea, CristianFaraut, GregoryBernardi, SimonaAlbareda, JorgeCastán, LidiaProbabilistic timed Petri nets for clinical pathway design and analysis: a case studyART-2025-145538The COVID-19 pandemic underscored the need for efficient hospital resource management and standardized patient care. Clinical pathways, structured plans for managing specific conditions, are critical but challenging to design and update. This paper introduces a methodology combining pattern mining and Probabilistic Timed Petri Nets (PTPN) to model, simulate, and evaluate clinical pathways, incorporating probabilistic transitions and continuous-time distributions for activity durations. In collaboration with medical professionals, the proposed methodology has been successfully applied to develop a clinical pathway for anterior cruciate ligament (ACL) ruptures using real data. Iterative refinement addressed data inconsistencies, producing a robust PTPN model that optimizes patient care and resource allocation. Validation using the CASAS database demonstrate the adaptability of the method to different healthcare contexts.2025http://zaguan.unizar.es/record/16300310.1007/s10626-025-00419-4http://zaguan.unizar.es/record/163003oai:zaguan.unizar.es:163003info:eu-repo/grantAgreement/EUR/AEI/TED2021-130449B-I00info:eu-repo/grantAgreement/ES/MICINN/PID2020-113969RB-I00info:eu-repo/grantAgreement/ES/MICINN/PID2021-125514NB-I00DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS 35, 3 (2025), 205-231byhttps://creativecommons.org/licenses/by/4.0/deed.esinfo:eu-repo/semantics/openAccess