Introduction:
Healthcare organizations face increasing challenges in optimizing clinical processes and deriving value from big data in health records. The PATHWAYS EIT Health project aimed to train healthcare professionals in Process Mining (PM) techniques to address these challenges through a learning-by-doing approach. The project focused on training hospital managers and directors to leverage PM as an advanced big data analytics tool for analyzing healthcare processes across multiple areas including emergency room operations and disease management.

Methodology:
The training program was implemented across three main stages:
1) Intensive Learning through face-to-face seminars about PM,
2) Hands-on implementation using real clinical data with expert guidance, and
3) Data Rodeos for result assessment and case presentations.
The program was deployed at three primary sites: Centro Hospitalar e Universitário de Coimbra (CHUC) in Portugal, Hospital Universitario Dr. Peset (HUDP) in Spain, and Karolinska University Hospital (KUH) in Sweden. In addition, due to the interest generated, it was implemented on three other sites: Hospital General de Valencia (HUGV) in Spain, Fundación San Juan de Dios (FSJD) in Spain and Associação Portuguesa de Administradores Hospitalares (APAH) in Portugal. Participant satisfaction was measured through surveys using a 5-point Likert scale across six key dimensions.

Results:
The program successfully recruited and trained diverse healthcare professionals, including hospital managers, clinicians, and data scientists. Of 95 learners, 47,4% completed the course, corresponding to CHUC and CHUGV sites. The first one, with 26 participants, was trained with an average exam score 8/10. The hands-on phase involved five teams working on specific clinical problems using PM, such as analyzing Segment Elevation Myocardial Infarction patient pathways and hospital readmission risks. The second one attracted 19 healthcare professionals, with an average exam score of 8,6/10, where three out of four teams produced valuable insights in triage time optimization, age-related emergency service patterns, and process improvements in trauma care. Participant satisfaction surveys showed consistently positive results across all sites, with particularly high scores for PM utility and data scientist involvement in hospital practice.

Conclusions:
The PATHWAYS project successfully implemented Process Mining training in healthcare through a structured learning-by-doing approach, revealing key insights about program flexibility and participant motivation. Course scheduling adaptability proved crucial, exemplified by KUH's one-on-one tutorials, while competitive elements and monetary prizes enhanced engagement and project quality. Despite varying participation levels across locations, highlighting the need to consider local organizational cultures, the project effectively bridged the gap between big data analytics and healthcare process improvement. High satisfaction rates and successful practical applications of PM to real clinical challenges established a valuable framework for future healthcare management education initiatives, emphasizing the importance of institutional support, practical application, and participant engagement.

Keywords: Education, Process Mining, Healthcare.