HealthProcessAI: a technical framework and proof-of-concept for LLM-enhanced healthcare process mining
HealthProcessAI provides a standardized framework that reduces technical and training barriers in healthcare process mining while maintaining scientific objectivity. By integrating multiple LLMs for automated interpretation and report generation, the framework addresses widespread unfamiliarity with process mining outputs, demonstrating technical feasibility for making them more accessible to clinicians, data scientists and researchers pending clinical validation. This structured analytics and AI-driven interpretation combination represents a novel methodological advance in translating complex process mining results into potentially actionable insights for healthcare applications. However, future work should involve systematic evaluation by clinicians.
Authors
- Fernando Seoane (ORCID: https://orcid.org/0000-0002-6995-967X)
- Farhad Abtahi (ORCID: https://orcid.org/0000-0001-7807-8682)
- K.C. Chen (ORCID: https://orcid.org/0000-0003-1254-9597)
- Eduardo Illueca-Fernandez
Institutions
- Karolinska University Hospital (SE)
- Karolinska Institutet (SE)
- KTH Royal Institute of Technology (SE)
- University of Borås (SE)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-01-30
- DOI
- https://doi.org/10.3389/frai.2026.1716819
- Primary Topic
- Business Process Modeling and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Karolinska Institutet
- EIT Health