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.

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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

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article

HealthProcessAI: a technical framework and proof-of-concept for LLM-enhanced healthcare process mining

Fernando Seoane, Farhad Abtahi, K.C. Chen, Eduardo Illueca-Fernandez
Frontiers in Artificial Intelligence
Business Process Modeling and Analysis
article

HealthProcessAI: a technical framework and proof-of-concept for LLM-enhanced healthcare process mining

Fernando Seoane, Farhad Abtahi, K.C. Chen, Eduardo Illueca-Fernandez
article en

Abstract

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.

Frontiers in Artificial IntelligenceVol. 9
Karolinska University Hospital (SE), Karolinska Institutet (SE), KTH Royal Institute of Technology (SE), University of Borås (SE)
Karolinska Institutet, EIT Health
Openalex Percentile: Top 100%
Business Process Modeling and Analysis
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