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

Background: Process mining has emerged as a powerful analytical technique for understanding complex healthcare workflows. However, its application faces significant barriers, including technical complexity, a lack of standardized approaches, and limited access to practical training resources. To address unfamiliarity and improve accessibility, we proposed a new framework for translating technical analyses into text outputs that users can understand. Objective: We introduce HealthProcessAI, a GenAI framework designed to simplify process mining applications in healthcare and epidemiology by providing a comprehensive wrapper around existing Python (PM4PY) and R (bupaR) libraries. To address unfamiliarity and improve accessibility, the framework integrates multiple Large Language Models (LLMs) for automated process map interpretation and report generation, helping translate technical analyses into outputs that diverse users can readily understand. Methods: HealthProcessAI implements modular architecture with the following components: (1) data loading and preparation, (2) process mining analysis, (3) integration of LLM for interpretation, (4) advanced analytics, (5) multimodal report orchestration, and (6) the validation framework. We validated the framework using sepsis progression data as a proof-of-concept example and compared the outputs of five state-of-the-art LLM models through the OpenRouter platform. This study presents a technical validation using automated LLM evaluation, and clinical validation by healthcare professionals is planned as future work. Results: To test its functionality, the framework successfully processed sepsis data across four proof-of-concept cases. A total of 32 reports were generated, demonstrating robust technical performance and its capability to generate reports through automated LLM analysis. In concrete terms, there are eight reports per case and four reports per LLM model. LLM evaluation using seven independent LLMs as automated evaluators revealed distinct model strengths: Claude Sonnet-4 and Gemini 2.5-Pro achieved the highest consistency scores (3.72/4.0 and 3.49/4.0) when evaluated by automated LLM assessors. It is important to note that outputs were not clinically validated by healthcare professionals. Conclusion: 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.