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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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HealthProcessAI: a technical framework and proof-of-concept for LLM-enhanced healthcare process mining.

Eduardo Illueca-Fernandez1, Kaile Chen1,2, Fernando Seoane1,3,4,5

  • 1Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.

Frontiers in Artificial Intelligence
|February 16, 2026
PubMed
Summary
This summary is machine-generated.

HealthProcessAI simplifies healthcare process mining using GenAI, translating complex analyses into understandable reports. This framework enhances accessibility for clinicians and researchers, pending clinical validation.

Keywords:
epidemiologygenerative AIhealthcare analyticslarge language modelsprocess mining

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Area of Science:

  • Healthcare Analytics
  • Process Mining
  • Artificial Intelligence

Background:

  • Process mining in healthcare faces barriers: technical complexity, lack of standardization, and limited training.
  • Existing methods often produce complex outputs, hindering accessibility for diverse users.

Purpose of the Study:

  • Introduce HealthProcessAI, a GenAI framework to simplify process mining in healthcare.
  • Enhance accessibility by translating technical analyses into understandable text outputs.
  • Integrate Large Language Models (LLMs) for automated interpretation and report generation.

Main Methods:

  • Developed HealthProcessAI with a modular architecture: data loading, process mining, LLM integration, advanced analytics, report orchestration, and validation.
  • Utilized sepsis progression data for proof-of-concept validation.
  • Compared five state-of-the-art LLM models via the OpenRouter platform for technical validation.

Main Results:

  • Successfully processed sepsis data across four proof-of-concept cases, generating 32 reports.
  • Automated LLM evaluation showed Claude Sonnet-4 and Gemini 2.5-Pro achieved highest consistency scores.
  • Demonstrated robust technical performance in generating reports through automated LLM analysis.

Conclusions:

  • HealthProcessAI reduces technical and training barriers in healthcare process mining.
  • The framework shows technical feasibility for making process mining insights more accessible.
  • Future work requires systematic clinical validation by healthcare professionals.