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Related Experiment Video

Updated: Jul 4, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

LLMs for Healthcare Process Orchestration: Promises and Challenges.

Murat Sariyar1

  • 1Bern University of Applied Sciences, Switzerland.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

This study explores integrating large language models (LLMs) and AI in healthcare workflows. It proposes an orchestration framework to match AI autonomy with process structure for optimal efficiency and evaluation.

Keywords:
BPMNCMMNHealthcare processesLarge language models (LLMs)agentic AI

Related Experiment Videos

Last Updated: Jul 4, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Area of Science:

  • Healthcare Management
  • Artificial Intelligence in Medicine
  • Process Optimization

Background:

  • Healthcare processes involve both stable workflows and unpredictable, case-dependent tasks.
  • Integrating AI, including large language models (LLMs), requires careful orchestration rather than just technical feasibility.
  • Existing evaluation metrics for healthcare processes may not adequately capture the complexities of human-AI collaboration.

Purpose of the Study:

  • To develop a framework for distinguishing between structured and case-dependent tasks in healthcare.
  • To guide the appropriate application of LLMs and autonomous AI systems within healthcare workflows.
  • To identify key challenges and propose new evaluation metrics for human-AI integrated healthcare processes.

Main Methods:

  • Conceptual review of literature on AI in healthcare processes.
  • Development of a Business Process Model and Notation (BPMN)-Case Management Model and Notation (CMMN) lens.
  • Analysis of task characteristics influencing AI autonomy and process structure.

Main Results:

  • LLMs are best suited for structured tasks like documentation, summarization, and communication.
  • More autonomous AI systems are relevant for case-dependent segments requiring context, exceptions, and judgment.
  • A significant challenge lies in evaluating processes distributed across humans and AI.

Conclusions:

  • Orchestration is key to effectively integrating LLMs and AI in healthcare.
  • New evaluation measures are needed beyond traditional KPIs to assess human-AI collaboration quality.
  • Healthcare organizations must consider supervision burden and outcome stability in AI implementation.