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Using AI-generated suggestions from ChatGPT to optimize clinical decision support.

Siru Liu1, Aileen P Wright1,2, Barron L Patterson3

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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|April 22, 2023
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Summary
This summary is machine-generated.

ChatGPT can generate valuable suggestions for improving clinical decision support (CDS) alerts. AI-generated ideas offer unique perspectives and are understandable, complementing human expertise in optimizing healthcare logic.

Keywords:
artificial intelligenceclinical decision supportlarge language model

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Health Systems Research

Background:

  • Clinical decision support (CDS) systems are crucial for enhancing patient care.
  • Optimizing CDS alert logic is an ongoing challenge for healthcare providers.
  • Human-generated suggestions for CDS improvement can be resource-intensive.

Purpose of the Study:

  • To evaluate ChatGPT's ability to generate useful suggestions for improving CDS logic.
  • To compare the noninferiority of AI-generated suggestions against human-generated ones.
  • To assess the quality and characteristics of AI-generated CDS improvement suggestions.

Main Methods:

  • Summaries of CDS logic were provided to ChatGPT for suggestion generation.
  • Human clinician reviewers evaluated both AI-generated and human-generated suggestions.
  • Suggestions were rated on usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy.

Main Results:

  • Of the top 20 suggestions, 9 were generated by ChatGPT.
  • AI-generated suggestions provided unique perspectives and were highly understandable and relevant.
  • AI suggestions showed moderate usefulness but low acceptance, bias, inversion, and redundancy.

Conclusions:

  • AI-generated suggestions can complement human efforts in optimizing CDS alerts.
  • ChatGPT demonstrates potential for improving CDS alert logic and other complex medical areas.
  • This approach represents a step towards advanced learning health systems.