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Using AI-generated suggestions from ChatGPT to optimize clinical decision support.
Liu, Siru; Wright, Aileen P; Patterson, Barron L; Wanderer, Jonathan P; Turer, Robert W; Nelson, Scott D; McCoy, Allison B; Sittig, Dean F; Wright, Adam.
Afiliación
  • Liu S; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Wright AP; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Patterson BL; Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Wanderer JP; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Turer RW; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Nelson SD; Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • McCoy AB; Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Sittig DF; Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Wright A; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
J Am Med Inform Assoc ; 30(7): 1237-1245, 2023 06 20.
Article en En | MEDLINE | ID: mdl-37087108
OBJECTIVE: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions. METHODS: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy. RESULTS: Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy. CONCLUSION: AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sistemas de Apoyo a Decisiones Clínicas / Aprendizaje del Sistema de Salud Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Am Med Inform Assoc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sistemas de Apoyo a Decisiones Clínicas / Aprendizaje del Sistema de Salud Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Am Med Inform Assoc Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido