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Issues And Trends In Healthcare Delivery System01:29

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Understanding Physician's Perspectives on AI in Health Care: Protocol for a Sequential Multiple Assignment Randomized

Jane Paik Kim1, Hyun-Joon Yang1, Bohye Kim1

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.

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This study investigates how clinical risk, AI information, and AI results affect physician decision-making with AI clinical decision support (CDS) tools. A novel "SMART vignette" design will analyze these factors to improve AI integration in healthcare.

Keywords:
AI-based clinical decision supportdecision-makingethicshypothesis-driven researchhypothetical vignettesphysician perspectivestakeholder attitudesweb-based survey

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Artificial intelligence (AI)-based clinical decision support (CDS) systems are increasingly integrated into patient care.
  • Physicians need to understand how factors like clinical risk and AI output influence their use of these tools.
  • Hypothesis-driven research is crucial for anticipating AI's impact on frontline healthcare providers.

Purpose of the Study:

  • To design and implement a web-based vignette survey exploring AI-based CDS system applications.
  • To systematically vary factors such as clinical risk, AI information, and AI results within hypothetical scenarios.
  • To determine the causal effects of these factors on physicians' judgments and decision-making.

Main Methods:

  • Recruitment of US-based family or internal medicine physicians (target n=420).
  • Utilizing a web-based survey with a novel "sequential multiple assignment randomization trial (SMART) vignette" design.
  • Randomizing participants to vignette variations to test the impact of clinical risk, AI information, and AI output certainty.

Main Results:

  • The study is currently in progress with data collection expected to conclude in 2024.
  • Results will quantify the influence of specific contextual factors on physician decision-making when using AI-CDS.
  • The "SMART vignette" design is anticipated to yield increased analytical validity and balanced group sample sizes.

Conclusions:

  • This web-based vignette study will elucidate how clinical risk, AI information, and AI results impact physician responses to AI in healthcare.
  • The novel "SMART vignette" design offers advantages over traditional methods for analyzing AI's influence on clinical decisions.
  • Findings will enhance understanding of physician decision-making processes involving AI-based clinical decision support tools.