Accepted for/Published in: JMIR Research Protocols
Date Submitted: Nov 21, 2023
Date Accepted: Feb 6, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Study Protocol for a Sequential Multiple Assignment Randomized Vignette Study to Assess Physician Perspectives on AI in Health Care
ABSTRACT
Background:
As the availability and performance of AI-based clinical decision support (CDS) systems improve, physicians and other care providers poised to be on the front lines will be increasingly tasked with using these tools in patient care and incorporating their outputs into clinical decision-making processes. Vignette studies provide a means to explore emerging hypotheses regarding how factors may impact physician acceptance and usage of AI-based CDS tools, or to generate new hypotheses regarding how individuals may react to specific scenarios involving new applications where much is unknown. To best anticipate the decision making of physicians in clinical scenarios involving AI decision support tools, hypothesis-driven research is needed that enables scenario testing before the implementation and deployment of these tools.
Objective:
This paper describes a research protocol for an online vignette-based survey study that aims to understand predictors and causal factors of physician decision-making in the context of AI decision support tools. This paper focuses on the implementation of an original online survey that utilizes a novel sequential randomization technique.
Methods:
U.S.-based physicians who are listed in the most recent version of the American Medical Association (AMA) Physician Masterfile (PMF) will be recruited via email and mail (target n=420). Via an online survey, participants will respond to questionnaires regarding their demographics, professional experience, and attitudes toward/experience with AI/ML in medicine. They will then be randomly assigned, at three time points, to a three-part hypothetical vignette detailing a clinical scenario involving an AI decision support tool. Participants will be asked to respond to questions regarding their hypothetical decision-making as it relates to clinical risk, the amount of information provided about the AI, and the AI result, as described in the vignette.
Results:
The study is currently in progress and data collection is anticipated to be completed in 2024.
Conclusions:
The online vignette study will provide information on how physicians may react to hypothetical scenarios that are based on emerging applications of AI/ML in health care settings. Our rationale for focusing on physicians is that these individuals will be faced with incorporating AI/ML algorithms to inform the evaluation and management of patients. This study will generate a better understanding of physician decision-making.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.