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Barry B, Zhu X, Behnken EM, Inselman J, Schaepe K, McCoy RG, Rushlow D, Noseworthy PA, Richardson JP, Curtis S, Sharp R, Misra A, Akfaly A, Molling P, Bernard M, Yao X
Provider Perspectives on Artificial Intelligence–Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study
Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care
Barbara Barry;
Xuan Zhu;
Emma M. Behnken;
Jonathan Inselman;
Karen Schaepe;
Rozalina G. McCoy;
David Rushlow;
Peter A. Noseworthy;
Jordan P. Richardson;
Susan Curtis;
Richard Sharp;
Artika Misra;
Abdulla Akfaly;
Paul Molling;
Matthew Bernard;
Xiaoxi Yao
ABSTRACT
Background:
The promise of Artificial Intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may suffer from barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine.
Objective:
To describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use.
Methods:
A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial (RCT) in a large academic medical center in the United States. Twenty-nine primary care providers were purposively sampled using positive deviance approach for participation in semi-structured focus groups after their use of the AI tool in the RCT was complete. Focus group data was analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings.
Results:
Findings revealed that providers understood the purpose and functioning of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool’s promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication.
Conclusions:
Implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable useful adoption of AI tools at the point of care. Clinical Trial: ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087
Citation
Please cite as:
Barry B, Zhu X, Behnken EM, Inselman J, Schaepe K, McCoy RG, Rushlow D, Noseworthy PA, Richardson JP, Curtis S, Sharp R, Misra A, Akfaly A, Molling P, Bernard M, Yao X
Provider Perspectives on Artificial Intelligence–Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study