Perceived Potential and Challenges of Supporting Coronary Artery Disease Treatment Decisions with Artificial Intelligence: Qualitative Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Coronary revascularization decision-making for patients with coronary artery disease (CAD) can be complex and challenging. Artificial intelligence (AI) has the potential to improve this decision-making by bringing data-driven insights to the point of care. </sec> <sec> <title>OBJECTIVE</title> To elicit, collect, and analyze various stakeholders’ perceived potential and challenges related to developing, implementing, and adopting AI-based CAD treatment decision support systems. </sec> <sec> <title>METHODS</title> A World Café was conducted with additional one-on-one interviews with general cardiologists, interventional cardiologists, cardiac surgeons, patients, caregivers, health system administrators, and industry representatives. Perceived potential and challenges of AI-based CAD treatment decision support systems were solicited by asking participants three broad questions: 1) what is most challenging about revascularization decision-making? 2) how could an AI tool be integrated into the existing clinical workflow? 3) what are critical components that need to be considered when developing the AI tool? Thematic analysis was performed to identify themes from the data. </sec> <sec> <title>RESULTS</title> There were nine participants in the World Café and three additional one-on-one interviews. Five main themes emerged: 1) evidence-based care, 2) workload and resources, 3) data requirements (subthemes: patient-centered approach; evidence-based AI; data integration), 4) tool characteristics (subthemes: end-user built; generation and presentation of decision support information; user-friendliness and accessibility; system logic, reasoning, and data privacy), and 5) incorporation into clinical workflow (subthemes: AI as an opportunity to improve care; knowledge translation). </sec> <sec> <title>CONCLUSIONS</title> While healthcare providers aim to provide evidence-based care, CAD treatment decision-making can often be subjective due to the limited applicability of clinical practice guidelines and randomized controlled trial evidence to individual patients. AI-based clinical decision support systems may be an effective solution if the development and implementation focus on the issues identified by end-users in this study (patient preference, data privacy, integration with clinical information systems, transparency, and usability). </sec>

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