Healthcare AI Treatment Decision Support: Design Principles to Enhance Clinician Adoption and Trust

Abstract

Artificial intelligence (AI) supported clinical decision support (CDS) technologies can parse vast quantities of patient data into meaningful insights for healthcare providers. Much work is underway to determine the technical feasibility and the accuracy of AI-driven insights. Much less is known about what insights are considered useful and actionable by healthcare providers, their trust in the insights, and clinical workflow integration challenges. Our research team used a conceptual prototype based on AI-generated treatment insights for type 2 diabetes medications to elicit feedback from 41 U.S.-based clinicians, including primary care and internal medicine physicians, endocrinologists, nurse practitioners, physician assistants, and pharmacists. We contribute to the human-computer interaction (HCI) community by describing decision optimization and design objective tensions between population-level and personalized insights, and patterns of use and trust of AI systems. We also contribute a set of 6 design principles for AI-supported CDS.