Artificial intelligence (AI) is proliferating, and many clinician-facing AI technologies are being adopted in health care. Examples of these include AI-powered scribes, AI-drafted replies to patients’ electronic health record (EHR) messages, AI electronic medical record summarization, AI tools that read radiographs, and AI-enabled clinical decision support tools. Important aims of these technologies include decreasing administrative burdens, improving clinician workflows, and enhancing clinician decision-making.
Early evaluations of these technologies have largely assessed the accuracy of their outputs and their impact on clinician time, experience, and decision-making, with mixed results.1–4 These results have left health care system leaders unmoored as they struggle to determine which tools are worth the cost—in money, time, and political capital. These trade-offs are often framed in terms of return on investment (ROI).
