The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence

Abstract

Abstract Diagnostic errors are a substantial source of patient harm. As artificial intelligence (AI) integrates into clinical workflows, opportunities are emerging to assess their impacts on diagnostic excellence (DxEx). The Coordinating Center for Diagnostic Excellence (CODEX) at the University of California San Francisco established the Action Incubator to translate research advances in DxEx into tangible strategies for improving diagnosis. The September 2025 in-person inaugural Action Incubator convened 30 multidisciplinary stakeholders representing health systems, patient advocacy, industry, and policy groups. Through structured discussions and breakout sessions, participants identified AI scribes as a near-term, scalable use case for evaluating AI’s impact on diagnosis not only because of their widespread adoption, but – as supported by cognitive load theory – because of their potential to reduce cognitive burden and allow clinicians to focus more on diagnosis. A modified Delphi process was used to prioritize candidate measures based on feasibility, and impact. Participants generated 17 candidate measures of AI scribe impact on DxEx. Consensus was reached on two priority metrics as functions of AI scribe usage rates by primary care physicians: (1) Timely follow-up of abnormal test results related to breast and colorectal cancer screening and (2) Patient-reported diagnostic experience. Participating health systems will pilot these measures using electronic health record audit logs and patient surveys. The first CODEX Action Incubator developed a pragmatic, consensus-driven framework for measuring the impact of AI on DxEx. Future annual Action Incubators will take up timely, actionable topics related to DxEx.