{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:29:29Z","timestamp":1786508969655,"version":"build-2736575974"},"reference-count":13,"publisher":"Walter de Gruyter GmbH","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u00a0in-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\u2019s impact on diagnosis not only because of their widespread adoption, but\u00a0\u2013\u00a0as supported by cognitive load theory\u00a0\u2013\u00a0because 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\u00a0DxEx.<\/jats:p>","DOI":"10.1515\/dx-2026-0112","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T03:55:46Z","timestamp":1786506946000},"source":"Crossref","is-referenced-by-count":0,"title":["The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence"],"prefix":"10.1515","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3609-6481","authenticated-orcid":false,"given":"Benjamin","family":"Rosner","sequence":"first","affiliation":[{"name":"Coordinating Center for Diagnostic Excellence, Division of Clinical Informatics and Digital Transformation , University of California San Francisco , San Francisco , CA , USA"},{"name":"Division of Hospital Medicine , University of California San Francisco , San Francisco , CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2097-2585","authenticated-orcid":false,"given":"Molly","family":"Hammer","sequence":"additional","affiliation":[{"name":"Coordinating Center for Diagnostic Excellence, Division of Clinical Informatics and Digital Transformation , University of California San Francisco , San Francisco , CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9479-8203","authenticated-orcid":false,"given":"Aaron","family":"Tabacco","sequence":"additional","affiliation":[{"name":"Department of Medicine , University of California San Francisco , San Francisco , CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0262-6491","authenticated-orcid":false,"given":"Julia","family":"Adler-Milstein","sequence":"additional","affiliation":[{"name":"Division of Clinical Informatics and Digital Transformation , University of California San Francisco , San Francisco , CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1941-0005","authenticated-orcid":false,"given":"Sumant R.","family":"Ranji","sequence":"additional","affiliation":[{"name":"Coordinating Center for Diagnostic Excellence, Division of Clinical Informatics and Digital Transformation , University of California San Francisco , San Francisco , CA , USA"},{"name":"Division of Hospital Medicine , University of California San Francisco at San Francisco General Hospital, San Francisco , CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"2026081203554099336_j_dx-2026-0112_ref_001","doi-asserted-by":"crossref","unstructured":"Singh, H, Meyer, AND, Thomas, EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 2014;23:727\u201331. https:\/\/doi.org\/10.1136\/bmjqs-2013-002627.","DOI":"10.1136\/bmjqs-2013-002627"},{"key":"2026081203554099336_j_dx-2026-0112_ref_002","doi-asserted-by":"crossref","unstructured":"Pelletier, JH, Watson, K, Michel, J, McGregor, R, Rush, SZ. Effect of a generative artificial intelligence digital scribe on pediatric provider documentation time, cognitive burden, and burnout. JAMIA Open 2025;8:ooaf068. https:\/\/doi.org\/10.1093\/jamiaopen\/ooaf068.","DOI":"10.1093\/jamiaopen\/ooaf068"},{"key":"2026081203554099336_j_dx-2026-0112_ref_003","doi-asserted-by":"crossref","unstructured":"Olson, KD, Meeker, D, Troup, M, Barker, TD, Nguyen, VH, Manders, JB, et al.. Use of ambient AI scribes to reduce administrative burden and professional burnout. JAMA Netw Open 2025;8:e2534976. https:\/\/doi.org\/10.1001\/jamanetworkopen.2025.34976.","DOI":"10.1001\/jamanetworkopen.2025.34976"},{"key":"2026081203554099336_j_dx-2026-0112_ref_004","doi-asserted-by":"crossref","unstructured":"Lukac, PJ, Turner, W, Vangala, S, Chin, AT, Khalili, J, Shih, YT, et al.. Ambient AI scribes in clinical practice: a randomized trial. NEJM AI 2025;2. https:\/\/doi.org\/10.1056\/aioa2501000.","DOI":"10.1056\/AIoa2501000"},{"key":"2026081203554099336_j_dx-2026-0112_ref_005","unstructured":"Knees, M, Raffel, KE, Kissler, M, Burden, M, Porter, S, Schnipper, J, et al.. Cognitive load theory and its impact on diagnostic accuracy; 2024. Available at: http:\/\/file:\/\/\/Users\/brosner\/Downloads\/dxsafety-cognitive-load-theory%20(2).pdf."},{"key":"2026081203554099336_j_dx-2026-0112_ref_006","unstructured":"Aspen Institute. Hatching new ideas to deliver health care to the last mile\u00a0\u2013\u00a0Aspen Institute. Ideas to action incubator; 2015. Available at: https:\/\/www.aspeninstitute.org\/blog-posts\/hatching-new-ideas-deliver-health-care-last-mile\/."},{"key":"2026081203554099336_j_dx-2026-0112_ref_007","unstructured":"Aspen Institute. Rural action roundtable on equity\u00a0\u2013\u00a0Aspen CSG. Rural action roundtable on equity; 2022. Available at: https:\/\/www.aspencsg.org\/rural-action-roundtable-on-equity\/."},{"key":"2026081203554099336_j_dx-2026-0112_ref_008","doi-asserted-by":"crossref","unstructured":"Nong, P, Neprash, HT. Unintended consequences of using ambient artificial intelligence scribes for billing. JAMA Health Forum 2026;7:e255771. https:\/\/doi.org\/10.1001\/jamahealthforum.2025.5771.","DOI":"10.1001\/jamahealthforum.2025.5771"},{"key":"2026081203554099336_j_dx-2026-0112_ref_009","unstructured":"Agency for Healthcare Research and Quality. Diagnostic safety supplemental items for medical office SOPS | Agency for Healthcare Research and Quality. 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