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TRUVACE RECORD VERSION
record: TRV-2026-0751
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-14T06:21:18.450107Z
status: published
lens: g_space
sector: health
headline: The CODEX action incubator: a consensus-driven approach to identify and implement diagnostic excellence measures in the context of artificial intelligence
dek: 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 multidiscip…
gain_title: The CODEX Action Incubator at UCSF convened 30 stakeholders and reached consensus on two priority metrics of AI scribe usage by primary care physicians to assess diagnostic excellence, including timely follow-up of abnormal breast and colorectal cancer screening results.
problem_title: (none)
trace_subject: (none)
gain_reading: The CODEX Action Incubator at UCSF convened 30 stakeholders and reached consensus on two priority metrics of AI scribe usage by primary care physicians to assess diagnostic excellence, including timely follow-up of abnormal breast and colorectal cancer screening results.
gain_evidence: Timely follow-up of abnormal test results related to breast and colorectal cancer screening
problem_reading: (none)
problem_evidence: (none)
quick_read: On September 2025, the CODEX Action Incubator at UCSF brought together 30 stakeholders from health systems, patient advocacy, industry and policy to address how to measure AI's effect on diagnostic excellence. Participants focused on AI scribes and, via a modified Delphi process, narrowed 17 candidate measures to two priority metrics tied to primary care physician usage rates: timely follow-up of abnormal breast and colorectal cancer screening results and patient-reported diagnostic experience.

The consensus framework matters because diagnostic errors are described as a substantial source of patient harm and AI scribes are already widely adopted, creating an opportunity to test whether reducing clinician cognitive burden improves diagnosis. As of the August 2026 publication date, the measures had not yet produced results; participating health systems plan to pilot them using EHR audit logs and patient surveys, leaving actual impact on follow-up timeliness and patient experience still to be demonstrated.
limitation: 
tag: Evidence-backed gain
key_points: The September 2025 inaugural Action Incubator convened 30 multidisciplinary stakeholders representing health systems, patient advocacy, industry, and policy groups. | Participants generated 17 candidate measures of AI scribe impact on diagnostic excellence and prioritized two using a modified Delphi process based on feasibility and impact. | Participating health systems will pilot the two priority metrics using electronic health record audit logs and patient surveys.
rundown: The Coordinating Center for Diagnostic Excellence at the University of California San Francisco established the Action Incubator to translate research advances into strategies for improving diagnosis, with the September 2025 in-person meeting focused on AI scribes as a near-term, scalable use case due to widespread adoption.

Through structured discussions and breakout sessions, the group identified AI scribes as relevant to diagnostic excellence as supported by cognitive load theory, and selected two metrics as functions of AI scribe usage rates by primary care physicians for future piloting.
sources:
- peer_reviewed | Diagnosis | https://doi.org/10.1515/dx-2026-0112 | 2026-08-13
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