Afshar ambient AI (well-being)

A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being

Patient / Population Intervention / Exposition Comparaison Critère de jugement

In 66 health care practitioners at ambulatory clinics in two states, an ambient AI scribe drafting visit notes reduced work exhaustion and interpersonal disengagement (professional fulfilment did not significantly improve) compared with documentation without ambient AI before each practitioner's activation.

N
66
Schéma
Stepped-wedge, individually randomised pragmatic trial (24 weeks)
Critère
Professional fulfilment and work exhaustion/interpersonal disengagement (Stanford PFI; co-primary)
Pertinence
2
RésultatWork exhaustion/interpersonal disengagement −0.44 points (95% CI −0.62 to −0.25; P<0.001); professional fulfilment +0.14 points (95% CI 0.004–0.28; P=0.04), reported as non-significant. Time on notes −0.36 h/day (95% CI −0.55 to −0.17); 38% of 71,487 notes drafted with ambient AI.
Afshar M, et al. NEJM AI. 2025;2(12). 10.1056/aioa2500945
Discussion et critique

A randomised implementation showing that ambient AI eases the exhaustion half of burnout and shortens documentation time without degrading note quality, billing codes or diagnosis capture — the safety question that matters to health systems. Small, unblinded and survey-based for its primary outcomes; the professional-fulfilment co-primary was null, the work-outside-work saving vanished once extreme days were trimmed, and 24 weeks is short for a well-being endpoint.