Impact of Unrestricted Artificial Intelligence Scribe Deployment in a Single Health System on Clinician Workload and Productivity
BACKGROUND: Ambient artificial intelligence (AI) medical scribes may reduce documentation burden and clinician burnout. Whether use intensity drives benefits remains uncertain. OBJECTIVE: To evaluate the association between AI scribe (Heidi) enrollment and documentation burden and whether changes differ by use intensity. DESIGN: Retrospective quality improvement study using an interrupted time series (ITS) indexed on each clinician's enrollment month adjusted for patient demographics and visit complexity. A secondary 3-group ITS compared enrolled nonusers, low users, and high users. SETTING: 14-hospital health system in Boston, Massachusetts. PARTICIPANTS: 902 outpatient clinicians enrolled in Heidi; 605 users and 297 enrolled nonusers. INTERVENTION: Heidi enrollment after system-wide implementation beginning January 2025. MEASUREMENTS: The primary outcome was note-active time. Secondary outcomes included same-day and after-hours note closures, note length, and work relative value units (wRVUs). RESULTS: 1 637 227 encounters had notes authored by enrolled clinicians between 1 July 2024 and 31 January 2026. Enrollment was associated with an immediate reduction in note-active time (-1.21 minutes [95% CI, -1.74 to -0.68 minutes]), with larger reductions among high users (-2.50 minutes [CI, -3.55 to -1.45 minutes]) than nonusers (difference, -2.20 minutes [CI, -3.46 to -0.95 minutes]) and low users (difference, -1.72 minutes [CI, -3.03 to -0.40 minutes]). High users had greater increases in note length than nonusers (difference, +170.90 characters [CI, 21.07 to 320.70 characters]) and low users (difference, +270.70 characters [CI, 131.04 to 410.36 characters]). No differences were observed in same-day closure, after-hours closure, or wRVUs. LIMITATION: Observational design with potential unmeasured confounding; inability to distinguish AI-scribed from clinician-authored notes postenrollment. CONCLUSION: Heidi enrollment was associated with reductions in documentation time and increases in note length, concentrated among high users. Enrollment was not associated with improvements in same-day, after-hours note closures, or wRVUs. PRIMARY FUNDING SOURCE: None.
Authors
- Stephanie Li (ORCID: https://orcid.org/0000-0003-1993-6530)
- Ashley L. O’Donoghue (ORCID: https://orcid.org/0000-0001-7401-5692)
- David M. Rubins (ORCID: https://orcid.org/0000-0003-1636-5779)
- Robert P. Fields
- Steven Horng (ORCID: https://orcid.org/0000-0002-0958-1820)
- Jennifer P. Stevens (ORCID: https://orcid.org/0000-0002-9880-9662)
- Ryan M Gould
- Venkat Jegadeesan
- Nancy Cibotti (ORCID: https://orcid.org/0009-0003-5503-6887)
- Ashley Simrak
- Divya Narayan
- Sara E. Booth
Institutions
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
Publication Details
- Journal
- Annals of Internal Medicine
- Published
- 2026-10-26
- DOI
- https://doi.org/10.7326/annals-25-05294
- Citations
- 1
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 7.62