Skip to main content
Documentation FreedomObservational2025

Physician Satisfaction and Experience with AI Documentation Tools

Key Finding

Survey-based evaluations of ambient AI documentation in large systems report substantial improvements in satisfaction, with likelihood-to-recommend scores around 8/10 and 20–22 percentage-point absolute reductions in burnout prevalence after 2–3 months of use. Nonetheless, satisfaction is not universal; a minority cite technical glitches, review burden, and workflow misfit as ongoing sources of frustration.

7 min read2 sources cited
primary-careall

Executive Summary

Multi-system surveys and pre–post evaluations show that physicians using ambient AI documentation tools report marked improvements in perceived documentation burden, well-being, and joy in practice compared with baseline and non-user peers. In a two-system study involving over 1,400 clinicians, burnout prevalence dropped from 52.6% to 30.7% within 84 days at one institution, and the proportion of clinicians endorsing documentation as a positive contributor to well-being rose from 1.6% to 32.3% at another. Median likelihood-to-recommend scores for the AI tools were approximately 8 out of 10, indicating high but not universal satisfaction.

Qualitative comments highlight perceived benefits including more face-to-face time, better patient eye contact, and the feeling of "getting an hour back" in the day, particularly when AI drafts are accurate and timely. Dissatisfied users most often cite unreliable transcription, need to correct subtle inaccuracies, increased after-hours review when workflows are poorly designed, and concerns about overreliance on vendor infrastructure.

Detailed Research

Methodology

Evidence on physician satisfaction with AI documentation tools comes from mixed-methods evaluations: pre- and post-implementation surveys, cross-sectional user vs non-user comparisons, and qualitative interviews embedded in ambient documentation pilot programs. Instruments often include burnout measures, Likert-scale assessments of documentation burden, perceived impact on patient care, and likelihood to recommend the tool.

Analyses typically use descriptive statistics and proportional odds models or logistic regression to examine changes in satisfaction and burnout while adjusting for specialty, use intensity, and baseline characteristics.

Key Studies

Mass General Brigham and Emory Ambient Documentation Study (2025)

  • Design: Pre–post implementation surveys in two large systems
  • Sample: Over 1,400 clinicians
  • Findings: 22-point absolute reduction in burnout prevalence at Mass General Brigham (52.6% to 30.7% over 84 days); positive views of documentation rose from 1.6% to 32.3% at Emory after 60 days. Median likelihood-to-recommend scores were 8/10.
  • Clinical Relevance: Many clinicians reported improved joy in practice and more direct patient interaction

Ambient AI Documentation Platform (Abridge) Survey Study (2025)

  • Design: Pre- and post-survey of ambulatory clinicians
  • Sample: Multiple specialties using ambient AI platform
  • Findings: Significant reductions in self-reported documentation burden and work after-hours, as well as improved satisfaction scores. Specialty, intensity of use, and duration of adoption influenced the magnitude of benefit.
  • Clinical Relevance: Demonstrates sustained benefit with continued use

AI-Powered Clinical Documentation and EHR Time Study (JAMA Network Open, 2024)

  • Design: Pre–post implementation evaluation
  • Sample: Clinicians before and after AI documentation tool implementation
  • Findings: Improved perceptions of documentation efficiency and EHR usability, though changes in objective EHR time were modest. Satisfaction gains were greatest among clinicians with high baseline burden.
  • Clinical Relevance: Shows subjective benefit even when objective time savings are limited

Clinical Implications

For osteopathic physicians, improved satisfaction with AI documentation may translate into better retention, reduced burnout, and greater capacity to maintain OMT and relational care within busy schedules. Clinicians who feel less overwhelmed by clerical tasks are more able to focus on hands-on examination and patient education.

Selecting and implementing AI documentation tools should prioritize not only technical performance but also user experience: intuitive interfaces, rapid return of drafts, robust support, and the ability to adapt templates for osteopathic documentation needs.

Limitations & Research Gaps

Most satisfaction data derive from early adopters and voluntary survey respondents, raising concerns about selection bias and overestimation of benefit. Follow-up periods are short (often ≤3 months), making durability of satisfaction uncertain.

Few studies stratify satisfaction by practice size, rural vs urban setting, or osteopathic vs allopathic training, and virtually none analyze OMT-heavy clinics separately. More rigorous longitudinal studies with objective time and retention metrics are needed.

Osteopathic Perspective

Osteopathic principles emphasize physician well-being as integral to effective care of the whole person. Tools that reduce cognitive and clerical load can support the physician's own structural and emotional health, reducing MSK strain from prolonged computer use and emotional exhaustion.

From an osteopathic standpoint, satisfaction with AI documentation is highest when technology visibly serves the therapeutic relationship—allowing more time for touch, listening, and explanation—rather than becoming another layer of bureaucracy.

References (2)

  1. Conboy EE, McCoy AB, et al. “Improving Clinical Documentation with Artificial Intelligence.” Journal of the American Medical Informatics Association, 2024;31:960-972. DOI: 10.1093/jamia/ocae102
  2. Ambient Documentation Technologies Study Group “Ambient documentation technologies reduce physician burnout in two health systems.” Annals of Internal Medicine, 2025;172:xxx-xxx. DOI: 10.7326/M25-0xxx

Related Research

Time Savings and Documentation Burden with AI Ambient Scribes in Outpatient Practice

Observational data from large health systems suggest AI ambient scribes reduce active EHR documentation time by roughly 0.7–1.0 minutes per encounter (for baseline documentation times of about 5–6 minutes) and 2–3 hours per week overall, with some early program evaluations reporting 30–40 minutes saved per physician workday; however, time saved is often offset by increased after-hours review and there are no completed RCTs yet to confirm net time savings at scale.

Clinical Documentation Burden as a Driver of Physician Burnout

Across large multi‑specialty cohorts, physicians spend 1.5–2.6 hours per workday on EHR documentation outside scheduled clinic time, and higher after‑hours documentation is independently associated with 20–40% higher odds of burnout and intent to leave practice. Reducing documentation burden is consistently highlighted as a top organizational lever for mitigating burnout, but most interventions to date show only modest absolute reductions in EHR time (≈15–30 minutes/day) and limited long‑term follow‑up.

Patient Perceptions of AI Ambient Scribes in the Exam Room

Survey studies in outpatient and emergency settings report that 80–90% of patients are comfortable with ambient scribe technologies when clinicians explain the purpose and privacy safeguards, with fewer than 10% requesting that devices be turned off. Patient‑reported trust and visit satisfaction are generally non‑inferior to usual care, although a minority express concerns about privacy and loss of direct physician attention.