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Documentation FreedomMixed Methods2025

AI-Enabled Clinical Documentation in Primary Care Workflows

Key Finding

Real-world evaluations of ambient AI scribes and documentation assistants in primary care show modest but meaningful reductions in per-encounter documentation time (≈0.5–1 minute per visit) and improved clinician-reported efficiency, without clear adverse effects on visit length or patient experience. Benefits depend heavily on workflow integration; poorly integrated tools can shift work to after-hours and blunt time savings.

7 min read2 sources cited
primary-carefamily-medicineinternal-medicine

Executive Summary

Primary care has been an early focus for AI-enabled documentation because of its high visit volumes and heavy documentation burden. Mixed-methods studies synthesizing data from multiple digital scribe deployments find that ambient AI systems in outpatient settings, including family medicine and general internal medicine, reduce in-clinic documentation time per encounter, improve perceived workflow efficiency, and are generally well accepted by clinicians and patients. A 2025 real-world evidence synthesis across several primary care implementations concluded that digital scribes "show promise in reducing documentation burden and enhancing clinician satisfaction," while emphasizing that most evidence is observational and short term.

Implementation reports describe improved alignment of documentation with visit flow, with AI capturing histories and counseling while clinicians focus on examination and relationship-building. However, when note drafts are returned with delay or outside the EHR workflow, clinicians may end up reviewing and editing them in the evening, increasing after-hours work despite reductions in active typing during the clinic day. Across studies, primary care physicians stress that AI documentation must be tightly embedded in existing workflows to yield net gains.

Detailed Research

Methodology

Evidence on AI documentation in primary care comes from a 2024 systematic review of AI documentation tools, a 2025 real-world synthesis of digital scribe implementations, and multi-system observational studies of ambient documentation in ambulatory practices. Designs include pre–post cohort studies, cross-sectional user vs non-user comparisons, and qualitative interviews addressing workflow integration, satisfaction, and perceived burden.

Outcomes typically include EHR documentation time per visit, time to note completion, burnout and satisfaction scores, and qualitative assessments of fit within primary care workflows.

Key Studies

Systematic Review: Improving Clinical Documentation with AI (2024)

  • Design: Comprehensive review of AI documentation tools
  • Sample: 129 studies across multiple settings
  • Findings: Primary care was a major implementation setting, with AI used to structure notes, support coding, and reduce manual entry. While most primary care studies were early-stage, many reported improved perceived efficiency and potential reductions in documentation burden.
  • Clinical Relevance: AI documentation shows promise but requires careful integration

Real-World Evidence Synthesis of Digital Scribes (2025)

  • Design: Synthesis of ambient digital scribe implementations
  • Sample: Multiple health systems and primary care practices
  • Findings: Digital scribes appear to reduce perceived documentation burden and improve satisfaction when integrated directly into EHR note workflows. Specialty, intensity of use, and duration of adoption influenced the magnitude of benefit.
  • Clinical Relevance: Workflow integration is critical for success

Ambient Documentation Technology in Clinician Experience (2025)

  • Design: Multi-system survey study
  • Sample: Primary care clinicians using ambient documentation
  • Findings: Reduced burnout and increased perceived well-being. Qualitative findings emphasized reduced clerical load and more patient-facing time.
  • Clinical Relevance: Requires robust training and technical support

Use of Ambient AI Scribes to Reduce Administrative Burden (2025)

  • Design: NIH-indexed implementation report
  • Sample: Ambulatory clinics
  • Findings: Clinicians received AI-drafted notes for review, with perceived reductions in administrative burden and improved note completeness.
  • Clinical Relevance: Shows feasibility in real-world settings

Clinical Implications

For osteopathic primary care, AI-enabled documentation can free cognitive and temporal bandwidth to perform structural exams and OMT during routine visits without extending clinic sessions. Capturing narrative histories and counseling via AI drafts allows DOs to focus their attention and hands on the patient.

Primary care practices should design workflows where AI-generated drafts are reviewed and signed during or immediately after the visit, minimizing after-hours documentation and aligning with team-based support (for example, MAs helping verify data) to maximize benefits.

Limitations & Research Gaps

Most primary care evidence is observational, with limited randomized comparisons and short follow-up periods. Documentation time savings are modest on a per-visit basis and may not consistently translate into large reductions in total EHR time without broader workflow changes.

There is a near-absence of osteopathy-specific data, including how AI documentation performs in OMT-heavy primary care visits or how well it captures somatic findings and manual therapies.

Osteopathic Perspective

Osteopathic primary care emphasizes longitudinal relationships and integration of structural examination and OMT into routine care; AI documentation that reduces typing and allows more face-to-face time aligns with this philosophy.

DOs should ensure that templates and vocabularies used by AI tools support detailed documentation of somatic dysfunction, OMT techniques, and functional goals, preserving the distinctiveness of osteopathic practice while leveraging AI for efficiency.

References (2)

  1. Conboy EE, McCoy AB, Wright A, 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. Tao K, Tseng C, et al. “Real-World Evidence Synthesis of Digital Scribes Using Ambient Artificial Intelligence.” JMIR AI, 2025;4:e76743. DOI: 10.2196/76743

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.