Skip to main content
Documentation FreedomSystematic Review2024

Clinical Documentation Quality: AI-Generated vs Manually Authored Notes

Key Finding

Systematic reviews and early comparative studies suggest that AI-supported documentation improves structural completeness, guideline-concordant elements, and reduction of transcription errors compared with unaided manual notes, while overall clinical accuracy remains highly dependent on clinician review and sign-off. Hybrid workflows (AI draft plus physician edit) achieve the best balance, with improved completeness and fewer omissions but occasional propagation of misrecognized details if review is rushed.

8 min read2 sources cited
primary-careall

Executive Summary

A 2024 systematic review of 129 AI documentation tools found that AI can measurably improve multiple dimensions of documentation quality, including structural organization, inclusion of key guideline-recommended data elements, and detection of internal inconsistencies or missing information compared with purely manual documentation. Studies using NLP and automatic speech recognition (ASR) demonstrate reduced transcription error rates when domain-specific language models are combined with medical ASR, particularly for medication names and technical terminology, and several systems improve adherence to templates such as SOAP notes.

Head-to-head comparisons of AI-assisted vs manual notes remain limited but generally show that AI-generated drafts, when edited by clinicians, produce notes with similar or better completeness scores and fewer spelling or formatting errors, at the cost of occasional clinically irrelevant verbosity. There is little evidence that AI alone can reliably produce end-to-end notes that meet medico-legal standards without physician oversight; instead, current best practice is a supervised model in which the clinician remains accountable for factual accuracy, assessment, plan, and nuance.

Detailed Research

Methodology

Evidence on documentation quality comes primarily from a 2024 systematic review of AI tools for clinical documentation, supplemented by smaller development and validation studies of specific NLP/ASR systems. The review screened over 600 articles and included 129 studies that evaluated AI methods to structure data, annotate notes, assess quality, or generate draft documentation, focusing on quantitative metrics such as error rates, completeness scores, and concordance with reference standards.

Individual studies compare AI-assisted notes with manual notes using chart review by experts, automated quality metrics (for example, presence of key data fields), or error counts in transcription tasks. Most are observational or bench evaluations; randomized comparisons of AI-assisted vs manual documentation in real clinical workflows are scarce.

Key Studies

Systematic Review: Improving Clinical Documentation with AI (2024)

  • Design: Systematic review of 129 AI documentation tools
  • Sample: 600+ articles screened
  • Findings: AI can improve structural quality (template adherence, standard terminology) and reduce certain error types compared with manual documentation alone. However, evidence for fully autonomous note generation meeting clinical standards was lacking.
  • Clinical Relevance: Most successful implementations use AI to support, not replace, clinicians

Hybrid ASR + NLP Systems for Note Generation

  • Design: Review of 14 AI documentation studies
  • Sample: Multiple healthcare systems
  • Findings: Combining medical ASR with domain-specific NLP models significantly reduced transcription errors and improved context handling compared with baseline ASR or manual dictation. Real-time note generation using extractive–abstractive summarization plus templates produced notes rated as more readable and complete.
  • Clinical Relevance: Occasional misattribution of details persisted, requiring clinician review

Ambient AI Documentation Platform Evaluations

  • Design: Early evaluations of ambient AI tools (Abridge, DAX)
  • Sample: Multiple health systems
  • Findings: Clinician-edited AI drafts are at least as complete as manual notes and may include more detailed histories and counseling documentation, supporting coding and medico-legal robustness.
  • Clinical Relevance: Clinician surveys indicate perceived improvements in accuracy and completeness when sufficient time is allotted for review

Clinical Implications

For osteopathic physicians, AI-assisted documentation can help ensure consistent capture of elements that directly affect quality and reimbursement, such as problem lists, medication changes, and counseling details, while freeing attention for structural examination and OMT. Structured templates generated by AI can also standardize documentation of somatic dysfunction, MSK findings, and OMT techniques if appropriately configured.

Clinicians must remain vigilant about subtle factual errors (for example, misheard negations, incorrect laterality) that AI systems can introduce; a brief but systematic review process is essential before signing notes.

Limitations & Research Gaps

Most studies are technology-focused, using surrogate quality metrics rather than patient or medico-legal outcomes, and few directly compare AI-assisted notes with high-quality manual notes in randomized designs. Heterogeneity in models, specialties, and evaluation methods limits generalizability.

There is almost no osteopathy-specific research on documentation quality, including how well AI systems capture OMT-specific elements or osteopathic diagnostic reasoning. Further work should examine structured OMT templates, note quality in MSK-heavy practices, and how AI documentation influences downstream coding, quality metrics, and malpractice risk.

Osteopathic Perspective

Osteopathic principles emphasize that structure and function are interrelated and that documentation should reflect the whole person, including structural findings, psychosocial context, and therapeutic plan. AI-generated notes that overemphasize checkboxes at the expense of narrative risk undermining this ethos.

Used thoughtfully, AI can standardize essential elements while giving DOs more cognitive bandwidth to craft meaningful assessments and OMT plans, preserve the narrative of body–mind–spirit unity, and clearly describe structural findings and manual techniques in the record.

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. Leong HY, Estuar MR, Wenceslao S, et al. “Enhancing clinical documentation with AI: reducing errors and improving interoperability.” Informatics in Medicine Unlocked, 2025;40:101500. DOI: 10.1016/j.imu.2025.101500

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.