AI Solutions for OMT and Musculoskeletal (MSK) Documentation Challenges

Executive Summary

Detailed Research

Methodology

This overview draws on systematic reviews of AI documentation tools and policy statements on AI in osteopathic medicine, which collectively reveal an absence of OMT-specific AI documentation research. Searches of major medical and informatics journals identify no trials or observational studies focusing explicitly on AI-assisted OMT or MSK documentation.

Existing AI documentation platforms are evaluated mostly in general primary care, specialty, and hospital settings without detailed analysis of OMT content or osteopathic diagnostic frameworks.

Key Studies

Systematic Review: Improving Clinical Documentation with AI (2024)

• Design: Comprehensive review of AI documentation tools
• Sample: 129 studies
• Findings: No models or evaluations tailored to OMT or osteopathic MSK documentation were identified, highlighting a blind spot in current research.
• Clinical Relevance: Gap in osteopathic-specific AI development

AOA Artificial Intelligence in Healthcare Policy (2024)

• Design: Professional society position statement
• Sample: Policy guidance for osteopathic physicians
• Findings: Explicitly notes opportunities to apply AI to enhance, rather than dilute, documentation of osteopathic diagnostic and treatment approaches. Calls for alignment with osteopathic principles.
• Clinical Relevance: Provides framework for DO-led AI development

General AI Documentation Platforms

• Design: Various implementations
• Sample: Multiple health systems
• Findings: Ambient documentation and AI scribe platforms allow customization of templates and phrases, which could theoretically support OMT documentation, but peer-reviewed evaluations of such customizations are absent.
• Clinical Relevance: Adaptation possible but unstudied

Clinical Implications

In current practice, osteopathic physicians must manually adapt general-purpose AI documentation tools to capture OMT and MSK details, for example by creating structured templates for somatic dysfunction regions, techniques, and response to treatment.

Without dedicated models, there is a risk that AI-generated notes under-document OMT and MSK findings, potentially affecting continuity of care, medico-legal clarity, and reimbursement.

Limitations & Research Gaps

The primary limitation is the near-complete absence of empirical data; most conclusions are inferential, based on what is not present in existing AI documentation literature.

Research priorities include developing and evaluating OMT-aware documentation schemas, training datasets that include detailed osteopathic notes, and AI models capable of recognizing and generating appropriate OMT documentation language.

Osteopathic Perspective

Osteopathic principles call for explicit attention to structure–function relationships and hands-on treatment; documentation should reflect this distinct approach.

A failure to embed OMT concepts in AI documentation risks eroding the visibility of osteopathic care. Conversely, DO-led design of AI tools and templates can preserve and amplify osteopathic identity, ensuring that body–mind–spirit unity and structural findings are clearly represented in the record.