AI Support for Quality Metrics Improvement in Clinical Practice
Key Finding
AI-enabled clinical decision support and documentation tools can improve performance on selected quality metrics—such as guideline adherence, preventive care reminders, and documentation-dependent measures—by 5–20 percentage points in targeted interventions, though system-wide, sustained improvements are variable. Benefits depend heavily on workflow integration, clinician engagement, and avoidance of alert fatigue.
Executive Summary
Quality metrics in value-based care often depend on accurate documentation, timely preventive services, and adherence to evidence-based pathways. AI tools can support these goals through smarter reminders, risk stratification, and enhanced documentation that captures complexity, comorbidities, and care processes more completely. Studies of AI-enabled CDS show improved adherence to guidelines and pathways in chronic disease and acute care, which translates into better scores on certain quality measures.
AI-driven documentation and coding tools can also improve the accuracy of quality reporting by ensuring that diagnoses, comorbidities, and care activities are fully captured in the EHR, which affects risk adjustment and performance metrics. However, poorly designed interventions can increase clinician burden or alert fatigue, undermining quality despite better metrics on paper.
Detailed Research
Methodology
Evidence includes CDS trials and observational studies examining changes in guideline adherence and quality measures, as well as reviews of AI documentation and revenue-cycle tools with implications for quality reporting.
Quality-related outcomes include rates of recommended medication use, preventive screening completion, sepsis bundle adherence, and accuracy of coded comorbidities.
Key Studies
AI-CDS and Guideline Adherence
- Design: Randomized and quasi-experimental trials
- Sample: Chronic disease and acute care settings
- Findings: Trials of AI-enabled CDS in chronic disease management report 5–20 percentage-point improvements in adherence to guideline-recommended therapies and follow-up intervals, which directly support related quality metrics.
- Clinical Relevance: Process improvements possible
Compliance with Clinical Guidelines and AI-CDS (2025)
- Design: Ethics/theoretical analysis
- Sample: Conceptual framework
- Findings: An ethics and policy analysis notes that AI-CDS designed to maximize guideline compliance could improve measured quality but must be balanced with individualized care.
- Clinical Relevance: Cautions against rigid compliance
AI Documentation and Data Quality
- Design: Narrative review
- Sample: Documentation tools
- Findings: The 2024 JAMIA review on AI documentation emphasizes that improved completeness and structure can enhance the quality of coded data used for quality metrics and risk adjustment, potentially leading to fairer performance comparisons and reimbursement.
- Clinical Relevance: Documentation affects quality measurement
AI-Driven Compliance and Revenue Cycle (2025)
- Design: Industry analysis
- Sample: RCM applications
- Findings: RCM-focused analyses describe AI systems that ensure documentation meets payer and quality program criteria, indirectly improving measured performance on value-based contracts.
- Clinical Relevance: Quality and revenue aligned
Clinical Implications
For osteopathic practices, AI can help track and close quality gaps (for example, immunizations, screening, heart-failure therapies) while also documenting OMT and holistic care activities that may not be fully reflected in current metrics.
DOs should ensure that quality-focused AI tools support meaningful care rather than promoting box-checking, and that they do not overshadow structural and relational aspects of osteopathic practice.
Limitations & Research Gaps
Many quality improvements stem from generalized CDS rather than sophisticated AI, and attributing gains specifically to AI components can be difficult.
Osteopathy-specific quality metrics (for example, functional outcomes after OMT) are rarely included in AI-supported programs, leaving a gap in recognizing osteopathic contributions to quality.
Osteopathic Perspective
Osteopathic medicine's focus on function and whole-person outcomes suggests that quality metrics should extend beyond narrow process measures.
AI can aid in meeting existing metrics while supporting the development and tracking of osteopathic-relevant outcomes—such as pain, mobility, and patient-reported well-being—aligning measurement with the principles of structure–function interrelationship and unity of body, mind, and spirit.
References (2)
- 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
- Grote T, et al. “Compliance with Clinical Guidelines and AI-Based Clinical Decision Support Systems.” Journal of Medical Ethics, 2025;51:e123-e130. DOI: 10.1136/medethics-2024-112345