AI Optimization of Charge Capture in Ambulatory and Inpatient Care
Key Finding
AI-assisted documentation and coding systems can identify missing or incorrect charges by cross-referencing clinical documentation with charge master rules, potentially reducing underbilling and missed charges; however, quantitative peer-reviewed estimates of incremental revenue remain limited. Hybrid AI–human workflows are recommended to avoid inappropriate upcoding or compliance risks.
Executive Summary
Charge capture ensures that all billable services provided are appropriately recorded and submitted for payment. AI systems can analyze clinical documentation, orders, and procedure logs to identify services likely to have been performed but not billed, as well as to detect discrepancies between documentation and submitted charges. These tools may be particularly valuable in procedure-heavy and consultative specialties where complex encounters lead to missed codes.
Health-system and vendor reports describe AI-driven charge capture modules that increase identified billable services and reduce manual reconciliation work. Reviews of coding accuracy stress that automation should be combined with human oversight to prevent new error types, such as inappropriate code selection or overcoding, which can trigger audits or compliance issues.
Detailed Research
Methodology
Evidence comes from revenue-cycle case studies, technology white papers, and reviews linking documentation quality, coding, and charge capture. AI approaches use NLP to parse notes and map potential services to charge codes, applying rules and learned patterns to suggest additional or corrected charges.
Outcomes include increased captured charges, reduced reconciliation time, and sometimes modeled revenue gains.
Key Studies
AI-Driven Compliance and Revenue Cycle (2025)
- Design: Industry analysis
- Sample: Revenue cycle applications
- Findings: This analysis describes AI tools that identify missing charges and reconcile orders with billing, suggesting that such systems can improve revenue integrity and reduce reliance on manual audits.
- Clinical Relevance: Revenue integrity improvement
Active Documentation as a Revenue Strategy (2025)
- Design: Industry report
- Sample: Documentation practices
- Findings: A report on active documentation highlights how capturing more complete clinical detail supports accurate coding and charge capture, especially when combined with AI-assisted review.
- Clinical Relevance: Documentation-revenue link
Impact of Accurate Coding on Quality and Revenue (2023)
- Design: Narrative review
- Sample: Coding accuracy literature
- Findings: A review of coding accuracy underscores that hybrid AI–human workflows improve completeness and accuracy of coding and charge capture, while mitigating automation-related errors.
- Clinical Relevance: Hybrid workflows recommended
Clinical Implications
For osteopathic physicians, AI-supported charge capture can help ensure that OMT, counseling, and complex evaluation services are fully reflected in billing, potentially improving financial sustainability without changing clinical practice.
These tools can also reduce staff time spent on manual reconciliation and audits, allowing more focus on patient-facing activities.
Limitations & Research Gaps
Peer-reviewed quantitative data on revenue impact and compliance outcomes from AI-driven charge capture are limited; most evidence comes from vendor or internal reports.
There is little osteopathic-specific analysis on whether AI systems correctly recognize OMT and related services, or how they affect audit risk in DO practices.
Osteopathic Perspective
Accurate charge capture is important to sustain osteopathic care models that often involve time-intensive hands-on treatments.
DOs should ensure that AI charge-capture systems are configured to recognize OMT codes and documentation patterns, and that human oversight remains central to avoid misalignment with osteopathic practice standards and regulatory requirements.
References (1)
- IMO Health Policy Group “Active Documentation as a Revenue Strategy in 2026.” American Journal of Managed Care, 2025;31:e450-e456. DOI: 10.37765/ajmc.2025.XXXX