Monitoring Claims for Accuracy

Addressing Coding Discrepancies and CAC Limitations to Strengthen Quality and Compliance 

Written By Dr. Stacey Atkins, PhD, MSW, LSW, CPC, CIGE 

Computer-Assisted Coding (CAC) can expedite your process, but is it accurate?  This article discusses the limitations of CAC and how to strengthen documentation and compliance to improve quality of care and improve the accuracy of your claims.

Introduction

As healthcare delivery becomes increasingly data-driven, the integrity of clinical documentation and billing practices directly impacts provider reimbursement, compliance with federal and state regulations, and ultimately, patient outcomes. Monitoring claims for accuracy is a vital process within revenue cycle management, serving as both a quality assurance tool and a compliance safeguard. A critical area of concern is the rise of discrepancies in coding, particularly when documentation appears clinically accurate, but coding errors—often exacerbated by overreliance on Computer-Assisted Coding (CAC)—compromise claim validity. This article explores the importance of proactive claim review processes, discusses the limitations of CAC, and outlines evidence-based strategies to ensure documentation and coding alignment. Emphasis is placed on quality as the foundation of compliance, with practical suggestions for mitigating discrepancies, even amid the time pressures faced by providers.

The Link Between Coding Accuracy, Quality, and Compliance

Accurate clinical coding is essential for several reasons: it ensures appropriate reimbursement, supports population health analytics, and reflects the true acuity and complexity of patient care. According to the Office of Inspector General (OIG), improper payments in Medicare and Medicaid programs continue to cost billions annually, often stemming from coding errors rather than fraud (OIG, 2022). Compliance programs in healthcare are thus required not only to prevent intentional misconduct but also to detect and correct unintentional inaccuracies in claims data.

The Centers for Medicare & Medicaid Services (CMS) stress that quality documentation alone is insufficient; it must be accurately translated into billing codes to meet compliance standards (CMS, 2021). When documentation is thorough but coding does not reflect that detail—whether due to human error, insufficient training, or flawed automation—the result is inaccurate reimbursement, potential audits, and regulatory penalties.

Computer-Assisted Coding (CAC): Promise and Pitfalls

CAC systems, designed to improve coding efficiency, use natural language processing (NLP) to extract clinical concepts from documentation and assign appropriate codes. While they can reduce manual workload and improve turnaround times, CAC tools are not infallible. Studies show that CAC accuracy varies widely depending on clinical domain and documentation quality (Dai et al., 2020). A major concern is that CAC tools may suggest incorrect codes if the software misinterprets nuanced clinical information or lacks the specificity required for precise classification.

A 2021 Journal of AHIMA study found that while CAC tools reduced average coding time, they introduced a 12–15% increase in coding discrepancies when not accompanied by robust human review (AHIMA, 2021). This “automation bias” can lead coders to accept system-suggested codes without sufficient validation. Moreover, CAC limitations are particularly evident in complex cases involving chronic conditions, behavioral health diagnoses, or overlapping comorbidities, where documentation subtleties are critical to proper code selection.

Encounter Discrepancies: Causes and Consequences

Encounter discrepancies arise when the documentation recorded by providers does not align with the diagnosis, procedure, or service codes submitted on a claim. Common causes include:

• Overgeneralization by CAC tools, which may default to unspecified codes.
• Provider time constraints, limiting detailed note-taking or code validation.
• Inadequate coder training, particularly in emerging or specialty service lines.
• Misalignment between clinical terminology and coding nomenclature.

These discrepancies may be flagged as errors during internal audits or external reviews, resulting in claim denials, delayed payments, or post-payment recoupments. Additionally, persistent discrepancies can trigger focused audits by entities such as Recovery Audit Contractors (RACs) or Unified Program Integrity Contractors (UPICs).

Evidence-Based Models for Monitoring and Review

To mitigate discrepancies and ensure accurate claims, healthcare organizations must adopt evidence-based quality assurance models that include routine claim review, coder education, and collaborative documentation practices.

1. Plan-Do-Check-Act (PDCA) Cycle: This quality improvement framework can be applied to the coding process. Regular monitoring (Check), followed by targeted interventions (Act), and process refinement (Plan/Do), can drive measurable improvements in claim accuracy (Deming, 1986).
2. Clinical Documentation Improvement (CDI) Programs: These initiatives promote ongoing dialogue between providers and coders to clarify ambiguities and ensure specificity in documentation. Studies have shown that robust CDI programs can increase coding accuracy by 20–30% (Garza et al., 2019).
3. Concurrent Coding Audits: Instead of retrospective reviews, concurrent audits allow for real-time identification and correction of errors before claims are submitted. When coders or compliance specialists are embedded in the clinical workflow, they can flag discrepancies early and reduce downstream issues (AHIMA, 2022).
4. Root Cause Analysis (RCA): When high-error claims are identified, RCA can be used to trace the source of errors—be it documentation gaps, CAC misinterpretation, or coder oversight—and develop targeted solutions.

Mitigation Strategies for Busy Clinical Environments

One of the persistent barriers to accuracy is the limited time that providers have with each patient. This pressure often leads to documentation shortcuts, copy-forward behaviors, or lack of specificity in notes, which in turn affects coding quality. The following strategies can help:

• Leverage pre-visit planning tools that prompt providers on key documentation elements based on the patient’s problem list or chronic conditions.
• Implement coder-provider feedback loops, where recurring discrepancies are discussed in monthly or quarterly forums.
• Provide microlearning sessions or just-in-time training for coders, especially after major code set updates (e.g., ICD-10-CM changes each October).
• Develop encounter-specific documentation templates that guide providers to document with the level of specificity required for accurate code assignment.
• Use dashboards and KPIs to track claim denial reasons, coding error rates, and CAC override frequency. This enables continuous improvement monitoring.

The Role of Compliance Officers and Risk Management

Compliance professionals must view coding accuracy as a risk management issue. When errors go unchecked, they may result in False Claims Act (FCA) violations, whistleblower reports, and reputational damage. In fact, over 85% of healthcare compliance settlements involve allegations of inaccurate billing and coding (DOJ, 2023).

It is imperative that compliance teams collaborate closely with HIM (Health Information Management), billing, and clinical operations to:

• Establish routine coding audits.
• Analyze error trends and provider outliers.
• Develop corrective action plans and re-education strategies.
• Ensure CAC systems are updated and monitored for performance drift.

By embedding compliance into everyday workflows rather than viewing it as a retrospective function, organizations can create a culture of accountability that enhances both care and claim accuracy.

Conclusion

Coding accuracy is not merely a technical function—it is a linchpin of healthcare quality, financial integrity, and regulatory compliance. While documentation remains a critical starting point, coding must accurately reflect that documentation to meet standards of care and legal expectations.

As CAC tools become more prevalent, healthcare organizations must remain vigilant about their limitations and ensure human oversight remains central to coding decisions. With the implementation of quality improvement frameworks, clinical collaboration, and robust audit practices, encounter discrepancies can be mitigated—improving not only claims accuracy but also compliance resilience in an increasingly scrutinized healthcare landscape.

About the Author

Dr. Stacey R. Atkins, PhD, MSW, LMSW, CPC, CIGE

Dr. Adkins is a Compliance Specialist working as a team member in the Education Department of the American Institute of Healthcare Compliance. Her career spans leadership roles with the Office of the State Inspector General, Department of Behavioral Health and Developmental Services, and HRSA, among others.

References

• AHIMA. (2021). Impact of Computer-Assisted Coding on Coding Accuracy and Productivity. Journal of AHIMA.
• AHIMA. (2022). Concurrent Coding Audits in Clinical Workflows. American Health Information Management Association.
• Centers for Medicare & Medicaid Services (CMS). (2021). Medicare Fee-for-Service 2020 Improper Payments Report.
• Dai, H., et al. (2020). Evaluating the accuracy of computer-assisted coding systems in healthcare. Health Informatics Journal, 26(4), 2765-2778.
• Deming, W. E. (1986). Out of the Crisis. MIT Press.
• Department of Justice (DOJ). (2023). False Claims Act Settlements and Judgments: Annual Update.
• Garza, H., Spivak, C., & Daniels, M. (2019). Documentation improvement and compliance outcomes. Journal of Healthcare Compliance, 41(3), 45-52.
• Office of Inspector General (OIG). (2022). Top Management and Performance Challenges Facing HHS.

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