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Billing/RCM

HCC Coding in 2026

Navigating Risk Adjustment in a Changing Healthcare Landscape

Written by: Joy Rose, MSA, RHIA, CCS, CHA, CHPS

In 2026, Hierarchical Condition Category (HCC) coding continues to evolve as a central pillar of risk adjustment in value-based care. Initially introduced by the Centers for Medicare & Medicaid Services (CMS) to project healthcare costs and determine payments for Medicare Advantage (MA) plans, HCC coding has become a strategic necessity across multiple payers and care settings.

Medicare Advantage Organizations (MAOs) are paid at a higher rate for patients who have conditions with greater levels of severity and multiple conditions, as their RAF scores and anticipated costs of care will be higher.

Key 2026 Medicare Advantage (MA) Cost Reporting Requirements

CMS requires Medicare-certified acute care hospitals reimbursed under the IPPS (inpatient prospective payment system) to report median negotiated payment rates from Medicare Advantage (MA) plans by MS-DRG on their annual cost reports for cost reporting periods ending on or after January 1, 2026.

This mandate aims to collect market-based data to set future inpatient prospective payment system (IPPS) relative weights.

Data will be used to set future MS-DRG weights likely by Fiscal Year 2029.
This requirement adds significant complexity to an already error-ridden annual Cost Report process.

Providers must ensure the accurate reporting of MA negotiated rates to avoid potential audit findings, as this data will influence future payment setting.

New in 2026 - Full transition to V28 Model has occurred

One of the biggest updates in 2026 is the full implementation of the CMS-HCC V28 model, which was first introduced in 2023. This model includes significant changes:

More clinically relevant or accurate groupings, especially for chronic conditions like diabetes and congestive heart failure.
Expanded but refined HCC categories: V28 increases the number of HCC categories from 86 to 115, creating more granular groupings while reducing additive combinations.
Renumbering and changing HCC categories.
Removal of some condition codes that were found to be less predictive of future healthcare costs.
Reduction in the number of ICD-10-CM codes from 9,797 to 7,770 (approximately 2294 codes deleted and 268 codes added)
More accurate clinical data and the use of data-drive results with the use of 2018 ICD-10-CM codes and 2019 payment information.

Healthcare providers must now re-map workflows for diagnosis coding processes and re-educate coding staff to ensure accurate code assignment based on the documentation provided by clinicians.

Greater Emphasis on Documentation Integrity - With more sophisticated audits by CMS and private payers, clinical documentation improvement (CDI) remains a top priority. Inaccurate or unsupported codes now carry steeper compliance risks, and real-time documentation tools are being widely adopted to assist clinicians. Clinicians must be educated and trained about the new model which will require even greater specificity in documentation and code assignment to ensure that the true level of the Medicare Advantage patients’ illness severity is captured and transmitted to CMS for appropriate costs analysis.

AI and NLP Integration - Natural Language Processing (NLP) and artificial intelligence (AI) tools are increasingly embedded in EHR systems to assist in identifying undocumented HCCs and improving capture rates. These tools help flag missed conditions, identify hierarchical overlaps, and ensure that chronic conditions are properly documented and reported annually. AI has its limitations according to a colleague managing denials.

Important Note - The AI tool that is being tested a major Boston medical facility is not intelligent enough to find HCCs, or even ICD-10 codes to ensure a robust denial can be created. The medical team working with the denials team does not approve the AI findings in about 80% of the AI suggestions.

Key Challenges - Training and education remain critical as coding teams and clinicians adjust to new rules and technology. In addition, there is coding fatigue from increased workload and regulatory pressure may affect coder accuracy and job satisfaction.

Providers must also balance HCC optimization with ethical standards and compliance, avoiding aggressive or unsupported upcoding practices. It is important for organizations to realize there is increased CMS scrutiny, by flagging providers as high-volume billing outliers or submitting claims with unusually high severity levels.

Opportunities:

Risk-adjustment data analytics now allow organizations to benchmark performance and track documentation trends in real time.
Proactive condition management enabled by accurate HCC coding allows payers and providers to better target care management and reduce preventable costs.
Interoperability and FHIR-based data exchange in 2026 enable smoother sharing of clinical data across systems, improving longitudinal risk tracking.
Increased focus on severity of patient diagnosis and claims by CMS

Real World Impact

As CMS moves further into outcome-based models and enhances its oversight of MA payments, the role of HCC coding will only grow in significance. Health systems that invest in robust CDI programs, AI-assisted coding tools, and clinician training will be better positioned to thrive in this value-based future.

Some analysts warn the shift could lower RAF scores 10-20% for providers still relying on V24-era documentation habits, since patients whose only qualifying condition was deleted in V28 effectively disappear from risk registries. Plans with large diabetic populations that previously captured a lot of complication-related detail are seeing the steepest declines, though expanding documentation breadth across different disease families can partly offset this.

Because of the revenue pressure, CMS/OIG have signaled they'll be watching closely for organizations overcompensating with inflated severity coding.

About the Author

Joy Rose, MSA, RHIA, CCS, CHA, CHPS is a member of the American Institute of Healthcare Compliance (AIHC) and serves as a subject matter expert on the AIHC Volunteer Education Committee.

References:

Chirok Health, 2026, https://chirokhealth.com/blog/cms-hcc-v28-model-raf-score-drop-2026/
The Centers for Medicare and Medicaid, https://www.cms.gov/newsroom/press-releases/cms-rolls-out-aggressive-strategy-enhance-accelerate-medicare-advantage-audits
American Medical Association, 2026, https://www.ama-assn.org/practice-management/payment-delivery-models/aligning-payment-and-care-value-based-practice-models

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Corporate Compliance

The Imperative of Documentation Integrity

Addressing the Healthcare Data Crisis

Written by Joanne Byron, LPN, BS, CCA, CIFHA, CHA, COCAS, CORCM, CHCO, HPOC, OHCC, CMDP, ICDCT-CM/PCS

The information in this article primarily applies to providers when recording patient encounters in their office or other places of service. Content is for educational purposes only and is not intended as consulting or legal advice.

Introduction

Clinical documentation represents the foundational pillar of modern healthcare, ensuring patient safety, care continuity, accurate reimbursement, and the ethical use of medical data for research. However, the healthcare industry is currently grappling with a severe data crisis driven by the proliferation of historical documentation errors.

The transition from paper-based charts to Electronic Health Records (EHRs), while designed to streamline operations and reduce medical errors, has inadvertently introduced systemic vulnerabilities that compromise the integrity of clinical data.

The modern healthcare data crisis is not simply a matter of lost or misplaced files; it is a systemic degradation of data quality caused by the cumulative effect of historical documentation errors. At the center of this crisis is the phenomenon known as "chart lore" or "note bloat," where inaccuracies and redundancies are perpetuated across multiple patient encounters.

Several structural and behavioral factors drive this crisis:

Overuse of Copy/Paste and Cloning: The implementation of EHRs introduced time-saving functionalities such as the "copy-forward" or copy/paste features. Studies have revealed that over 50% of the text in inpatient and outpatient notes is duplicated. This practice often results in carrying over outdated, irrelevant, or entirely incorrect clinical information (e.g., documenting an allergy that was proven false years prior), creating information overload and increasing the risk of adverse events.
Template and Drop-Down Menu Errors: The reliance on pre-populated templates and drop-down menus can lead to "mouse-click errors," where a provider accidentally selects a normal finding for an abnormal condition. These errors obscure the true "patient story" and result in contradictory or missing clinical context.
Patient Matching and Interoperability Failures: Poor data entry and fragmented system integration contribute to patient misidentification. Industry surveys indicate that up to 20% of patients may not be correctly matched to their records, leading to scenarios where providers make treatment decisions based on another individual’s medical history.
Defensive and Billing-Driven Documentation: Because healthcare systems rely on Evaluation and Management (E/M) codes and reimbursement structures, clinicians are often pressured to document excessively to satisfy complex billing requirements, rather than focusing purely on clinical utility. This return-on-investment approach distorts the clinical record and leads to defensive medicine.
- In light of Evaluation & Management guidelines allowing time or medical decision-making for many codes, providers must remember, when time is used, the complexity of the visit must be reflected to support longer visit times (higher reimbursed codes). Payers will question when high levels of service are billed but the note does not reflect the amount of work to support reimbursement.

Artificial Intelligence and the Physician/Provider Burden

Ironically, the tools intended to make documentation easier, EHR systems, have become a leading driver of clinician stress and burnout. The "cognitive load" of navigating drop-down menus and templating systems detracts from face-to-face patient time. And now with Artificial Intelligence (ambient scribes) being integrated into clinical documentation, the burden can become overwhelming due to time to ensure there are no errors in the record. AI is being built of historical information that is peppered with errors, inaccuracy, and omissions.

Despite promised efficiency gains, a large multi-center study found that AI ambient scribes saved a relatively modest 16 minutes of documentation time per eight hours of care. Because physicians are ultimately responsible for the accuracy of their medical records, they are forced to shift cognitive effort from typing to auditing—carefully reviewing AI-generated text to ensure no critical data has been omitted or misstated

Integrating artificial intelligence (AI) as ambient scribes in clinical settings reduces documentation time but yields distinct error profiles. Studies from the National Library of Medicine indicate that up to 70% of AI-generated notes contain at least one error, with an average of 2 to 3 errors per note. Omissions are the most common mistake, accounting for 71% to 83% of all errors.

Breakdown of AI Errors

Research shows that the types and frequencies of errors vary widely by system:

Omissions: Occurring in roughly 70-80% of recorded mistakes, this happens when AI leaves out critical details. Studies note that over 40% of these omissions carry moderate to significant clinical importance (e.g., omitting comorbidities or medication side effects).
Additions: Representing 4% to 11% of errors, this occurs when the AI fabricates or inserts information that was never discussed.
Hallucinations & Wrong Outputs: Fabricated or severely misidentified medical terminology.
Misplacements: Occurring in 6% to 25% of errors, where the AI correctly transcribes the info but places it in the wrong section of the chart.

Documentation Integrity & Accuracy Metrics

While traditional self-documentation by doctors can also be fragmented, ambient AI drafts often capture a much higher volume of the spoken interaction. However, this can sometimes lead to an inverse problem of information overload for the physician reviewing notes for accuracy.

Patient Safety and Clinical Continuity

The primary purpose of any clinical note is to support continuous, high-quality patient care. Outpatient practices frequently treat patients across extended timelines and involve diverse clinical staff. Therefore, documentation integrity is critical for several interconnected reasons:

Preventing Diagnostic and Medication Errors: When previous providers fail to update active problem lists, or when notes contain contradictory information, the risk of adverse events skyrockets.
- Accurate documentation ensures that allergy lists, historical diagnoses, and ongoing treatment regimens are clear, preventing medication interactions and duplicative testing.
Facilitating Coordinated Care: In an era of team-based care and interoperability, patient notes are often referenced by external specialists, primary care physicians, and allied health professionals.
- Complete, up-to-date clinical notes give care teams a holistic view of a patient’s health journey, allowing them to make informed, data-driven decisions.

Financial Sustainability and Revenue Cycle

Documentation dictates reimbursement and an organization’s ability to support compliant billing and reimbursement. In outpatient settings, practices rely on Evaluation and Management (E/M) coding guidelines established by the Centers for Medicare & Medicaid Services (CMS) and the American Medical Association (AMA).

Reducing Claim Denials: Payers use automated systems to verify that documented services match the billed codes. Incomplete or vague documentation leads to high rates of claim denials, requiring expensive and time-consuming rework for billing staff.
Combating the "Cloning" Risk: EHRs offer time-saving features like "copy-and-paste," "carry-forward," and auto-fill. While efficient, these features frequently lead to documentation cloning, where notes contain outdated or clinically irrelevant information.
- Payers increasingly view cloned notes as a compliance risk, which can lead to delayed payments or allegations of upcoding, leading to allegations of violating the False Claims Act.

The Clinical and Legal Repercussions

The accumulation of these errors across vast databases has severe, real-world consequences for patient safety and institutional liability. Regulatory bodies, including the Department of Health and Human Services (HHS) Office of Inspector General (OIG), heavily scrutinize outpatient billing. Ensuring documentation integrity limits the financial and reputational damage of audits:

Demonstrating Medical Necessity: Every medical service must be justified by documented medical necessity. Documentation must clearly demonstrate why a course of action was taken and what alternatives were considered. Without this, practices are vulnerable to recoupment during post-payment audits.
Combating Fraud, Waste, and Abuse: Accurate charting protects both the provider and the organization. Attempting to add missing information or diagnoses to a chart after an audit has been initiated is a serious legal violation that carries civil and criminal penalties. Maintaining real-time, tamper-evident documentation is the best legal defense for providers.
Patient Harm and Medication Errors: Data integrity issues directly impact diagnostic accuracy and treatment planning. Studies indicate that a significant percentage of EHR-related events—sometimes cited as over one-third of cases—have life-threatening potential. When providers are forced to skim through bloated records, critical changes in a patient's condition or medication history are frequently missed.
Artificial Intelligence and Big Data Limitations: The current push toward integrating artificial intelligence (AI) and machine learning (ML) into healthcare relies entirely on the premise of data accuracy. However, because a high percentage of EHR records contain documentation errors, predictive models are frequently built on flawed or "missing" data indicators, which compromises their clinical reliability and introduces unconscious biases into algorithmic decision-making.
Malpractice Liability: Legal teams increasingly scrutinize EHR meta-data and documentation errors during litigation. Many EHR-related malpractice liabilities stem directly from documentation errors and omission, making inaccurate record-keeping a major risk management concern.

Strategies for Restoring Documentation Integrity

Addressing the healthcare data crisis requires a fundamental shift in how documentation is viewed, created, and audited. Organizations must move beyond billing-centric metrics and prioritize true Clinical Documentation Integrity (CDI). We simply need more documentation professionals, specifically in the outpatient setting where most care is rendered.

Implement Continuous CDI Programs - Healthcare facilities must establish dedicated CDI teams that routinely review and audit charts for clarity, completeness, and clinical accuracy. However, it is important that auditors and those training providers in CDI have structured training themselves first. Not all coding and billing auditors are qualified to conduct a documentation integrity audit. By educating all those involved on best practices and modern documentation guidelines, organizations can ensure that the patient's medical history accurately reflects their current clinical state.

Engage with organizations for online CDI training to improve the basic understanding of a compliant medical record. Registering qualified staff and/or providers with an organization which is a Licensing/Certification partner with CMS is recommended, such as the American Institute of Healthcare Compliance which offers online training with option to Certify as a Medical Documentation Professional.

EHR Usability and Design Overhaul - Software vendors and IT departments must collaborate to redesign EHR interfaces. This includes implementing strict limits on copy-paste functionalities, utilizing anomaly detection tools to flag duplicated or contradictory text, and enhancing interoperability to reduce patient matching errors.

Structured Data Capture - Shifting from unstructured narrative notes to standardized, structured data formats allow for better data reuse, less error-prone information exchange, and more effective clinical decision support systems.

Patient Engagement as a Verification Tool - Opening up EHRs to patients—allowing them to access their own health records and actively report discrepancies—has proven to be an effective strategy for identifying and resolving embedded "EHRrors" before they cause harm.

Conclusion

The historical degradation of healthcare data integrity poses a significant public health threat, turning patient records from life-saving tools into repositories of perpetuated errors.

To mitigate this crisis, the healthcare ecosystem must prioritize actionable, systemic reforms. By investing in enhanced EHR design, responsible implementation of integrating AI, rigorous auditing and compliance, and a culture of clinical clarity, the industry can restore trust in medical data and safeguard patient lives.

Outpatient practices can no longer treat clinical documentation as a mere administrative byproduct. Documentation integrity is the structural backbone of patient safety, financial compliance, and legal protection. By actively investing in CDI processes, ongoing provider education, and optimized EHR workflows, outpatient practices can safeguard patient outcomes, reduce audit vulnerabilities, and restore clinician satisfaction.

About the Author

Joanne Byron, BS, LPN, CCA, CHA, CHCO, CHBS, CHCM, CIFHA, CMDP, COCAS, CORCM, OHCC, ICDCT-CM/PCS is an executive educator with the American Institute of Healthcare Compliance, a Licensing/Certification non-profit partner with CMS. She shares her experience of over 40 years as a nurse, consultant, auditor, and investigator in the healthcare field.

References

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Artificial Intelligence

Coding Under Pressure

Documentation Challenges and the Impact of Ambient AI on Medical Coding Integrity

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

Clinical documentation expertise and coding skills are both required as ambient Artificial Intelligence (AI) technology is being implemented by healthcare providers. This article explores the dual-edged impact of ambient AI on coding integrity, highlighting the challenges, risks, and opportunities coders face in this evolving documentation landscape.

Introduction

Medical coders are vital to the healthcare system’s operational and financial backbone, yet their work is often misunderstood and underappreciated. Coders face immense pressure to extract meaningful, compliant data from clinical records that are frequently incomplete, ambiguous, or rushed. As the healthcare industry seeks to alleviate physician burnout and improve clinical efficiency, ambient artificial intelligence (AI), technology that silently captures patient-clinician dialogue and generates real-time documentation, has emerged as a transformative solution. While ambient AI promises to ease burdens for providers, it introduces new complexity for coders.

The Realities Coders Face: Burden, Ambiguity, and Incomplete Records

The responsibility of a medical coder goes far beyond transcribing diagnoses into codes. Coders interpret complex medical narratives and translate them into standardized codes that support clinical quality metrics, billing accuracy, and regulatory compliance. Yet the documentation coders rely on is often insufficient, filled with vague language, or completely missing critical elements. This not only slows productivity but results in increased claims denials, higher query volumes, and elevated coder burnout.

Studies indicate that a significant portion of documentation-related denials are due to issues like insufficient specificity or lack of medical necessity, both of which can be tied directly to documentation quality. Coders are often forced to query providers repeatedly, which can strain professional relationships and delay claim submission.

What Is Ambient AI and How Is It Changing Documentation?

Ambient AI refers to passive, voice-enabled technologies designed to record and transcribe patient-clinician interactions in real time. Solutions like Nuance’s DAX (Dragon Ambient eXperience), Suki, and Notable are increasingly being deployed across primary care, urgent care, and specialty settings. These tools are marketed as clinician support technologies, offering automatic generation of structured progress notes, improved documentation timeliness, and enhanced patient-provider interaction by removing the burden of manual EHR entry.

While these systems offer clear benefits for provider wellness and workflow, they also reshape the nature of clinical documentation. The AI-generated notes may reflect patient discussions accurately, but they do not always align with coding or billing requirements. For coders, this means navigating a new documentation style with inconsistent structures and a growing need for auditing both clinical and AI accuracy.

The Impact on Coders: Efficiency vs. Integrity

From a compliance and reimbursement standpoint, ambient AI documentation presents a double-edged sword. On one hand, AI-generated notes reduce physician documentation errors and may eliminate transcription backlogs. On the other, they often produce templated language, redundancies, or incomplete clinical pictures.

Coders have reported challenges in interpreting these notes due to the lack of specificity, missing time elements, or insufficient medical decision-making details. The AI may also inaccurately capture conversation snippets that create contradictions in the note or inflate complexity. This requires coders to serve not only as clinical interpreters but as AI editors—flagging inconsistencies, auditing for risk adjustment data, and ensuring the documentation meets payer and regulatory standards. Furthermore, the rise of ambient AI has introduced new legal and ethical concerns about authorship, documentation attribution, and coder responsibility.

Systemic Challenges and Industry Trends

The healthcare industry’s shift to ambient documentation is not occurring in a vacuum. It intersects with larger issues such as coder shortages, increased demand for productivity, and evolving compliance requirements. Coders are expected to maintain accuracy and turnaround time despite growing documentation complexity. The American Health Information Management Association (AHIMA) and AAPC have both issued guidelines acknowledging the growing tension between coder expectations and the limitations of emerging technologies. Without adequate training, ongoing evaluation of AI systems, and coder feedback integration, the promise of ambient AI may become a source of further burden.

Healthcare organizations must treat coders as key stakeholders in the implementation process—not afterthoughts. Only then can ambient AI reach its intended goal of enhancing documentation.

Opportunities and Recommendations

Despite the challenges, there is room for optimism. Coders are uniquely positioned to inform how ambient AI evolves. Organizations can involve coding professionals in pilot programs, conduct dual audits of human- and AI-generated notes, and build feedback loops to improve note quality. Additionally, coders can play a central role in training clinicians to understand what is—and is not—captured accurately by AI tools.

Best practices include standardizing templates for AI-generated notes, enhancing collaboration between coding and clinical teams, and investing in AI literacy for coding staff. With proactive investment and interdisciplinary collaboration, ambient AI can supplement human expertise rather than obscure or replace it.

Coder Burnout and the Hidden Cost of Automation

As documentation technology evolves, the human cost of automation must be addressed. Coders increasingly experience mental fatigue from toggling between multiple systems, interpreting AI-generated notes, and maintaining productivity quotas. Research highlights that coder burnout mirrors patterns seen in clinicians: decreased job satisfaction, higher turnover intentions, and increased error rates.

Organizations must treat coder well-being as a strategic asset.

A sustainable documentation ecosystem must include workload monitoring, ergonomic tools, mental health resources, and professional development opportunities. Coder fatigue not only impacts morale but may result in missed diagnoses, under coding, or compliance breaches that trigger payer audits or liability exposure.

Ethical Implications and Documentation Integrity

As ambient AI increasingly authors parts of the medical record, the question of authorship and accountability becomes more urgent.

If a physician passively approves an AI-generated note with embedded inaccuracies, who is responsible when an audit reveals discrepancies? Coders, tasked with ensuring that documentation meets payer guidelines and legal standards, face ethical dilemmas when AI documentation is flawed yet signed. Institutions must establish clear attribution protocols, provide coders with protected mechanisms to flag concerns, and create policies that reflect the shared accountability between technology and human oversight. Ensuring documentation integrity in this new era requires ethical clarity as much as technical precision.

Conclusion: Human Expertise Still Matters

Medical coders remain indispensable to the integrity of the healthcare system. While ambient AI presents a compelling solution to clinician burden, it must be implemented with an understanding of how documentation changes impact downstream functions like coding and billing. High-quality clinical documentation requires both technological innovation and human oversight. As healthcare continues to embrace digital transformation, coders must be empowered—not overlooked—to ensure accuracy, compliance, and financial sustainability.

Start with clinical documentation expertise – enroll in the Clinical Documentation Improvement online training program. This online education is designed for office nurses with coding experience and those responsible for providing support services to healthcare providers to improve both outpatient documentation and services related to inpatient pro-fee 1500 claims. Earn your Certified Medical Documentation Professional (CMDP^SM) credential offered by the American Institute of Healthcare Compliance, a Licensing/Certification Partner w/CMS.

About the Author

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

Dr. Atkins 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. (2022). The Evolving Role of the Medical Coder in the Age of Artificial Intelligence.
Dyrda, L. (2023). How ambient listening tech like DAX is changing physician documentation. Becker’s Healthcare.
Gordon, P., & DeCicco, M. (2022). Ambient AI and Documentation Integrity: Risks and Rewards. Journal of AHIMA.
Wong, A., Otles, E., Donnelly, J. P., et al. (2022). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 182(5), 540–548.
AAPC. (2023). Ambient Documentation Technology: Best Practices for Coders.
Miliard, M. (2022). How Suki and similar tools aim to streamline clinical documentation. Healthcare IT News.

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Artificial Intelligence

AI and Algorithms: The Other Side of Medical Claims Processing

Written by Corliss Collins, BSHIM, RHIT, CRCR, CSM, CCA, CBCS, CPDC

Artificial Intelligence (AI) and Revenue Cycle Management (RCM) are hot topics in the healthcare industry. Optimizing RCM across all payment and reimbursement models can reduce overhead and improve accuracy which results in allowing your RCM workforce to focus on financial counseling and other important area requiring personal interactions with your patients and conducting pre and post documentation and coding audits. This article addresses what Chief Financial Officers (CFOs) and other C-Suite executives needs to know about AI efficiencies and revolutionizing your RCM process.

Revenue Cycle Management Compliance

Revenue cycle management involves complex processes to manage financial transactions related to healthcare services. Artificial Intelligence (AI) Automation and Claims Processing Algorithms for coding, billing, and payments are designed to reduce billing errors and maximize revenue recovery. Payers are already implementing AI as a solution to deny inappropriately coded claims and detect potential fraud and abuse.

In today’s environment, it is important for providers to increase efficiency and reduce the cost of operations, and it is the responsibility of Revenue Cycle Managers to understand and get ahead of root causes which can contribute to potential compliance issues. As we navigate some of the more common underlying causes of billing and reimbursement compliance discussed below, it becomes evident that AI and advanced technology may be a solution to consider, but what if AI algorithms don't perform as expected?

Revenue Cycle Management Coding Edit Errors

Implemented by payers is Medicare’s NCCI Edits (National Correct Coding Initiative) which are coding edits designed to minimize improper payer payments. They include Procedure-to-Procedure (PTP) edits and Medically Unlikely Edits (MUEs). Auto-Coding Edit Errors (automated prepayment edits) significantly impact RCM billing, claims processing, and revenue operations. Because payers are advancing their technology in these areas, it is important for providers to increase billing accuracy and implement cost-effective technology to stay compliant.

Coding related edit errors stem from improperly programmed edits and rules-based algorithms designed or written by human intelligence. These issues can exist within your practice management system or occur during an update. These unintentional flaws can cause claims to be held up in the Medical Claims Scrubber, and not submitted to payers timely. This causes delayed payments, cash flow problems, manual work to detect and correct as well as compliance issues.

Here are a few examples of why coding edit errors may occur and why detecting system failures is critical to the RCM process:

Incorrect Coding: One of the primary causes of errors is incorrect coding. This could involve using the wrong CPT (Current Procedural Terminology) or HCPCS (Healthcare Common Procedure Coding System) codes.
Bundling Issues: PTP edits often occur when procedures or services should not be billed separately because they are considered bundled together.
- For example, if a comprehensive service includes specific components that should not be billed separately, attempting to do so will trigger a PTP edit.
Mutually Exclusive Procedures: PTP edits also arise when two procedures are mutually exclusive, meaning they should not ever be performed during the same encounter.
- Billing for both procedures would trigger an edit and if billed, could trigger an audit.
Frequency Limits: MUEs limit the number of times a specific service can be billed within a given timeframe. Payer edits look for inappropriate number of units per line-item on a claim as well.
- Errors occur when providers attempt to bill for services that exceed these limits.
Documentation Deficiencies: Errors can also stem from documentation deficiencies.
- If the medical record does not support the services billed or lacks sufficient detail, it can trigger automated edits.
Upcoding or Unbundling: Sometimes errors occur due to intentional or unintentional upcoding.
- An example is billing for a more complex or expensive service than what was provided; or unbundling (billing separately for components that should be billed together).
System Glitches or Errors: Occasionally, edit errors can occur due to glitches or errors in the billing system.
- Software updates, database errors, or incorrect application of rules could cause this.
Changes in Coding Guidelines or Regulations: Updates to coding guidelines or regulations can sometimes lead to errors.
- When providers are not aware of the changes and the systems and software applications aren't updated appropriately for guidelines/regulations to be applied correctly.
Inaccurate Charge Capture: Failing to capture all automated billable services rendered to a patient. This includes failing to charge for documented services which are medically necessary and allowed under the patient’s insurance plan.
- Inaccurate charge capture results in lost revenue and potential compliance issues.
- Manual Review determines an automation edit error occurred (Claim should not have ever been placed on a bill hold).

While it would be very challenging to provide an exact dollar amount for the cost of auto-coding edit errors across all healthcare organizations, studies and industry reports suggest that they contribute to significant financial losses. Inaccurate auto-coding can lead to poor data quality, which can undermine the reliability of healthcare data used for billing, claim reimbursements, quality improvement, and policy development.

Conclusion

Each year the complexity of medical coding increases due to provider’s need to capture detailed accuracy in how they charge for treatment to maximize revenue. AI can sort through codes, annual updates and guidelines with lightning speed. The root causes of these automated coding edit errors are broad in scope and require a multifaceted approach to identifying the issues involved, reconciling and improving AI programming, and Algorithmic System updates.

To mitigate the auto-coding edit errors' financial impact, healthcare organizations should implement effective coding validation processes, regularly audit coding edit rules, and leverage technology solutions that help to improve coding accuracy and efficiency.

Healthcare Organizations should address coding edit errors proactively, evaluate how the current state and rework cost impact revenue, and enhance overall revenue cycle management performance:

Implement robust testing policies and procedures;
Measure, and monitor progress (what is measured is what improves);
Provide ongoing oversight to critical education system users;
Address the factors that can help reduce auto-coding edit error occurrences;
Created sustainable accuracy and efficiency best practices for the entire revenue cycle management process; and
Realize that AI and algorithms can cause claims processing errors in key ways, including overreliance on incomplete or inaccurate data, lack of human oversight, and the inability to keep up with evolving regulations and guidelines.

Addressing these issues through automating error-proofing measures, data quality control, and maintaining the right balance between automation and human expertise is crucial to mitigate the risk of AI-driven claims processing errors.

Auditing & Implementing a Proactive Approach

As providers implement advanced technology, critical oversight is needed to ensure the new systems are performing within compliance guidelines. Your coding professionals can be assigned to perform oversight by conducting Root Cause Analysis (RCA) after a problem is identified and implementing prospective assessments, as seen with Healthcare Failure Mode and Effect Analysis (HFMEA).

HFMEA is a prospective assessment that identifies and improves steps in a health care process thereby reasonably ensuring a safe and clinically desirable outcome. It is also considered a systematic approach to identify and prevent product and process problems before they occur.

References

About the Author

Corliss Collins, BSHIM, RHIT, CRCR, CSM, CCA, CBCS, CPDC

Corliss is the founder and Chief Revenue Integrity Officer, Chief Compliance Officer of P3 Quality LLC. She is serves as a subject matter expert and volunteer on the Education Committee for the American Institute of Healthcare Compliance.

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Artificial Intelligence

The Role of Artificial Intelligence in Revolutionizing Medical Billing Services for Physicians

Written by: Rana Awais Coding, Billing, IT Expert

In today's rapidly advancing healthcare landscape, medical billing has become a critical aspect of running a successful medical practice. Efficient and accurate billing processes are essential for healthcare providers to receive timely payments for their services. With the advent of artificial intelligence (AI) technology, medical billing services have undergone a revolutionary transformation. This article explores the role of artificial intelligence in revolutionizing medical billing services for physicians, highlighting the benefits and advancements brought about by this innovative technology.

Streamlining Documentation and Coding

Artificial intelligence has greatly simplified the documentation and coding process in medical billing. Traditionally, physicians had to spend significant time and effort on manual coding, leading to potential errors and delays in claim submissions. However, AI-powered systems can now analyze clinical documentation, extract relevant information, and automatically assign appropriate billing codes. This streamlines the entire process, ensuring accurate coding and reducing the burden on physicians.

Enhancing Accuracy and Compliance

Medical billing involves complex regulations and coding guidelines that are prone to human error. Mistakes in coding can result in claim denials, delayed reimbursements, and even legal issues. Artificial intelligence algorithms have the capability to learn and adapt to evolving billing rules, ensuring accurate code assignment and compliance with industry standards. AI-powered systems can also flag potential billing errors, such as duplicate charges or incorrect modifiers, minimizing compliance risks and optimizing revenue capture.

Improving Revenue Cycle Management

Efficient revenue cycle management is crucial for the financial sustainability of medical practices. AI technology has significantly improved revenue cycle management by automating various tasks, including claim submission, payment posting, and denial management. AI-powered billing systems can analyze historical data to identify patterns and trends, enabling predictive analytics for optimized billing processes. This proactive approach enhances revenue capture, reduces claim denials, and accelerates payment cycles, ultimately improving the financial health of healthcare providers.

Enhancing Fraud Detection and Prevention

Medical billing fraud is a pervasive issue that costs the healthcare industry billions of dollars each year. Artificial intelligence plays a vital role in detecting and preventing fraudulent activities in medical billing. By analyzing vast amounts of healthcare data, AI algorithms can identify patterns and anomalies indicative of fraudulent billing practices. This proactive approach helps in preventing fraudulent claims from being paid, safeguarding the integrity of the billing process, and reducing financial losses for both insurers and healthcare providers.

Facilitating Real-time Eligibility Verification

Determining patient eligibility and insurance coverage in real-time is essential for accurate medical billing. AI-powered systems can integrate with insurance databases and instantly verify patient eligibility, coverage limits, and pre-authorization requirements. This real-time eligibility verification reduces claim rejections and denials due to coverage issues, enabling physicians to provide timely and appropriate care while maximizing revenue potential.

Optimizing Workflow Efficiency

Artificial intelligence has the potential to revolutionize the workflow efficiency of medical billing services. AI-powered systems can automate repetitive and time-consuming tasks, such as data entry, claims submission, and payment reconciliation. By eliminating manual intervention and streamlining processes, AI frees up valuable time for medical billing professionals to focus on more complex and value-added activities. This leads to increased productivity, reduced administrative burdens, and improved job satisfaction among healthcare staff.

Ensuring Data Security and Privacy

The sensitive nature of patient health information requires robust data security and privacy measures in medical billing. Artificial intelligence solutions integrate advanced security protocols to safeguard patient data from unauthorized access and breaches. AI algorithms can identify potential security vulnerabilities, detect anomalies, and proactively protect against cyber threats. With AI-driven data encryption and access control mechanisms, healthcare providers can ensure compliance with data protection regulations and maintain patient trust.

Conclusion

Artificial intelligence has undoubtedly revolutionized medical billing services for physicians. From streamlining documentation and coding to enhancing accuracy and compliance, AI-powered systems have significantly improved efficiency and effectiveness in medical billing processes.

The role of AI extends to revenue cycle management, fraud detection, real-time eligibility verification, workflow optimization, and data security. By embracing AI technology, healthcare providers can streamline their billing operations, reduce costs, and focus more on delivering quality care to patients. As the field of artificial intelligence continues to evolve, its potential to transform medical billing services will only grow, paving the way for a more efficient and sustainable healthcare system.

Author Bio:

Rana is a highly accomplished healthcare professional with over 11 years of experience in healthcare administration, medical billing and coding, and compliance. He worked previously at a large multi-physician family care and occupational health practice with two locations in northwestern PA and now works for Physician Billing Company (PBC) in the medical billing department to write articles about medical coding. He enjoys sharing his knowledge and experience as a certified PMCC instructor. He has authored many articles for healthcare publications and has been a featured speaker at workshops and coding conferences across the country.

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