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Billing/RCM

Common Compliance Risks in OB/GYN Medical Billing and How to Address Them

Written by Noah Smith for BillingFreedom

The article will help healthcare professionals identify common compliance risks that can arise in OB-GYN medical billing and understand practical approaches for addressing those risks through accurate documentation, coding, claim review, internal audits, and consistent billing workflows.

OB/GYN billing can get complicated quickly. During the same week, a practice may bill for preventive visits, ultrasounds, office procedures, prenatal care, surgery, delivery services, postpartum visits, and treatment for unrelated gynecologic conditions. Those services do not always follow the same documentation, coding, or payer rules. That leaves plenty of room for small mistakes to slip into the billing process.

Sometimes the problem is obvious. A claim is rejected because the subscriber number is wrong or a required field is missing. Other problems are harder to notice. A payer may process a claim even though the documentation is weak, a modifier has been used inconsistently, or staff are following an outdated billing process.

One paid claim does not necessarily tell a practice that everything behind the claim was handled correctly. A better way to think about compliance is to look at the entire path a claim takes:

Patient information has to be accurate.
Coverage needs to be checked.
The provider's note has to support the service.
Coding needs to match the record, and payer requirements have to be addressed before the claim goes out.

When one part of that chain breaks down repeatedly, the problem can spread across dozens of claims before anyone recognizes the pattern.

Where OB/GYN Billing Problems Usually Start

Many compliance issues begin before a coder ever looks at the chart. Consider a returning patient whose insurance changed since her last appointment. If the old plan is still listed in the system, the claim may be sent to the wrong payer. By the time the rejection comes back, staff may need to update the account, verify benefits again, rebill the service, and make sure a filing deadline has not been missed.

Authorization problems can develop in much the same way. A service may have been appropriate and clearly documented, yet the claim can still run into trouble if the payer required prior authorization and nobody confirmed it.

Then there is the medical record itself. A provider may remember exactly what was discussed or performed during a visit, but the billing team can only rely on what appears in the documentation. If the note does not clearly support the service being reported, defending the claim later becomes much more difficult.

The Centers for Medicare & Medicaid Services (CMS) provides guidance on electronic healthcare claims and the information needed for claims processing. The larger point for a practice is simple: compliance starts long before a denial or payer review arrives.

Documentation and Coding Need to Tell the Same Story

Documentation and coding are often discussed as separate tasks. In actual billing, they are difficult to separate. The code on the claim is supposed to represent what happened during the encounter. The medical record is what supports that representation.

Problems begin when the two tell different stories.

A common OB/GYN situation is a preventive visit in which the patient also brings up a new medical concern. Additional evaluation may take place during the same encounter. Whether separate reporting is appropriate depends on the services performed, the documentation, coding rules, and the payer's requirements. Similar questions come up with procedures, diagnostic testing, postoperative care, maternity services, and modifier use.

A diagnosis code may be valid in general but still fail to match what the provider actually documented. A procedure code may describe a service correctly but lack enough support in the chart. A modifier can also create problems when staff use it routinely instead of deciding whether the circumstances of that particular encounter justify it. These are not always dramatic errors. That is part of the problem.

When the same documentation habit or coding shortcut is repeated week after week, an isolated weakness can turn into a larger compliance concern.

Periodic chart-to-claim reviews can help uncover those patterns. Instead of asking only whether the claim was paid, the reviewer looks at whether the claim accurately reflects the record and whether the documentation is strong enough to support what was billed.

Some Claim Errors Have Nothing to Do with Complex Coding

Not every denied or rejected claim involves a difficult coding question. Sometimes the problem is a wrong date, an outdated insurance record, missing provider information, an incorrect subscriber ID, or a claim field that was left incomplete. These errors may sound minor, but they still consume staff time and slow down payment.

Electronic claims generally pass through automated edits during processing. Certain missing or inconsistent details can cause the claim to stop before it gets very far.

A short review before submission can catch many of those problems. Staff may want to verify:

Patient and subscriber information.
Current insurance coverage and coordination of benefits.
Provider and practice identifiers.
Diagnosis codes, procedure codes, and modifiers.
Documentation supporting the billed service.
Required authorization or referral information.
Payer-specific claim requirements and missing fields.

The review does not have to turn into a lengthy approval process for every claim. What matters is that the practice has a reliable way to catch repeatable errors before the payer does.

Eligibility Deserves More Attention in OB/GYN Billing

Insurance information can change during the course of care, and OB/GYN practices are especially likely to encounter that issue because many patients receive services over an extended period.

Pregnancy is an obvious example. A patient may have one insurance plan early in the pregnancy and another later. Employment can change. A spouse's coverage can change. Coordination of benefits may need to be updated. Authorization rules may also be different under the new plan. If staff rely on an eligibility check performed months earlier, the billing team may not find out about the change until a claim is denied.

Eligibility problems can affect more than reimbursement. They may also result in the wrong amount being assigned to the patient or create confusion about who is financially responsible for the service.

Checking coverage at appropriate points throughout treatment gives staff a chance to address those issues before the claim has already gone through the billing cycle. It also makes financial conversations with patients more accurate.

A Denial May Be Pointing to a Workflow Problem

Correcting a denied claim is necessary. Correcting the same type of denial twenty times should raise a different question - Why does it keep happening?

Suppose claims for a particular procedure regularly come back because information is missing. Billing staff can add the information and resubmit each claim, but that does not explain why the original claims were incomplete.

Maybe the registration team is not collecting something the payer requires. Perhaps the authorization information exists but is not being transferred correctly. It could also be that staff misunderstood a payer policy. The denial itself is only the visible part of the problem.

This is why useful denial management goes beyond counting how many claims were denied. Practices can look at which reasons occur most often, which payers are involved, whether one service keeps appearing, and where in the workflow the original error began.

That kind of review can reveal patterns that would otherwise remain hidden. The CMS Medical Review and Education resources also discuss claims analysis and medical record review in the context of identifying improper billing and documentation issues. For an OB/GYN practice, denial data can serve as a practical warning system. It shows where the revenue cycle is struggling, not just where payment was delayed.

Internal Audits Can Be Small and Still Be Useful

An internal audit does not have to involve hundreds of charts. A practice can learn a great deal from a carefully chosen sample.

Maybe one modifier has been causing questions. Perhaps a particular payer has denied an unusually high number of claims. There may be concerns about preventive visits, maternity billing, surgery, medical necessity documentation, or another service that carries more risk. Those claims can be reviewed against the medical record.

The reviewer may find that everything was handled appropriately. If not, the next step is to determine whether the problem was isolated or whether it reflects a larger habit. That distinction matters. One coding mistake made on a single claim may require a simple correction. Finding the same mistake across several providers or multiple dates of service suggests that the practice may need education, a workflow change, or closer monitoring. The audit should not end when the error is identified.

If a change is made, the practice needs some way to determine whether it worked. Reviewing another sample later can show whether the same problem is still appearing. Without follow-up, the practice has documented a problem but has not necessarily solved it.

Compliance Works Better When It Is Part of Routine Operations

A compliance process does not need to be complicated to be useful. In many practices, consistency matters more than creating a large set of policies that nobody uses. Staff should know how registration is handled, when eligibility is checked, how authorization information is recorded, how claims are reviewed, what happens when a denial arrives, and who is responsible for following up on recurring problems.

Those processes should not exist only in one employee's memory. Training matters for the same reason. Payer policies change. Coding guidance changes. Internal workflows change. New employees arrive, and experienced employees sometimes continue using a process that made sense under an older rule.

Regular education gives the practice a chance to catch those gaps.

Billing data can also help determine where training is needed. If eligibility denials suddenly increase, the first response should not necessarily be a general coding seminar. The practice may need to look at registration and verification instead. If several claims involving the same modifier are being questioned, a focused review of those encounters is probably more useful than retraining the entire staff on every coding topic. Compliance becomes easier to manage when the response matches the actual problem.

Documentation Reviews Should Include the Claim

A chart can look complete on its own while the corresponding claim still contains a problem.

The opposite is also possible. A claim may appear technically correct until someone compares it with the medical record.

Looking at both together usually provides a clearer picture. This is particularly important for services where the circumstances of the encounter affect billing. Preventive care, problem-oriented visits, procedures, maternity care, and postoperative services can all raise questions that cannot be answered by looking at a code alone.

The reviewer needs to understand what actually happened during the visit, what the provider documented, and how that information was translated into the claim.

Preparing for Billing Changes Before They Reach the Claims Department

One of the easiest ways for a billing problem to spread is for a rule to change while the practice keeps following the old process. Changes may affect coding, documentation, payer policies, reimbursement, or the way certain services are reported.

The first sign should not have to be a wave of denials.

When a significant change is announced, the practice can identify which services will be affected and who needs to know about it. Providers may need different documentation. Billing staff may need revised procedures. Software settings or claim edits may also need to be updated. Testing the new process early is usually easier than correcting a backlog later. This becomes especially important when changes affect maternity services because the care and billing may span several months.

The Bigger Compliance Question

A claim can be paid and still come from a weak process. That is why payment should not be the only measure of whether an OB/GYN billing operation is working well. A better question is whether the practice could explain and support the claim if someone reviewed it later.

Was the patient's coverage checked?
Does the chart support the service?
Does the code match what was documented?
Were payer requirements addressed?
If a similar problem appeared last month, was anything changed afterward?

Those questions bring compliance into the normal revenue-cycle process instead of treating it as something that matters only during an audit.

Most billing problems do not begin as major compliance failures. They usually start much smaller: an insurance detail that was not updated, documentation that was a little too vague, a modifier applied out of habit, or a denial that was corrected without asking why it happened.

The risk grows when the same issue becomes routine.

Finding those patterns early is what gives a practice the best chance to correct them before they affect more claims, more patients, or more revenue.

About the Author Noah Smith

This article is written by Noah Smith on behalf of BillingFreedom. Noah is a medical biller, SEO and Content Outreach Specialist.

Additional Resources

Centers for Medicare & Medicaid Services (CMS) Electronic Health Care Claims
Centers for Medicare & Medicaid Services (CMS) Medical Review and Education
BillingFreedom OB/GYN Billing & Coding Guidelines
BillingFreedom 2027 OB/GYN Billing Changes: Prepare for Unbundled Care

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Billing/RCM

Mitigating Compliance Risks in Genetic Testing Billing and Medical Necessity Claims

Written by: Ricky Bell

Having spent a decade advising clinical laboratories and health systems on revenue cycle management, I can tell you that molecular diagnostics remains one of the most volatile operational areas in healthcare. Federal spending on genetic testing under Medicare Part B now sits above $3.6 billion every year. That rapid financial growth brought aggressive oversight from the U.S. Department of Health and Human Services Office of Inspector General (HHS-OIG) and the Department of Justice.

In the complex arena of medical billing, molecular diagnostic testing sits right in the crosshairs of federal auditors. Regulators no longer rely on random sampling. Instead, they deploy advanced data analytics to flag billing anomalies instantly. For compliance officers and practice managers, ensuring every claim meets strict coverage standards isn't just a recommendation—it is a survival strategy that lab executives cannot afford to sleep on. Rules change overnight. When billing protocols lack internal controls, financial penalties and False Claims Act liability follow quickly behind.

Where Labs Usually Get Burned

When reviewing Federal enforcement actions, one may find specific aspects of operations that lead to regulatory setbacks, including clawbacks and fines. For example, OIG has on multiple occasions published fraud alerts with the primary goal of targeting genetic testing practices and has pointed out that claims that result in financial penalties most often stem from major failure of the system's processes rather than from honest error.

Common High-Risk Testing Behaviors:

High-Risk Testing Behaviors.
Billing unbundled molecular CPT codes.
Bill a panel without a chart proof.
No signature by the doctor on the order.

Use of non-compliant lead-generation practices that may violate healthcare marketing regulations. Incorrect use of unlisted codes that relate to the genome.

Examine billing of multi-gene panels for cancer. Legal consequences come immediately when multi-gene hereditary cancer or pharmacogenomic panels are billed without showing the medical necessity of each individual gene target. Paying entities do not generally accept that a broadly screening panel is a medical necessity simply because a patient has a family history of disease. In addition, laboratory-marketing relationship set-ups frequently breach the Eliminating Kickbacks in Recovery Act (EKRA) and the Anti-Kickback Statute. When labs pay for marketing services in proportion to volume or claim value, they open themselves up to the possibility of being investigated by the Department of Justice, a common compliance issue that many lab managers face.

Navigating Medical Necessity and Coverage Controls

Defining medical necessity in genetics testing is really about finding a middle ground between clinical utility and coverage criteria determined by payers. An example is when a physician thinks a 50-gene panel is the ideal choice for giving the right diagnosis. Still, if the local coverage policy (LCD) lists just five genes as the only ones that are covered and the patient's condition is consistent with only these genes, then the doctor will be referring to the patient for the other testing that the insurance is not covering.

Maintaining billing compliance, organizations must master the requirements set by the Molecular Diagnostic Services (MolDX) program and commercial utilization management policies. Commercial payers and state Medicaid programs frequently diverge on prior authorization rules, creating administrative friction for billing staff. Truth is, what works for Medicare might fail completely with a commercial plan.

Key Operational Checks for Coverage:

Review local coverage rules monthly.
Get prior approval before testing.
Document clinical rationale in charts.
Verify specific CPT code coverage.
Check doctor order signatures daily.

A pre-test verification procedure is a compulsory setup. If a lab gets referrals from community physicians outside, it will be wrong to assume that the requesting provider already wrote medical necessity notes in their EMR. The lab on its own has to verify that clinical records back up the selected test panel before carrying out the test and presenting the charge. Not checking the chart papers exposes the lab to risks during an after-payment review of billing practices. So, you don't ever want to end up having that as your big error.

How to Build an Audit Framework That Works

To prevent improper payments, progressive health systems are moving away from passive retro-audits. Implementing an active Genetic Testing Stewardship Program (GTSP) provides a proven operational blueprint. For example, Nemours Children’s Health successfully curtailed unnecessary genetic testing orders by placing certified genetic counselors directly into the ordering workflow and embedding hard-stops in their Electronic Health Record (EHR) systems.

A solid internal audit framework evaluates claims both before submission and after payment. Health systems must establish routine internal controls that evaluate coding accuracy, physician intent, and documentation completeness.

Essential Audit Program Controls:

Add decision support in EHR.
Audit high-risk codes monthly.
Use genetic counselors as gatekeepers.
Track payer denial codes weekly.
Check fair market value rates.

Concurrently, compliance teams should conduct random quarterly audits on claims utilizing unlisted CPT® codes (such as CPT® 81479). Unlisted codes attract automatic payer scrutiny. If your team uses unlisted codes to bypass prior authorization or LCD restrictions, auditors will flag those claims for recoupment. Training billing personnel to double-check local coverage policies ensures that claims align precisely with current billing guidelines.

Real Exposure Under Federal Statutes

The risks linked to statutory non-compliance are not just limited to denial of claims. Compliance risks related to molecular diagnostic services can have far-reaching consequences, including the imposition of heavy statutory penalties under the False Claims Act, Stark Law, and EKRA. Pursuant to the False Claims Act, if one submits claims for tests that do not have a documented medical necessity, this may result in the payment of triple damages plus the imposition of compulsory civil money penalties per claim.

Labs need to figure out as well, how they relate their working relationships, if any, with ordering physicians, and clinical consultants. It is a federal crime under anti-kickback laws to distribute free point-of-care testing devices, offer lavish consulting arrangements, or to provide generous collection fees to ordering clinics. Basically speaking, financial arrangements between you and a referrer should only be as much as the Fair Market Value (FMV) of the service actually done. Besides, having clear and complete documentation of FMV determinations and legal opinions is another defense measure that every lab board should definitely work on.

About the Author

Ricky Bell (https://www.dastifysolutions.com/team/rickybell/) is Head of Operations at Dastify Solutions, where he oversees healthcare operations, revenue cycle management, and compliance initiatives for physician practices, clinical laboratories, and healthcare organizations across the United States. With extensive experience in medical billing, coding compliance, denial management, and revenue cycle optimization, he helps healthcare providers strengthen operational efficiency while maintaining regulatory compliance.

Resources

U.S. Department of Health and Human Services Office of Inspector General (HHS-OIG): Fraud Alert: Genetic Testing Scam.
https://oig.hhs.gov/fraud/consumer-alerts/fraud-alert-genetic-testing-scam/
American Health Law Association (AHLA): Fraud and Abuse Issues in Diagnostic and Molecular Testing.
https://www.healthlawyers.org
Centers for Medicare & Medicaid Services (CMS): MolDX: Molecular Diagnostic Tests (LCD L35025).
https://www.cms.gov/medicare-coverage-database/view/lcd.aspx?lcdid=35025
Kaiser Family Foundation (KFF): Coverage of Breast Cancer Screening and Prevention Services.
https://www.kff.org/womens-health-policy/coverage-of-breast-cancer-screening-and-prevention-services/
National Center for Biotechnology Information (NCBI / PMC): The Genetic Testing Stewardship Program: A Bridge to Precision Diagnostics for the Non-genetics Medical Provider.
https://pmc.ncbi.nlm.nih.gov/articles/PMC9124555/

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Billing/RCM

Healthcare Revenue Cycle Compliance

Common Risks and How Practices Can Address Them

Written by: Zara Ahmad

A revenue cycle rarely breaks because of one dramatic mistake. More often, the problem begins with something ordinary: an insurance card was updated but the old plan stayed in the system, a provider’s note lacked enough detail for coding, or a denied claim was resubmitted before anyone checked the first one.

Compliance is not limited to the billing office. It starts when patient information is collected and continues through documentation, coding, claim submission, payment posting, denials, and follow-up.

Where Compliance Risks Can Enter the Revenue Cycle

Consider a routine office visit. The front desk enters the patient’s demographic and insurance information. If the member number is wrong, or the payer on file is outdated, the claim may already be inaccurate.

The next risk may appear in the medical record. A provider knows what happened during the visit, but a coder can only rely on what is documented. If a note is vague, staff should not fill in missing details from habit or assumption.

Charge capture creates another point of exposure. A service can be missed, entered twice, or attached to the wrong date. Later, a biller may resend a denied claim without confirming whether the original is still processing. Payment posting and accounts receivable follow-up can create problems too, especially when adjustments or corrections receive little review.

Common Revenue Cycle Compliance Risks

One familiar risk is a mismatch between the medical record and the claim. The service billed should be supported by the documentation. CMS guidance for Medicare makes documentation part of determining whether applicable coverage, coding, billing, and payment requirements are supported.

Incomplete documentation is often less obvious. A note may show that care occurred but still omit information needed to support a code, modifier, or service level. If that happens regularly, the issue is no longer just one troublesome claim.

Administrative mistakes matter as well. Incorrect patient details, insurance information, provider identifiers, and dates of service can cause denials and repeated corrections. Duplicate claims are another example. When payment is delayed, resubmitting the same claim may feel harmless, but claims-processing rules include duplicate edits.

Corrections need a consistent approach – contingent upon the payer and circumstances, the right step may be a corrected claim, replacement claim, appeal, or another defined process.

Why Documentation and Coding Accuracy Matter

Documentation, coding, and billing are different jobs, but they should describe the same encounter.

Suppose a coder returns the same type of note to the same provider several times each month because one detail is routinely missing. Correcting each claim solves the immediate problem, not the workflow problem.

A short, focused discussion with the provider may be more useful than another round of individual corrections. The aim is simply to make sure the record clearly reflects the service provided and gives coding staff the information they need.

Using Internal Audits to Identify Compliance Risks

Internal audits are most useful when they answer a specific question.

A manager might sample claims involving a frequently used modifier, one provider, a service with rising denials, or a payer that has generated repeated corrections. The review can compare claims with medical records, check key fields, examine adjustments, and see whether staff followed internal procedures.

Patterns often tell the real story. Several eligibility denials traced to the same registration step suggest a front-end workflow problem. Repeated coding questions may point to training or documentation habits instead.

An audit should lead somewhere. Someone needs to own the follow-up, record what changed, and later check whether the change helped.

Building a Stronger Compliance Culture

Compliance works better when people see how their own work affects the claim. Front-office staff influence patient and insurance information. Providers influence documentation. Coders and billers influence what reaches the payer. Managers decide whether recurring problems are investigated or simply worked around.

OIG’s General Compliance Program Guidance discusses written policies, education, communication, auditing and monitoring, and corrective action as parts of a compliance program. In everyday practice, those ideas are more useful when connected to real problems rather than treated as an annual checklist.

Training should follow the same principle. If an audit finds repeated modifier errors, train on that issue. If registration mistakes are driving denials, review that workflow with the people who perform it.

Practical Steps Healthcare Practices Can Take

Review a representative sample of claims regularly.
Compare billed codes with the supporting medical record.
Track denials and claim corrections by reason.
Review write-offs, refunds, adjustments, and claim changes for consistency.
Use recurring errors to guide staff and provider education.
Keep billing and compliance procedures current and easy to find.
Document corrective actions and check whether they worked.
Follow relevant CMS, OIG, and other authoritative guidance as requirements change.

Keeping Compliance Part of Everyday Work

No revenue cycle will be completely free of errors. What matters is what happens after a mistake is found. Comply with overpayment rules. Submit appropriate claims adjustments, credit balance reports, or self-reported refunds directly to your assigned Medicare contractor.

Investigate. Correct the affected account, but do not stop there. Ask where the error entered the process, why it was not caught earlier, and whether the same thing is happening elsewhere. That turns compliance from a periodic exercise into part of ordinary revenue cycle work. Over time, it can reduce avoidable rework, support more accurate billing, and leave a practice better prepared when claims are reviewed.

About the Author

Zara Ahmad is a healthcare industry professional and Marketing Team Lead at MedsIT Nexus, with a focus on healthcare revenue cycle management, healthcare operations, and industry education. Her work involves developing educational resources on healthcare administration, revenue cycle processes, and operational challenges affecting healthcare organizations.

Resources – obtain training in conducting internal audits and investigations from the American Institute of Healthcare Compliance, a Licensing/Certification partner w/CMS.

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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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Auditing

Measuring Audit Results

Why Statistical Literacy is Crucial for Auditors

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

Information provided below is a basic overview of common statistical terminology used when Auditing for Compliance and not intended as being comprehensive, legal or consulting advice. Please consult a professional for more information regarding the importance of using statistical measures for your healthcare organization.

Why Is This Important When Software Generates the Statistics?

It is essential for chart auditors to understand statistical terms even when software generates the statistics because automated tools cannot interpret context, detect hidden biases, or make judgment calls regarding data quality.

While software speeds up the analysis of large datasets, an auditor's understanding of statistics is required to validate that the results are meaningful, accurate, and truly answer the audit's objective rather than just identifying coincidental correlations.

Advantages of Using AI - Artificial intelligence (AI) and software tools are transforming medical chart audits from infrequent, retrospective sampling into continuous, comprehensive, and automated processes. These tools primarily utilize Natural Language Processing (NLP) and Machine Learning (ML). AI integrates with electronic health records (EHRs) to analyze 100% of patient records continuously, rather than relying on limited retrospective samples, providing the ability to identify documentation gaps (such as missing signatures, late entries, unsupported coding), coding errors, and compliance risks in real-time.

Advanced, AI-enabled health information systems can now analyze raw, disparate data from Electronic Health Records (EHR)—including clinical notes, lab results, and patient-reported metrics—to automatically calculate complex health scores like Metabolic Equivalents (METs) and identify declining kidney function.

AI-driven tools identify patterns of documentation errors, allowing auditors to proactively manage risks related to payer audits and recoupments or situations which can trigger investigative external audits.

Human Review is Necessary

Statistical software works on the principle "garbage in, garbage out" (GIGO). Auditors must know if the data was collected properly, if there are missing values, and if the data is skewed, as automated tools may process flawed data without flagging it.

Auditors ensure data integrity – that the data accurately reflects the patient encounter or financial transaction before software analyzes it. Understanding statistical distribution helps auditors know when a simple "average" is misleading and when they need to look at the median or standard deviation.

Then there is the importance of contextual interpretation. Human auditors are better at interpreting the context and intent of a clinical note, such as differentiating "CTA" (clear to auscultation) from "CTA" (CT-angiogram) based on the surrounding narrative. Also, auditors can integrate information not explicitly written together in a single section. It is important to insert the human factor because auditors can spot subtle indicators or nuance not easily quantifiable by algorithms.

Risk Oversight - Ethical and Regulatory Accountability

Human oversight is crucial to prevent the "black box" problem where AI makes decisions without transparency, mitigating potential bias in automated audit systems. It prevents algorithmic bias and "automation complacency" where humans over-rely on AI.

By keeping human judgment in the loop, especially when auditing complex datasets or making critical decisions, the integrated process ensures the logic behind a decision is interpretable, transparent, and legally sound.

Importance of Statistical Literacy

Compliance should be the focus of all your audit functions. Measuring where you are now and improvements achieved is accomplished by applying statistics to understand data collected during the baseline audit and subsequent audits over time.

In healthcare chart audits, statistics are primarily used to summarize coding, billing and documentation compliance as well as clinical performance, identify variations in care, and determine if quality improvement (QI) initiatives are successful. These audits rely on both basic descriptive measures and more specialized tools for monitoring trends.

Auditors use descriptive statistics to summarize data and describe the basic features of a set of patient records. Instead of reading hundreds of individual charts, auditors use these "snapshots" to see the big picture—like how well a clinic is following safety rules or what the "typical" patient looks like. Common techniques include frequency distribution, percentages, and proportions to assess compliance with rules, regulations and reimbursement standards. Visual tools such as bar charts, histograms, and run charts analyze trends over time, providing a visual illustration of the data.

Key Statistical Measures Auditors Should Know

Statistics provide a "snapshot" of performance, helping to identify areas for improvement in clinical care, documentation accuracy, and compliance without making broad generalizations about the entire population. Here are the most common descriptive statistics used in healthcare audits explained in simple terms:

Finding the Middle or Central Tendency (the “typical”)

Used to find the average or typical value in audit data. Auditors use these to identify the most common or "average" value in a group of charts. These statistics help identify the center or "middle" of the data set. Terminology associated with central tendency are:

Mean (average): The average value, calculated by adding all values and dividing by the total count. The sum of all values divided by the number of cases. It helps identify the average performance, such as the average length of stay.
Median (middle value): The middle value, often used to avoid skewing data with extreme outliers, especially in run charts. For instance, let’s say you have 5 patients waiting 10, 15, 20, 25, and 100 minutes to see the provider. The mean is 34 ((10+15+20+25+100)/5), but the median is 20. The median is better for spotting typical patient experience when a few outliers (like the 100-minute patient) skew the average.
Mode (most common value in the data set): The most frequently occurring data point. The mode helps auditors identify anomalies. If a provider's billing pattern shows a "mode" that differs significantly from peers (e.g., almost all visits are coded as complex), it serves as a red flag for review.

Measures of Dispersion (Variability or Spread)

Measures of Dispersion (also known as variability or spread) in a chart audit tell you how consistent or scattered your data is. While the average (mean) tells you where the center of the data is, the dispersion tells you if most records are close to that average or wildly different.

In a chart audit, high dispersion often means high variability in clinical practice, which might suggest a need for better standardization (e.g., in documentation, timing of care, or drug dosages).

Standard Deviation (SD): Measures the spread of data; a small standard deviation indicates data is tightly clustered around the mean. – An example – if the average audit score was 90% with an SD of 5% means most charts fall between 85% and 95%. SD is the most common, precise measure, but best used when data is roughly bell-shaped (normally distributed).
- Low SD = Data is consistent (most nurses/doctors documenting similarly).
- High SD = Data is inconsistent (wide variation in practice).
Range & Interquartile Range (IQR): Identifies the highest/lowest values and the spread of the middle 50% of the data. Excellent for skewed data or when you have outliers, as it ignores the extreme top and bottom, focusing on the "typical" records. It is a robust method identify the "normal" range of data while excluding extreme outliers that might skew results.

In a chart audit, high dispersion often means high variability in clinical practice, which might suggest a need for better standardization (e.g., in documentation, timing of care, or drug dosages). In summary, dispersion tells you if your performance is reliable (low spread) or unreliable (high spread).

Frequency & Proportions

Frequency and Proportions are the two primary, simple statistics used to turn raw medical record data into actionable information. Frequency measures how often a specific event, behavior, or error occurs in a set of charts. It is a simple raw number or count. Proportions (often presented as percentages) measure the frequency relative to the whole. It tells you what part of the total population or sample had the characteristic, rather than just the raw count.

Frequency Distribution (Raw Count): Illustrates how often specific criteria are met. Frequency is simply counting how many times something happened. It tells you the total volume.
- Example: You audit 50 charts to see if doctors signed their notes. You find that 40 charts have signatures. The frequency? 40.
Proportion (Percentages): Used to define compliance rates (e.g., % of charts with documented allergies). Proportion puts that count into context by comparing it to the total. It tells you the "score" or the rate of success.
- The Formula: (Number of times it happened) ÷ (Total number of charts checked). Example: Using the same 50 charts, you take the frequency (40) and divide it by the total (50). The Proportion? 0.80 or 80%.
Why use both?
- Frequency is great for understanding workload (e.g., "We had 100 falls this month").
- Proportion is better for measuring quality (e.g., "Only 2% of our patients had falls").

If you check 10 charts and find 5 errors, the frequency is low (only 5), but the proportion can be horrifying (50%).

Conclusion

Compliance auditors must understand audit statistics to ensure their findings are defensible, accurate, and scalable. A firm grasp of statistical concepts allows auditors to identify high-risk patterns of non-conformance while minimizing the risk of "false positives".

Furthermore, when regulatory bodies like CMS or the OIG perform audits, they often use extrapolation to project error rates into massive financial recoupments; an auditor who understands the underlying math can effectively validate or challenge these high-stakes calculations

For more information, consider enrolling in the Auditing for Compliance online course. Tuition includes online, proctored certification to earn your Certified Healthcare Auditor (CHA^SM) credential.

About the Author

Joanne Byron, BS, LPN, CCA, CHA, CHCO, CHBS, CHCM, CIFHA, CMDP, COCAS, CORCM, OHCC, ICDCT-CM/PCS is an educator with Officer of 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

American Institute of Healthcare Compliance

https://dev-main.aihc-assn.org/product/auditing-for-compliance/

National Library of Medicine – Descriptive Statistics

Purdue University – Descriptive Statistics

https://owl.purdue.edu/owl/research_and_citation/using_research/writing_with_statistics/descriptive_statistics.html

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Billing/RCM

What Government Enforcement Can Teach Us About Coding and Reimbursement

Written By: CJ Wolf, MD

This article presents educational information related to compliant documentation, coding and billing to avoid fraud, waste and abuse in our healthcare system. Dr. Wolf makes his point by presenting a qui tam case related to vascular diagnostic testing.

Medical coding is a critical aspect of accurate reimbursement for a variety of medical services. Many healthcare compliance enforcement actions, especially those brought under the Federal False Claims Act (FCA), stem from allegations of inaccurate coding.

Healthcare compliance professionals can learn a great deal from diving deep into the details of various enforcement actions. As it relates to medical coding, some enforcement actions teach compliance and coding professionals a great deal of how inaccurate coding can lead to significant investigations and multi-million-dollar settlements.

The details behind a recent $37 million settlement between the U.S. government and a medical device company, along with their former distributor, inform coders and compliance professionals about the risks of inaccurate coding related to a common medical condition known as peripheral arterial disease (PAD)¹.

PAD in the lower extremities is the result of narrowing or blockage of the arteries carrying blood with oxygen to the legs. A common symptom for patients with PAD is leg pain when walking. This type of pain is frequently referred to as claudication. Physicians use their clinical knowledge, experience and certain tests to diagnosis PAD and its varying degrees of severity.

One of the most common diagnostic tests utilized by physicians to evaluate PAD is the ankle brachial index (ABI). The test can help estimate the severity of the blockage, which is important when planning treatment and management options. Medicare has coverage policies and requirements for tests that can measure blood circulation in situations such as PAD. The critical policy that played a major role in this multi-million-dollar settlement is Medicare’s National Coverage Determination (NCD) 20.14 on plethysmography. Plethysmography involves the measurement and recording (by one of several methods) of changes in the size of a body part as modified by the circulation of blood in that part.

In addition, the definitions of certain Current Procedural Terminology (CPT®) codes, 93922, 92923, or 93924 must be accurately met to submit these codes on claims to Medicare for reimbursement of these diagnostic tests. The medical codes require that a provider conduct an ABI test plus certain additional testing. In addition, Medicare does not cover noninvasive vascular tests that use photoelectric plethysmography, also known as photoplethysmography (PPG), which uses a light sensor to detect changes in blood volume.

For example, the Medicare NCD classifies the types of technology used for the testing that is covered compared to those not covered. The covered and non-covered procedures from the NCD are listed below:

Covered

Segmental Plethysmography
Electrical Impedance Plethysmography
Ultrasonic Measurement of Blood Flow (Doppler)
Oculoplethysmography
Strain Gauge Plethysmography

Non-covered (Medicare considers these experimental)

Inductance Plethysmography
Capacitance Plethysmography
Mechanical Oscillometry
Photoelectric Plethysmography

Two experts in vascular diagnostic testing filed a qui tam, or whistleblower, lawsuit under the False Claims Act. They alleged the companies were marketing their devices to providers, such as physicians, telling them their testing device could be reimbursed by Medicare even though the procedure used is PPG, which is a non-covered classification as described in Medicare’s NCD. The government intervened in the case and joined in alleging that the medical codes submitted on claims to Medicare were inaccurate, thus the companies caused providers to submit false claims.

According to the legal complaint filed with the courts, the whistleblowers stated that the device manufacturer and their distributor promoted use of their PPG devices as easier, quicker, and less expensive than the use of Doppler technology for diagnosing PAD. They also claimed the companies said Medicare (and other government payers) pay out the same amount for any service that fits within a specific CPT code, irrespective of the actual cost to a medical provider to provide the service. Medical providers are consequently incentivized to perform the most inexpensive and least time-consuming services that qualify for a specific CPT code. Because the company claimed these PPG products are much less expensive and faster than the traditional diagnostic tests the devices can "diagnose" PAD within as little as five minutes, while traditional diagnostic tests take approximately 30-45 minutes.

The legal complaint also included materials about how the companies marketed the devices to providers.

The whistleblowers claimed:

The companies marketed one of their devices as a "new reimbursable office diagnostic test you can perform quickly and easily with no capital equipment purchase and no specialized personnel."
A physician gave a presentation at the New Cardiovascular Horizons (NCVH) conference and promoted the device as a method to help "increase your daily practice revenue." The presentation addresses the CPT codes that can purportedly be used to bill Medicare for services using the device and lists CPT codes 93922 and 93923.

Lessons Compliance Professionals Can Learn

Compliance professionals working for hospitals or physicians can learn a great deal from these details, such as:

First, compliance professionals should ensure the accuracy of any coding and reimbursement advice coming from device and/or pharmaceutical manufacturers.
Second, diligently review and follow Medicare and Medicaid coverage policies.
Third, go beyond just reading a medical code’s definition. Review enforcement settlements, audits, and authoritative references for the proper and intended use of medical codes.

This is just one of many enforcement actions that healthcare compliance professionals should be conversant about if they perform services for the common condition of PAD.

About the Author

CJ Wolf, MD, CPC, CPB, COC, AAPC Approved Instructor, is a highly regarded healthcare professional with more than 25 years of experience in revenue cycle management, practice management, compliance, coding, billing, auditing, and client services. He is a nationally recognized compliance thought leader who has published numerous articles and resources and has been featured at national conferences and events. He is a subject matter expert with Healthicity, a leading provider of compliance and auditing software solutions at https://www.healthicity.com

References:

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Quality

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.

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).
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).
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).
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. 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. (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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Quality

Coding Integrity and CAC

Why Quality Must Precede Compliance in Healthcare Documentation

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

Computer-Assisted Coding, better known as “CAC” has become the norm over the past decade, but are we producing compliant, accurate results? Compliance begins with quality. In the realm of clinical coding, that means ensuring that documentation tells the full story—and that the codes assigned accurately reflect that story. As CAC becomes more widespread, the need for trained human oversight becomes more critical, not less, which is the reason for this article.

Introduction

In today’s fast-paced healthcare environment, coding accuracy is often caught in the crossfire between compliance pressures, productivity demands, and evolving technology. While documentation may be clinically sound, coding associated with documentation can be misaligned or inaccurate, particularly when it is generated by CAC tools. CAC can trigger regulatory scrutiny, revenue cycle inefficiencies, and reputational risk without verification by an experienced coding first. As a compliance specialist and educator, I contend that quality cannot be compromised for speed or convenience. In fact, quality is the cornerstone of compliance.

Healthcare consultants recently noted that “documentation is often accurate, but the coding is not,” underscoring a critical gap in the way organizations approach their revenue cycle and risk management. This article explores the current landscape of coding discrepancies, the limitations and risks of CAC, and the essential need for robust internal review processes.

The Disconnect Between Documentation and Coding

In many provider organizations, clinical documentation accurately reflects the patient’s story—diagnoses, treatments, and provider decision-making—but coding processes fall short. Coders may misinterpret documentation, overlook nuances, or rely too heavily on automation, leading to miscoded encounters that can have ripple effects across billing, audit, and quality reporting systems. When errors go undetected, the result can be upcoded services, denied claims, compliance violations, and patient safety concerns. According to the Office of Inspector General (OIG), improper payments stemming from inaccurate coding continue to plague the Medicare program, costing billions annually (OIG, 2023).

CAC: A Double-Edged Sword

Computer-assisted coding (CAC) software, designed to improve speed and efficiency, is now a common fixture in health information management. While these systems can process large volumes of data quickly, their reliance on algorithms rather than clinical reasoning poses significant challenges.

Research has shown that CAC tools may struggle to interpret context, such as distinguishing between active and historical conditions, or differentiating provider impressions from definitive diagnoses (AHIMA, 2022). Without skilled human oversight, these limitations result in critical coding inaccuracies. Unfortunately, some healthcare systems mistakenly treat CAC outputs as final codes without sufficient validation.

Quality needs to be the focus to meet compliance standards. CAC should be a tool to enhance human accuracy—not replace it.

Compliance Risks from Coding Discrepancies

Coding discrepancies—particularly those uncorrected in CAC workflows—are not simply operational issues; they are compliance risks. Auditors from CMS, OIG, and commercial payers increasingly target mismatches between documentation and billing codes. These discrepancies may be flagged as potential fraud, waste, or abuse. Examples of common coding problems that trigger scrutiny include:

Upcoding or down coding visits that do not align with documentation
Inaccurate diagnosis coding affecting risk adjustment
Use of unspecified or non-supported codes
Failure to reflect clinical severity accurately

The DOJ's increased enforcement under the False Claims Act often centers on patterns of poor coding oversight. Healthcare entities must demonstrate that they are taking proactive steps to ensure coding integrity.

Quality as a Compliance Imperative

Ensuring the integrity of clinical coding isn’t just about reimbursement—it’s about compliance, patient care quality, and data accuracy. As healthcare moves toward value-based models, accurate coding supports correct risk adjustment, patient attribution, and performance measurement.

Implementing regular coding reviews, especially of CAC-assisted encounters, is a best practice that healthcare experts recommend. These reviews should be multidisciplinary, involving coding professionals, clinicians, and compliance officers. They help:

Identify patterns of misinterpretation or misclassification
Provide targeted coder education and clinical documentation improvement (CDI)
Verify whether CAC algorithms need adjustment or replacement

Quality assurance activities are not optional—they are essential to both ethical billing and regulatory compliance.

Balancing Productivity Pressures with Accuracy

It is well understood that providers are under immense pressure to manage high volumes of patients while fulfilling extensive documentation requirements. These constraints often lead to documentation fatigue and over-reliance on templated language or CAC tools. However, automation cannot replace clinical judgment or attention to detail. Coders must be trained to spot subtle inconsistencies and to understand that their role is pivotal in compliance integrity. Likewise, providers need CDI support that makes documentation more efficient and accurate—not more burdensome.

Healthcare leaders should prioritize investments in coder training, CDI collaboration, and coding audits rather than shortcutting review processes for the sake of productivity.

Recommendations for Compliance-Driven Coding Integrity

To address the systemic risks tied to coding discrepancies and CAC errors, organizations should implement the following:

Routine Internal Coding Audits: Conduct monthly or quarterly reviews of randomly selected encounters, with particular focus on high-risk services.
Coder & Provider Education: Offer ongoing training on documentation standards, code selection, and regulatory updates.
Review of CAC Outputs: Routinely validate CAC-generated codes against documentation. Never treat CAC outputs as final.
Real-Time Feedback Loops: Encourage communication between CDI specialists, coders, and providers to resolve discrepancies quickly.
Compliance-Focused KPI Tracking: Monitor error rates, denial trends, and audit findings to identify areas needing improvement.

Conclusion

Compliance begins with quality. In the realm of clinical coding, that means ensuring that documentation tells the full story—and that the codes assigned accurately reflect that story. As CAC becomes more widespread, the need for trained human oversight becomes more critical, not less.

Automation cannot replace accountability.

Compliance leaders must treat quality assurance and coding integrity as non-negotiable pillars of risk management. Let us not allow convenience to compromise compliance. Instead, let quality lead the way.

About the Author

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

References

American Health Information Management Association (AHIMA). (2022). The Realities of Computer-Assisted Coding. Retrieved from https://www.ahima.org
Office of Inspector General (OIG). (2023). Medicare Improper Payment Reports. Retrieved from https://oig.hhs.gov
Centers for Medicare & Medicaid Services (CMS). (2024). Evaluation and Management Services Guide. Retrieved from https://www.cms.gov
U.S. Department of Justice. (2023). False Claims Act Settlements and Judgments Exceed $2 Billion in Fiscal Year 2023. Retrieved from https://www.justice.gov/opa/pr

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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

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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