Electronic Health Reporter

By Eric McGuire, senior vice president, Medical Coding and CDI Service Lines and Corporate Strategy, AGS Health.

Traditional fee-for-service reimbursements are falling by the wayside as healthcare continues its transition toward value-based reimbursement models. This is evidenced by data from the Health Care Payment & Learning Action Network (LAN), which shows more than half of healthcare payments in 2022 were made under value-based care models. Additionally, nearly 75% of health plan leaders surveyed by LAN believe value-based care model activity will continue to rise.

Integral to this transition – which requires providers to better manage patient costs based on a clear, concise, and comprehensive picture of patients’ health and medical conditions – are Hierarchical Condition Category (HCC) codes. Used by the Centers for Medicare and Medicaid Services (CMS) and commercial payers to forecast medical costs for patients with more complex healthcare needs, the HCC risk adjustment model measures relative risk due to health status to determine reimbursement levels. The more complex the patient’s medical needs, the higher the provider’s payment.

In fact, HCCs are the preferred method of risk adjustment for the Medicare population, which includes nearly 60 million people on both Part A and Part B, CMS reports. As such, accurate HCC management is critical for appropriate reimbursement of the care provided to Medicare patients and beneficiaries.

Accuracy is Key

The highly complex HCC model includes approximately 10,000 diagnosis codes that map to HCC codes and 189 different HCC categories with 87 CMS-HCCs, each of which represents diagnoses with similar clinical complexity and expected annual costs of care. Any error can significantly impact reimbursements, which under HCC is determined by mapping a patient’s diagnoses to these codes to create a Risk Adjustment Factor (RAF) score.

The RAF score represents the estimated cost of caring for that patient based on their disease burden and demographic information. It is then multiplied by a base rate to set the provider’s per-member-per-month (PMPM) reimbursement amount. The sicker the patient, the higher the RAF score and, subsequently, the provider’s reimbursement.

Each year, CMS publishes a list of diagnosis codes and corresponding HCC category. Hierarchies (or ‘Families’ of categories) are listed among related condition categories, which set values based on the severity of illnesses. Improperly documenting HCC codes, or failing to document the highest appropriate specificity, results in lower reimbursement rates. For example, HCC 19 (diabetes with no complications) might pay an $894.40 premium bonus compared to a bonus of $1,273.60 for diabetes with ESRD, which requires two HCC codes mapping to 18 and 136.

Conversely, properly documenting HCCs at the highest appropriate specificity can boost reimbursements. For example, if CMS has set a $1,000 PMPM for a patient with an RAF of 2.234 who has diabetes with complications reimbursement would be just $673 per month if the condition is not coded. However, if the case was properly coded as E11.9 Type 2 diabetes mellitus without complications under HCC19 Diabetes without complications, the RAF increases to 2.366, resulting in reimbursement of $1,062 per month. If properly coded as E11.41 Type 2 diabetes mellitus w/diabetic mononeuropathy under HCC18 Diabetes w/ chronic complications, the RAF increases to 2.513 for a reimbursement of $1,312.5.

HCC coding is also vital for population health management. Failing to capture a comprehensive and accurate picture of the health and risks of a patient population can lead not only to reduced reimbursements but also to inaccurate or ineffective decision-making regarding interventions and investments. For example, risk scores that inaccurately reflect a population’s congestive heart failure rate could result in a provider organization investing resources into something other than cardiac care, potentially impacting outcomes and revenues.

Interlocking Challenges

Coding for HCC is particularly complex due to its interlocking steps, as hierarchies ensure a patient is coded for the most severe manifestation among related diseases. Diagnosis codes roll up to diagnostic categories, which are included in condition categories, which then become HCCs. Each mapped diagnosis must be supported with documented evidence to ensure timely, accurate, and appropriate coding and billing.

Optimal accuracy is achieved by leveraging HCC tables to capture diagnosis codes, complication/comorbid conditions (42% of HCCs), and major complication/comorbid conditions (16% of HCCs). Also crucial is optimizing MS-DRGs assignments that confirm the severity of illness and risk of mortality. Thus, clinical documentation is the foundation for proper HCC assignment.

However, the HCC documentation and coding process is fraught with challenges, which typically fall into three categories.

1. Incomplete medical records, which can lead to undercoding, resulting in lower reimbursements, inaccurate RAF scores, downgrades to lower hierarchical category levels, and bad investment decisions to support the patient population. It is a challenge that can be exacerbated by coders working in a manual environment who may not recognize they are working with incomplete records.
2. Limited resources, including coding specialists with the skills and experience necessary to properly evaluate a patient’s chart and extrapolate the information needed to document the appropriate HCC category.
3. Complex and rapidly evolving regulations, which can be difficult to stay on top of, particularly in a manual environment, leaving coders to work from outdated HCC code sets and guidebooks.

Many organizations also struggle to engage physicians in the query process, which hampers efforts to improve documentation related to risk-adjusted coding. Additionally, because physicians embrace technology at varying rates, it is often necessary to employ multiple communication methods to succeed at risk-adjusted and HCC coding.

Maximizing Appropriate Reimbursement

To optimize HCC coding and subsequent reimbursement, there are several key areas upon which providers should focus. First is adhering to the MEAT (monitor, evaluate, assess/address, and treat) criteria to support proper documentation, which coders use to help them correctly identify and assign HCC chronic condition diagnoses. Payers also use MEAT to account for the overall health and medical cost expectations of each patient enrolled in a health plan. This is important, as value-based payment models that require providers to carry greater financial risk are becoming the norm.

A second focal point is the patient population. Focus on areas with the most significant impact on risk adjustment and high value and volume encounters. Implement outpatient clinical documentation improvement (OP CDI) initiatives where appropriate to close documentation gaps, shore up weaknesses, and improve evidence capture.

Third, deploy technology tools that can facilitate needed improvements in HCC coding and documentation. These include computer-assisted coding (CAC) solutions that update automatically, as well as:

• Computer-assisted professional coding (CAPC) tools that leverage natural language processing (NLP), natural language understanding (NLU), and machine learning (ML) to automatically annotate documentation and autosuggest ICD-10 with HCC mappings for improved diagnosis capture, as well as identify diagnosis without supporting evidence and estranged evidence without a diagnosis.
• HCC ROI calculators or RAF aggregation tools that optimize HCC coding and accurately capture all relevant diagnoses.
• Dashboards that provide an accurate reflection of a provider’s scores for real-time management.

Provider-health plan relationships are also integral to any HCC improvement strategy. Request regular updates on payer diagnosis codes, which will help providers identify what is missing or no longer allowed.

Further, ensure physician engagement by developing a program that balances the use of auto-generated queries and NLP-based functionality with meaningful CDI. For example, some providers prefer a checklist in the medical record, while others respond better to more direct prospective OP CDI reviews.

Finally, leverage internal audits to identify problem areas and design appropriate interventions. Retrospective audits can bridge documentation gaps and identify areas for additional provider and coder education, while prospective audits can help improve how physicians document care and how coders code health conditions, translating into improved financial performance.

Conclusion

Compliant and accurate HCC coding is vital to providers’ and payers’ financial strength, as well as to the health of patient populations. The obstacles and challenges inherent to the HCC process can be eliminated with properly designed and implemented strategies designed to ensure appropriate documentation that supports accurate coding at the highest specificity. This, in turn, leads to significantly improved bottom lines and patient outcomes.