Exploring the Impact of HCC Risk Adjustment Coding on Medical Service Reimbursement

HCC coding plays a critical role in Medicare Advantage Plan reimbursement. Specially designed risk adjustment models use diagnosis codes and demographics to assign patients a “risk score.” This score influences a healthcare organization’s prospective payment for treating its Medicare Advantage enrollees each contract year.

Identifying Patient Populations with High RAF Scores

Healthcare organizations must paint a full picture of their patient populations in the value-based payment environment. The hierarchical condition category (HCC) coding is one of the best ways to do this. HCC coding is a risk-adjustment model originally designed to estimate future patient healthcare costs by tracking all documented diagnoses from all inpatient, outpatient, and physician office encounters each calendar year.

The RAF score, heavily weighted on HCCs, determines how costly a patient is predicted to be. Each patient’s RAF score is calculated by analyzing the demographic information, medical history, and conditions documented in each patient encounter. This is a powerful tool that healthcare organizations can use to identify patients who are most likely to need high-cost and complex care.

Correct diagnosis coding is essential for accurate RAF scoring. However, because physicians often focus on documenting the presenting complaint, they frequently omit other conditions that could negatively impact their RAF scores. Incorrect ICD-10 codes can be found across the EHR, including symptom-based reporting, labs, medications, and radiology reports. HCC coders must review all relevant documentation to ensure the most complete and accurate picture of a patient’s health is painted. This will help avoid underpayment to a patient that results in unnecessary medical services being consumed. 

Identifying Patient Populations with High HCCs

HCCs are the basis for a patient’s risk score determining the capitation payment sent to Medicare Advantage plans from CMS. The RAF calculation is based on a complex formula that includes demographics, diagnosis, and other risk factors. To maintain a positive RAF score, providers need accurate documentation of every eligible encounter throughout the year linked to an APRN, PA, or physician’s documented diagnosis. Inaccurate or incomplete coding erroneously predicts the costs required to care for each individual and negatively affects reimbursement.

A key challenge to accurate coding is the sheer volume of diagnoses and conditions that must be accurately reported. This is especially true as healthcare shifts from fee-for-service to value-based care and encounter-based payments. In addition, coding gaps can be caused by missing or misplaced codes. Moreover, the presence of multiple HCCs often adds up to a higher overall RAF score than a single medical condition would have on its own.

Those struggling with HCC accuracy can take action by identifying patients more likely to have high RAF scores and taking steps to correct them. This can be done by evaluating data from EMR and claims to identify patterns of missed or under-reported HCCs. This can also be done by creating a clinical dashboard that can provide an at-a-glance view of EMR and claims data to identify HCC coding gaps and opportunities for improvement.

Identifying Patient Populations with Low HCCs

The risk adjustment model used by CMS and many commercial payers rely on the accuracy of International Classification of Diseases – 10 (ICD–10) diagnoses submitted to them on incoming claims. As a result, medical coders play a critical role in the HCC coding process. They must ensure that HCC codes are accurate and supported by the appropriate medical record documentation.

Coders must consider a patient’s active diagnosis during their face-to-face encounter with the healthcare provider and whether the diagnosis is still relevant to their clinical profile. Incorrect coding can impact the RAF score assigned to that patient, which may result in lower payments to physicians. As a result, healthcare organizations invest heavily in training their staff and implementing processes to help them document and code to the highest level of specificity. This is especially important in the case of Medicare Advantage plans and commercial insurers that utilize HCCs to calculate capitated payments for enrollees.

As a result, patients who are documented and coded correctly for chronic conditions can have their RAF scores adjusted next year, increasing the funding they receive from CMS. 

Identifying Patient Populations with Low RAF Scores

The HCC coding process is a key component of a payer’s ability to predict healthcare costs and assign reimbursement accurately. The federal Centers for Medicare and Medicaid Services (CMS) requires physicians to document all conditions related to their patient’s health status that may fall within an HCC category each year. This translates to thousands of hours physicians, and coders spend searching each medical record yearly.

The RAF score is based on demographic elements like age, sex, and residence and the disease risk scores submitted on claims from face-to-face encounters with qualified practitioners. As a result, a low RAF score can be misleading because it suggests that patients are healthier than they are. Often, these scores can result from inaccurately captured or reported diagnoses, indicating that medical records must include important information.

For example, a physician could report a single diagnosis of diabetes as “diabetes.” However, the patient has additional comorbidities that would significantly increase their RAF scores, such as major depressive disorder, bipolar disorder, and other psychiatric disorders. This additional information could significantly impact the patient’s total cost of care, so a provider must accurately capture all relevant diagnostic codes in their RAF models.