Unfamiliar with risk adjustment coding? Wondering what is hcc coding? understanding today’s risk adjustment model. Learn more about CMS-HCCs, and how they impact Medicare Advantage plan diagnosis coding and physician reimbursement. Continue reading as we delve into different risk assessment efforts and the business’ right risk adjustment services.
Hierarchical elimination of CMS-HCCs
CMS has implemented the Hierarchical Condition Categories (HCCs) in its risk adjustment coding model. Each HCC represents a clinical diagnosis with similar expected annual care costs. The CMS-HCC Risk Adjustment Model uses a subset of HCCs that meet specific criteria. The primary reason for this coding system’s inclusion is to improve the accuracy of risk adjustment coding.
Under the current model, each CMS-HCC is assigned a risk score based on the individual’s demographics, disease profile, and underlying comorbidity. The higher the risk score, the sicker the individual. The HCCs are assigned a diagnosis code, each with a coefficient representing its incremental contribution to overall costs. For each HCC, the risk score for that condition is calculated using the information submitted to CMS in the calendar year prior to the event.
In January 2014, CMS issued guidance for payment reductions ranging from 1.9% to 3.65%. This guidance affected physicians differently, but it lowered the error rate for those coding HCCs. Physicians should focus on learning the new system, as well as on improving their HCC documentation. A comprehensive approach is essential to adapting to the new risk adjustment coding environment.
Impact on diagnosis coding for Medicare Advantage plans
Health care providers should avoid making up codes and claiming they’ve never seen a patient. This practice can lead to denial of claims and overpayments. While some plans do pay for diagnosis coding, others do not. The federal government has investigated the problem and imposed new rules to curb it. A new analysis shows that Medicare Advantage plans may have higher diagnostic coding than Fee-for-Service plans.
In the year 2000, the CMS implemented a new system that incorporates the predicted health status of enrollees. Each enrollee receives a “risk score” based on their diagnosis codes—the lower the risk score, the healthier the beneficiary. The risk score is then used to adjust the payment made to plans. Higher risk scores mean the plan is more likely to pay higher reimbursements, while lower risk scores indicate lower payments.
The coding intensity adjustment is designed to reduce overpayments to MA plans. However, CMS has the option to reduce payments or raise the threshold. The current estimates assume that the entire adjustment will be applied to all plans by 2023. To minimize disruption to the MA plans, policymakers may ramp up the adjustments gradually over several years. However, implementing a plan-specific adjustment will be complicated. The initial adjustment should be fair for all plans and account for heterogeneity among contracts.
Impact on physician reimbursement
When you think about the impact of risk adjustment coding on physician reimbursement, you might think of the Affordable Care Act. By 2020, an estimated 8.3 million Americans will be enrolled in health plans that use risk adjustment coding. Most of these patients do not have a baseline risk adjustment factor score. For them, the risk adjustment score may increase by 3% for every actual change in the patient’s understanding. And, if the risk adjustment score projections are incorrect, you might end up with a year-end shortfall.
This system is administered by the CMS and is used to reimburse physician groups in Medicare Advantage plans, which have a risk-sharing relationship with insurance companies. Insurers pay in and out based on the risk associated with enrollees. While the current system seems to mitigate these risks, it can create disparities between health systems, create a stimulus for consolidation, and redirect resources away from programs and initiatives that aim to improve the quality of care. As a result, it detracts from the goal of health care reform.
Currently, the CMS has estimated that the problem will cost the nation $1 billion next year. It is mainly due to a lack of data for understanding member populations. Therefore, organizations must arm themselves with a robust risk adjustment coding program to counter this problem. The organization can better determine a member’s need for care and reduce risk exposure by ensuring coding accuracy. Furthermore, automated analytics tools can minimize the reconciliation process and mitigate risk exposure.
