Regarding Medicare risk adjustment (RADV), health plans must verify diagnoses are reflected accurately in medical records. This requires a strong, enduring partnership between payers, providers, and members.
Verify the Source Data
Whether it’s a Medicare Advantage plan, a Medicaid managed care plan, or a public health insurance exchange, and the risk adjustment process is essential to ensure that payers are compensated appropriately for their healthcare costs. It also levels the playing field to encourage providers and payers to offer members more preventive and appropriate care.
Accurate risk adjustment begins with collecting statistics and evaluating patients’ health conditions, medical history, and professional encounter data. This data is then used to calculate each member’s risk score and determine their payment level.
The data collected from face-to-face encounters between patients and healthcare providers must be accurate. The medical records must be signed by approved specialty types and contain the correct diagnosis codes for risk score calculation.
Second, the diagnostic categories should be clinically meaningful and predict future medical expenditures. Third, and perhaps most importantly, the data should have a sufficient sample size to allow for stable estimates of expenditures.
Fourth, the diagnoses should be associated with the specific disease processes that cause illness in a patient’s body. Unrelated diseases can skew predicted care costs and increase a person’s risk score.
Finally, the diagnoses should be consistent throughout a health plan’s coding and reimbursement cycle. This can be challenging to accomplish, especially when multiple coding domains are within the plan.
Verify the Target Data
Health plans are critical in managing the risk of their members’ care. By identifying and confirming the presence of chronic illnesses and preexisting conditions, they can ensure their member population has access to the services they need while ensuring proper compensation for their members’ health expenses.
As the number of diagnosis codes submitted by providers on claims increases, it becomes increasingly important for health plans to effectively use this data to identify and verify their members’ diagnoses. In addition, they must be able to accurately predict the future costs of their members’ care and the programs they need to pay for them.
To help health plans meet this challenge, CMS created a program called HHS-RADV (Risk Adjustment Data Validation) to ensure that the data used for Medicare Risk Adjustment is accurate and complete. The Medicare risk adjustment data validation uses various audits to ensure risk adjustment is based on accurate and complete data.
However, the risk adjustment model is complicated and involves many factors. For example, the model includes disease coefficients that determine how much or less a patient will be charged for healthcare services based on their diagnosed condition.
Verify the Schema
Risk adjustment is a critical process that pays health plans based on the proportion of members they serve who have chronic conditions. As such, all data used in risk adjustment must be accurate and comprehensive.
Using the Hierarchical Condition Category method, CMS assigns encounter data like medical diagnoses into groupings based on resource use. Higher category risk scores mean greater anticipated Medicare Advantage risks and healthcare costs.
To collect this information effectively, MAOs must have a robust and well-organized encounter reporting process that ensures they collect clean encounter data from all sources. They must use consistent coding rules and procedures across all types of healthcare providers, including hospital inpatient and outpatient facilities.
They must also have a robust quality assurance program that performs data quality checks before submitting encounters to CMS. These quality assurance processes should identify and correct rejected encounters that impact risk score accuracy, review duplicates to ensure they don’t miss diagnoses, and verify chart review data before submission.
Verify the Formats
Medicare Risk Adjustment is a statistical technique used in Medicare Advantage (MA) to adjust the federal government’s capitated payments to MA organizations (MAOs). It aims to help MAOs avoid enrollees with high medical costs. Ultimately, it can also prevent plans from gaming the system to get more revenue.
To ensure that CMS’s risk adjustment model is accurate, MAOs and their healthcare providers must verify the data submitted to the agency. This includes encounter data, which is information about the diagnoses and services that MA members have received in their lifetime.
CMS has recently begun using this encounter data to measure the accuracy of the risk score models it develops. However, this hasn’t been without its challenges.
One major problem is that encounter data is not a full substitute for the diagnoses and conditions that Medicare beneficiaries see in their lives. This is because not all healthcare providers document the same clinical conditions or provide the same types of services.
This is particularly true for chronic diseases, which often require multiple visits to the same healthcare provider over some time. For a patient’s risk scores to be accurate, their diagnoses must be linked to the same treatment they receive in their life.
Verify the Numbers
If you want to ensure that your risk adjustment program is working efficiently and accurately, verifying the numbers is critical. This includes ensuring diagnosis codes are being properly submitted, and the data is valid. It also involves ensuring that you have proper documentation for any diagnosed condition.
If there is a problem with a diagnosis code, there is a good chance that the MAO may not receive the payment that it expected. This is because CMS must make a “risk adjustment” to determine the base payment rate for the enrollee. Depending on the plan level, beneficiaries may have some conditions mapping to different CMS-HCCs. These conditions are then added together to calculate a risk score for the member. These risks can be chronic, severe, or acute, which is important to understand because these conditions are expected to affect the member’s healthcare costs over time. For example, diabetes with complications is more serious than diabetes without complications.
