Whether an insurance company or a patient, you should know how Medicare risk adjustment information works. This information can be critical to your business’s success and patients’ health.
Monitor coding quality and accuracy
Identifying and monitoring coding quality and accuracy of medicare risk adjustment information is crucial to ensuring timely care delivery and increased reimbursement. In addition, many tools are available to health plans and providers to help provide accurate documentation of clinical conditions. Therefore, it’s crucial for health plans and provider organizations to work together to improve coding accuracy and patient care. For example, a study presented at the 16th Annual Forum of the Institute for Healthcare Improvement explored the relationship between hospital structural characteristics and coding accuracy.
As the aging population demands value-based care, health plans and provider organizations must work together to ensure accurate documentation of clinical conditions. This collaboration helps providers improve care quality and receive timely care.
A risk-adjustment model is a method used by the Centers for Medicare & Medicaid Services (CMS) to reflect the ongoing needs of a patient population. The model uses evidence-based patient care reporting to identify patients at risk for chronic conditions and incentivizes providers to document more thoroughly.
CMS-HCC for diagnosis-related risk
Using the Hierarchical Condition Category (HCC) for diagnosis-related risk adjustment information is a crucial way to calculate patient costs. The HCC model is used by the Centers for Medicare & Medicaid Services (CMS) to determine capitated reimbursement for Medicare Advantage plans.
Hierarchical condition category coding provides a comprehensive view of a patient’s health. It allows healthcare organizations to optimize data and educate clinicians about complex patients. It also helps to measure quality performance.
Private health plans, Medicaid, and Medicare Shared Savings Programs use the CMS-HCC risk adjustment model. It uses demographic information, diagnoses in the base year, and actuarial adjustments to assign Risk Adjustment Factor (RAF) scores.
In addition to risk adjustment, Hierarchical Condition Category (HCC) codes are used to calculate reimbursement. They are based on medical records submitted for reimbursement when the condition occurred. These codes are updated annually, and they represent specific medical conditions.
HCC codes represent acute and chronic health conditions. They are grouped clinically and are assigned to ICD-10-CM diagnosis groups. The categories are based on cost patterns and other factors.
CMS-HCC for prescription-related risk
Using the Hierarchical Condition Categories (HCC) model for prescription-related risk adjustment information can reduce the incentive for biased selection of patients in risk-based payment programs. In addition, this information is vital for healthcare organizations to understand because the HCCs directly affect how much money is received.
The HCC model is a prospective, additive, and hierarchical model that captures the risk of patients with varying health statuses. It uses diagnoses from the base year, outpatient, and professional claims. This information is used to calculate the risk adjustment factor (RAF), a measure of the estimated cost of care. This factor is then multiplied by a predetermined dollar amount.
The CMS-HCC model is a risk adjustment payment methodology that has been in use since 2004. It is also used in Medicare Advantage plans and healthcare organizations that provide services to Medicare enrollees.
The HCC model is based on the International Classification of Diseases, Tenth Revision, and Clinical Modification (ICD-10-CM). It is a hierarchical classification system that maps ICD-10 codes to 19 HCC categories.
CMS-RAPS for commercial risk adjustment
Several years ago, the Centers for Medicare and Medicaid Services (CMS) began collecting encounter data through the Encounter Data System (EDS). The system was designed to provide an electronic version of the encounter record, created from paper claims submitted by health care providers.
The EDPS system requires a substantial number of data elements. The EDPS system also requires significant capital to be invested in its infrastructure. In addition, the system is more complex than the RAPS system.
The CMS has made several important decisions to improve the quality of data collected and the accuracy of its risk-adjusted payments. Among the findings is implementing a risk adjustment formula to compare plan risk scores to the average risk score for all plans. The procedure is intended to eliminate gaps in risk scores. In addition, it may foster competition among plans.
The CMS has also introduced several metrics to improve data completeness. For example, it includes data filtering logic that validates the acceptable combinations of data elements.
The CMS has also published an Advance Notice of Methodological Changes for CY 2021. These changes will accelerate the data integrity and reconciliation processes. They will also identify gaps in risk scores.
Track reconfirmation rates of chronic medical conditions
A well-implemented risk adjustment system will ensure that contracted providers get enough resources to provide quality care. Conversely, a poorly implemented system could lead to overpayments for high-risk enrollees and underpayments for low-risk enrollees.
The Centers for Medicare & Medicaid Services (CMS) collects encounter data to determine risk ratings for Medicare Advantage members. The data consists of more than a dozen measures, such as the number of visits, type of care received, and medical conditions. In addition, it determines Medicare payments to health plans after beneficiaries have received care.
The nitpick is that the system doesn’t account for factors such as social support, medical care received by high-risk enrollees, and the complexities of the population served. The model is also susceptible to gaming and fraud. The model can be improved by altering data. For example, some experts propose using two years of claims data instead of one.
The encounter data that plans submitted was incomplete, according to the Medicare Payment Advisory Commission (MedPAC). As a result, it led to the demise of a dozen Medicare Advantage plans. The Centers for Medicare & Medicaid Services has since revised the data collection process and is on track to achieve its goal of reducing Medicare Advantage plan excess payments by $12.5 billion by the end of the year.