The implementation of the Patient Protection and Affordable Care Act (PPACA) makes risk adjustment an increasingly important element of regulation-driven health insurance products.  Medicare Advantage, Part-D and many Medicaid programs are already leveraging risk adjustment for plan payments, and healthcare reform mandates risk adjustment for Exchanges, as well as Pioneer ACOs. 

Risk adjustment was first introduced as a compliance component in the Balanced Budget Act of 1996, which mandated a phased-in approach to set budgets, as well as payments to Medicare Advantage Plans.  Subsequently, the Federal Center for Medicare and Medicaid Services (CMS) created a risk adjustment methodology called Hierarchical Condition Categories (CMS-HCCs). 

Risk adjustment (or risk assessment) methodologies calculate risk for each individual, which supports comparisons of illness burden between or among health plans.  This methodology is designed to “level the playing field” between insurers, and requires a two-step process:  Assessing the illness burden of each enrollee (risk assessment), then moving funds from plans which enroll lower risk populations to those who cover higher risk members (risk adjustment). 

Beginning in 2014, risk adjustment will be one of three mandated methods designed to reduce adverse selection.  As rules are promulgated, insurers will be responsible to maintain and provide the necessary data to run risk adjustment models.  This process will be important for insurers offering small group and individual health insurance inside and outside of the Exchanges, since risk adjustment will occur in both environments.

Health plans without adequate data may be disadvantaged in risk adjustment.  Two examples:

• Plans which administer capitated provider contracts but do not collect diagnosis data will have less information on the illness burden of their members. 
• Plans will need to assure that data collection is a requirement in behavioral health carve-out contracts, so that the risk associated with mental health is included in their calculated scores.  

The Federal Department of Health and Human Services (HHS) is setting the protocols and processes for risk adjustment, including defining the data elements required, recommending data collection processes, and requirements for acceptable risk adjustment models. 

Once the data have been collected, the results will be subject to audit.  Existing approaches require plans to submit a statistically valid sample of data for audit, although future methodology may allow for self-audit by plans, or from approved auditors (using methods similar to HEDIS audits), in order to compare against plan average risk metrics established by HHS.   Adjustments will be made to the risk scores (and resulting charges/payments) based on the error rates found through the audit. 

Many potential challenges exist for payers, which will be further clarified based on emerging rules in the coming months.  For example, plans will face hurdles deriving and maintaining risk adjustment data that can stand up to audits, and the deidentification that will be required in many instances.   The possibility exists for payers to use data warehouses to facilitate this process, although the methodology favored by regulators in still being determined; for example, a “detailed data collection” method versus a “distributed” method, the latter which would typically maintain data on cloud servers maintained by the health insurance carriers. 

Since data is already collected, validated, aggregated and analyzed for multiple purposes in these environments, it may be possible to utilize these existing capabilities for risk adjustment data collection, health care analytics and reporting.  While some approaches may not be as effective (for example, all-payer claims databases often do not have the legal authority to use for risk adjustment purposes – when they were created, a central risk adjustment process was typically not under consideration), multiple avenues should be considered to assist in this complex and vitally important compliance process.