Risk Adjustment Equity
Background and Purpose
Risk adjustment has been utilized for many years in different settings, primarily as a tool to help properly compensate insurers for the risks they take when enrolling members in their insurance plans. Risk adjustment has been regularly used by ACA, Medicare Advantage and Medicaid product lines to help normalize risks across plan participants. While risk adjustment is a useful tool to help account for risk disparities between different individuals and health plans, it is not perfects and may result in the potential for inequity. For example:
- Risk adjustment may tend to distort the risk scores of individuals at either end of the risk-score scale such that individuals at the low-score end of the scale get assigned artificially high risk scores, and individuals at the high-score end of the scale get assigned artificially low risk scores.
- Coding may drive artificial differentials in risk scoring, particularly in the Medicare Advantage population, or in different uses of social determinants of health (SDOH) codes.
- Risk adjustment algorithms may reflect other inherent bias(es) that may result in artificially worse outcomes for underprivileged groups.
Research Objective and Deliverables
This research would examine risk adjustment methodologies with particular focus on potential biases inherent within the algorithms used and identify and quantify how they can potentially distort predictions of claims costs to potentially result in worse outcomes for underprivileged and underserved populations. This research would identify risk adjustment methodologies that most appropriately allocate health care risks while minimizing negative impacts on at-risk populations and demonstrate how risk adjustment can be used to improve health outcomes of at-risk populations.
Target Audience and Impact
Health actuaries who develop and utilize risk adjustment methodologies will better understand the potential for inequitable risk adjustments as well has how to equitably distribute risk among health plans and members and will be better able to identify and support at-risk populations.
Estimated Cost
$50,000
Estimated Timeline
Approximately 4 months