HAI Policy Briefs
Balancing Fairness and Efficiency in Health Plan Payments
It is no secret that the healthcare system is rife with inequities—from geography to race to class. Recent advances in data science and algorithmic fairness modeling are empowering researchers to quantify those inequities at unprecedented scale. These AI-driven opportunities may have a significant impact on the pricing of healthcare. This brief seeks to provide policymakers a new opportunity to realign the healthcare market’s incentives in favor of patients, recommending interventions that shape companies’ incentives around the pricing models they deploy—all of which come with their own trade-offs.
➜ Health insurance marketplaces use risk adjustment formulas to estimate how much money each enrollee will cost their plan—and to prevent insurers who take on sicker enrollees from suffering catastrophic losses.
➜ Current risk adjustment methods underpredict spending for specific groups like individuals with mental health and substance use disorders. Health policy researchers are developing new statistical methods to significantly improve this situation.
➜ Policymakers should familiarize themselves with the latest fairness research and metrics, engage with patient advocacy groups and insurers, support deeper research and exploration of fairness in the health insurance industry, and think beyond technical responses to consider legal and other remedies.