Chapter one expands on concepts from the statistics, computer science, and health
economics literature to develop new fair regression methods for health plan payment risk …

The distribution of health care payments to insurance plans has substantial consequences
for social policy. Risk adjustment formulas predict spending in health insurance markets in …

Data-driven decision-making has justifiably come under scrutiny in recent years for its
tendency to perpetuate human biases, and few use-cases make the dangers as clear as …

The conventional method for developing health care plan payment systems uses existing
data to study alternative algorithms with the purpose of creating incentives for an efficient …

Modifications of risk adjustment systems used to pay health plans in individual health
insurance markets typically seek to reduce selection incentives at the individual and group …

Risk-adjustment systems used to pay health plans in individual health insurance markets
have evolved towards better “fit” of payments to plan spending, at the individual and group …

Two of the three elements of the ACA's “premium stabilization program,” reinsurance and
risk corridors, are set to expire in 2017, leaving risk adjustment alone to protect plans …

Health plan payment systems serve important objectives of social policy, including
encouraging the efficiency of the health insurance and healthcare markets, containing …

Risk adjustment is instituted to counter risk selection by accurately equating payments with
expected expenditures. Traditional risk‐adjustment methods are designed to estimate …

To combat adverse selection, governments increasingly base payments to health plans and
providers on enrollees' scores from risk-adjustment formulae. But because the variance of …