  High quality risk adjustment in health insurance markets weakens insurer
incentives to engage in inefficient behavior to attract lower-cost enrollees.
We propose a novel methodology based on Markov Chain Monte Carlo methods to
improve risk adjustment by clustering diagnostic codes into risk groups optimal
for health expenditure prediction. We test the performance of our methodology
against common alternatives using panel data from 500 thousand enrollees of the
Colombian Healthcare System. Results show that our methodology outperforms
common alternatives and suggest that it has potential to improve access to
quality healthcare for the chronically ill.
