Regression by clustering using Metropolis-Hastings
This work addresses risk adjustment in health insurance to reduce inefficient insurer behavior and potentially improve healthcare access for the chronically ill, representing a domain-specific incremental improvement.
The authors tackled the problem of improving risk adjustment in health insurance markets by clustering diagnostic codes into optimal risk groups for health expenditure prediction, using a novel methodology based on Markov Chain Monte Carlo methods. Results from testing on data from 500,000 enrollees showed that their methodology outperformed common alternatives.
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.