Regression by clustering using Metropolis-Hastings

TL;DR

This paper introduces a novel MCMC-based clustering method for risk adjustment in health insurance, improving expenditure prediction and potentially enhancing healthcare access for the chronically ill.

Contribution

It presents a new clustering approach using Metropolis-Hastings to optimize risk groups for better health expenditure prediction.

Findings

Outperforms common risk adjustment methods

Improves accuracy of health expenditure predictions

Potential to enhance healthcare access for chronic patients

Abstract

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.