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 3.5 million 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.
Simón Ramírez Amaya (Quantil Matemáticas Aplicadas)
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2018 : Poster session: Contributed papers »
Michael Cvitkovic · Arijit Patra · Yunpeng Li · RAHMAN BANYA SAFF SANYA · Guanghua Chi · Benjamin Huynh · Hamed Alemohammad · Simón Ramírez Amaya · Nazmus Saquib · Jade Abbott · Teo de Campos · Viraj Prabhu · Alvaro Riascos · Hafte Abera · praney dubey · Tanushyam Chattopadhyay · Hsiang Hsu · Mayank Jain · Kartikeya Bhardwaj · Gabriel Cadamuro · Bradley Gram-Hansen · Georg Dorffner