Algorithmic Prediction of Health-Care Costs

The rising cost of health care is one of the world's most important problems. Accordingly, predicting such costs with accuracy is a significant first step in addressing this problem. Since the 1980s, there has been research on the predictive modeling of medical costs based on (health insurance) claims data using heuristic rules and regression methods. These methods, however, have not been appropriately validated using populations that the methods have not seen. We utilize modern data–mining methods, specifically classification trees and clustering algorithms, along with claims data from over 800,000 insured individuals over three years, to provide rigorously validated predictions of health–care costs in the third year, based on medical and cost data from the first two years. We quantify the accuracy of our predictions using unseen (out–of–sample) data from over 200,000 members. The key findings are: (a) our data–mining methods provide accurate predictions of medical costs and represent a powerful tool for prediction of health–care costs, (b) the pattern of past cost data is a strong predictor of future costs, and (c) medical information only contributes to accurate prediction of medical costs of high–cost members.