Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.
Journal:
Health care management science
Published Date:
Apr 29, 2014
Abstract
A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.
Authors
Keywords
Artificial Intelligence
Bayes Theorem
Birth Weight
Diagnosis-Related Groups
Female
Gestational Age
Hospital Administration
Hospital Mortality
Humans
Intensive Care Units, Neonatal
Logistic Models
Outcome Assessment, Health Care
Pregnancy
Pregnancy Complications
Premature Birth
Prenatal Care
Risk Adjustment
Socioeconomic Factors