Predicting Hospital Safety Measures using Patient Experience of Care Responses

To make healthcare more cost effective, the current trend in the U.S. is towards a hospital value-based purchasing program. In this program, a hospital's performance is measured in the safety, patient experience of care, clinical care, and efficiency and cost reduction domains. We investigate the efficacy of predicting the safety measures using the patient experience of care measures. We compare four classifiers in the prediction tasks and concluded that random forest and support vector machine provided the best performance.