A segmentation approach to patient health intervention

Abstract

There has been an increasing interest in considering models that try to predict which patients will be readmitted to hospitals, or, if not yet having been a hospital patient, which patients will need hospitalization. One major reason for this is to optimize the intervention process, thereby saving billions of health-care dollars. In this article, we use patient-data from a large healthcare organization and attempt different segmentation models to identify patients who are most at risk for hospitalization. One of these models uses multiple linear regression to provide a prediction of a patient’s subsequent year’s hospitalization, and examines the variables that are significant in the prediction process. Two subsequent segmentations use logistic-regression to explore (1) how well we can discriminate between those who will be hospitalized and those who will not be hospitalized, and (2) those who will be hospitalized for a “lengthy stay” (≥15 days) vs. those who will not be hospitalized. We adopt an approach from the database-marketing literature, and consider “lift” and Pareto curves to evaluate the success of the segmentations. Our findings suggest that there is great potential for this segmentation approach. This approach provides not only profit motivation for the focal (healthcare) organization, an HMO, pharmaceutical company, or similar organization, but also the societal benefit of more effective and cheaper healthcare.