Data analytics in health promotion: Health market segmentation and classification of total joint replacement surgery patients

Providing insight into healthcare consumers' behaviors and attitudes is critical information in an environment where healthcare delivery is moving rapidly towards patient-centered care. We apply a two-stage methodology using both supervised and unsupervised machine learning methods to a patient data set from the electronic medical records of an academic medical center located in central Pennsylvania. The data are from patients who had total joint replacement surgery between December 2013 and September 2015. Two clustering methods and four classification algorithms were applied to the data set. Patients cluster into six distinct health market segments from which the cluster assignment is used as the response variable in supervised learning to classify patients. The classification model accurately predicts the cluster assignment for out-of-sample patients, while offering insight into patient behaviors and attributes to help clinicians, health marketers, and healthcare consumers move toward the goal of patient-centered and value-based healthcare. Published by Elsevier Ltd.