Patient segmentation and prediction of patient behaviors using the EMR and Machine Learning

In a recent CHOT study, researchers found healthcare organizations can effectively target specific segments of joint replacement patients with readily available information from the electronic medical record (EMR). The goal of this study was to apply unsupervised learning (clustering) methods to a large subset of the patient data to identify unique clusters of patients. By using a new algorithm developed by CHOT researchers, clinicians, can adopt specific protocols during the perioperative phases of Total Joint Arthroplasty by patient cluster, health insurers can isolate high costs and high-risk patients and then incentivize them and their primary care providers to reduce health risks, and patients can learn from their segment what actions to take now to optimize outcomes. Health organizations don’t need to purchase expensive market data to fully understand how patients cluster and respond to health promotions. This innovative algorithm presents an opportunity to efficiently and effectively target segments of the market with health promotions tailored specifically to positively impact health outcomes using existing data in the EMR.

Swensn, E. R., Bastian, N. D., & Nembhard, H. B. (2016). Data analytics in health promotion: Health market segmentation and classification of total joint replacement surgery patients. Expert Systems with Applications60, 118-129.

 

Research Contact:
Eric Swenson
ers187@psu.edu

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