Predicting Patient Turnover: Lessons From Predicting Customer Churn Using Free-Form Call Center Notes

Predicting Patient Turnover: Lessons From Predicting Customer Churn Using Free-Form Call Center Notes

Gregory W. Ramsey (Morgan State University, USA) and Sanjay Bapna (Morgan State University, USA)
DOI: 10.4018/978-1-5225-8244-1.ch006

Abstract

Predicting patient turnover within health services is beneficial for resource planning. In this chapter, patient turnover is viewed as a form of customer churn. As such, the authors examine whether free-form notes are useful for solving the classification problem typically associated with customer churn. The authors show that classifiers which incorporate free-form notes, using natural language processing techniques, are up to 11% more accurate than classifiers that are solely developed using structured data. In addition, the authors show that free-form notes aggregated for each account perform better than treating each note separately. It is suggested that hospitals and chronic disease management clinics can use structured data and free-form notes from electronic health records to predict which patients are likely to cease receiving care from their facilities. Classification tools for predicting patient churn are of interest to hospital administrators; such information can aid in resource planning and facilitates smoother handoffs between care providers.
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Literature Review

In this section, the authors review the literature on churn, applications of churn in the healthcare domain, models utilized to determine churn of customers, and text mining as applied to churn models.

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