Welcome to the InfoSci Platform
Reference Hub1
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, Sanjay Bapna
Copyright: © 2019 |Pages: 25
ISBN13: 9781522582441|ISBN10: 1522582444|ISBN13 Softcover: 9781522592037|EISBN13: 9781522582458
DOI: 10.4018/978-1-5225-8244-1.ch006
Cite Chapter Cite Chapter

MLA

Ramsey, Gregory W., and Sanjay Bapna. "Predicting Patient Turnover: Lessons From Predicting Customer Churn Using Free-Form Call Center Notes." Computational Methods and Algorithms for Medicine and Optimized Clinical Practice, edited by Kwok Tai Chui and Miltiadis D. Lytras, IGI Global, 2019, pp. 108-132. https://doi.org/10.4018/978-1-5225-8244-1.ch006

APA

Ramsey, G. W. & Bapna, S. (2019). Predicting Patient Turnover: Lessons From Predicting Customer Churn Using Free-Form Call Center Notes. In K. Chui & M. Lytras (Eds.), Computational Methods and Algorithms for Medicine and Optimized Clinical Practice (pp. 108-132). IGI Global. https://doi.org/10.4018/978-1-5225-8244-1.ch006

Chicago

Ramsey, Gregory W., and Sanjay Bapna. "Predicting Patient Turnover: Lessons From Predicting Customer Churn Using Free-Form Call Center Notes." In Computational Methods and Algorithms for Medicine and Optimized Clinical Practice, edited by Kwok Tai Chui and Miltiadis D. Lytras, 108-132. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8244-1.ch006

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.