Healthcare Analytics and Clinical Intelligence: A Risk Prediction Framework for Chronic Care

54 Pages Posted: 1 Jun 2014 Last revised: 20 Jun 2014

See all articles by Yu-Kai Lin

Yu-Kai Lin

Georgia State University - J. Mack Robinson College of Business

Hsinchun Chen

University of Arizona - Department of Management Information Systems

Randall Brown

University of Arizona

Shu-Hsing Li

National Taiwan University

Hung-Jen Yang

Min Sheng General Hospital

Date Written: June 4, 2014

Abstract

While recent research has suggested the tremendous potential of electronic health records (EHR) to transform healthcare, there remains a limited understanding of the best ways to utilize EHR data to improve clinical decision-making. Healthcare analytics based on EHR data may be able to offer a solution to the challenging goal of providing effective clinical decision support in chronic care. This paper takes a first step towards data-driven, evidence-based healthcare analytics in information systems research. Following the paradigms of design science and predictive analytics research, we propose, demonstrate and evaluate a design framework of risk prediction in the context of chronic disease management. Our framework draws on a large longitudinal real-world EHR dataset and evidence based guidelines to support data- and science-driven clinical decision making. We choose diabetes and coronary heart disease as our experimental cases, each with thousands of patients in their respective cohorts. The results of the experiments suggest that our design can achieve an accurate and reliable predictive performance and that the design is generalizable across chronic diseases. The design artifact and the experimental results contribute to the IS knowledge base and provide important theoretical and practical implications for design science, predictive analytics, and health IT research.

Keywords: design science, predictive analytics, health informatics, time-to-event modeling, theories of abstraction, electronic health records, EHR, health IT, evidence-based medicine

Suggested Citation

Lin, Yu-Kai and Chen, Hsinchun and Brown, Randall and Li, Shu-Hsing and Yang, Hung-Jen, Healthcare Analytics and Clinical Intelligence: A Risk Prediction Framework for Chronic Care (June 4, 2014). Available at SSRN: https://ssrn.com/abstract=2444025 or http://dx.doi.org/10.2139/ssrn.2444025

Yu-Kai Lin (Contact Author)

Georgia State University - J. Mack Robinson College of Business ( email )

P.O. Box 4050
Atlanta, GA 30303-3083
United States

HOME PAGE: http://robinson.gsu.edu/profile/yu-kai-lin/

Hsinchun Chen

University of Arizona - Department of Management Information Systems ( email )

AZ
United States

Randall Brown

University of Arizona ( email )

Department of History
Tucson, AZ 85721
United States

Shu-Hsing Li

National Taiwan University ( email )

50 Lane 144, Section 4
Taipei 32026
Taiwan
886 2 3366 1117 (Phone)

Hung-Jen Yang

Min Sheng General Hospital ( email )

No. 168, Jingguo Rd
Taoyuan County, 33044
Taiwan

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