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 sciencedriven 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.
| Original language | English (US) |
|---|---|
| Title of host publication | 24th Workshop on Information Technology and Systems |
| Publisher | University of Auckland Business School |
| State | Published - 2014 |
| Event | 24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014 - Auckland, New Zealand Duration: Dec 17 2014 → Dec 19 2014 |
Other
| Other | 24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014 |
|---|---|
| Country | New Zealand |
| City | Auckland |
| Period | 12/17/14 → 12/19/14 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Computer Science Applications
Cite this
Healthcare analytics and clinical intelligence : A risk prediction framework for chronic care completed research paper. / Lin, Yu Kai; Chen, Hsinchun; Brown, Randall A.; Li, Shu Hsing; Yang, Hung Jen.
24th Workshop on Information Technology and Systems. University of Auckland Business School, 2014.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Healthcare analytics and clinical intelligence
T2 - A risk prediction framework for chronic care completed research paper
AU - Lin, Yu Kai
AU - Chen, Hsinchun
AU - Brown, Randall A.
AU - Li, Shu Hsing
AU - Yang, Hung Jen
PY - 2014
Y1 - 2014
N2 - 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 sciencedriven 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.
AB - 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 sciencedriven 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.
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M3 - Conference contribution
AN - SCOPUS:84924705962
BT - 24th Workshop on Information Technology and Systems
PB - University of Auckland Business School
ER -