Healthcare analytics and clinical intelligence: A risk prediction framework for chronic care completed research paper

Yu Kai Lin, Hsinchun Chen, Randall A. Brown, Shu Hsing Li, Hung Jen Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 languageEnglish (US)
Title of host publication24th Workshop on Information Technology and Systems
PublisherUniversity of Auckland Business School
StatePublished - 2014
Event24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014 - Auckland, New Zealand
Duration: Dec 17 2014Dec 19 2014

Other

Other24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014
CountryNew Zealand
CityAuckland
Period12/17/1412/19/14

Fingerprint

Health
Decision making
Medical problems
Information systems
Experiments
Predictive analytics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Computer Science Applications

Cite this

Lin, Y. K., Chen, H., Brown, R. A., Li, S. H., & Yang, H. J. (2014). Healthcare analytics and clinical intelligence: A risk prediction framework for chronic care completed research paper. In 24th Workshop on Information Technology and Systems University of Auckland Business School.

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 proceedingConference contribution

Lin, YK, Chen, H, Brown, RA, Li, SH & Yang, HJ 2014, Healthcare analytics and clinical intelligence: A risk prediction framework for chronic care completed research paper. in 24th Workshop on Information Technology and Systems. University of Auckland Business School, 24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014, Auckland, New Zealand, 12/17/14.
Lin YK, Chen H, Brown RA, Li SH, Yang HJ. Healthcare analytics and clinical intelligence: A risk prediction framework for chronic care completed research paper. In 24th Workshop on Information Technology and Systems. University of Auckland Business School. 2014
Lin, Yu Kai ; Chen, Hsinchun ; Brown, Randall A. ; Li, Shu Hsing ; Yang, Hung Jen. / Healthcare analytics and clinical intelligence : A risk prediction framework for chronic care completed research paper. 24th Workshop on Information Technology and Systems. University of Auckland Business School, 2014.
@inproceedings{ec8bc667f2854a548a72e3c4c2f3d702,
title = "Healthcare analytics and clinical intelligence: A risk prediction framework for chronic care completed research paper",
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.",
author = "Lin, {Yu Kai} and Hsinchun Chen and Brown, {Randall A.} and Li, {Shu Hsing} and Yang, {Hung Jen}",
year = "2014",
language = "English (US)",
booktitle = "24th Workshop on Information Technology and Systems",
publisher = "University of Auckland Business School",

}

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.

UR - http://www.scopus.com/inward/record.url?scp=84924705962&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84924705962&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84924705962

BT - 24th Workshop on Information Technology and Systems

PB - University of Auckland Business School

ER -