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    Health Analytics and Predictive Modeling: Four Essays on Health Informatics

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    Author
    Lin, Yu-Kai
    Issue Date
    2015
    Keywords
    Health Analytics
    Information Systems
    Management Information Systems
    Electronic Health Records
    Advisor
    Chen, Hsinchun
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    There is a marked trend of using information technologies to improve healthcare. Among all the health IT, electronic health record (EHR) systems hold great promises as they modernize the paradigm and practice of care provision. However, empirical studies in the literature found mixed evidence on whether EHRs improve quality of care. I posit two explanations for the mixed evidence. First, most prior studies failed to account for system use and only focused on EHR purchase or adoption. Second, most existing EHR systems provide inadequate clinical decision support and hence, fail to reveal the full potential of digital health. In this dissertation I address two broad research questions: a) Does meaningful use of EHRs improve quality of care? and b) How do we advance clinical decision making through innovative computational techniques of healthcare analytics? To these ends, the dissertation comprises four essays. The first essay examines whether meaningful use of EHRs improve quality of care through a natural experiment. I found that meaningful use significantly improve quality of care, and this effect is greater in historically disadvantaged hospitals such as small, non-teaching, or rural hospitals. These empirical findings present salient practical and policy implications about the role of health IT. On the other hand, in the other three essays I work with real-world EHR data sets and propose healthcare analytics frameworks and methods to better utilize clinical text (Essay II), integrate clinical guidelines and EHR data for risk prediction (Essay III), and develop a principled approach for multifaceted risk profiling (Essay IV). Models, frameworks, and design principles proposed in these essays advance not only health IT research, but also more broadly contribute to business analytics, design science, and predictive modeling research.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Management Information Systems
    Degree Grantor
    University of Arizona
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