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Published in Chapter:
Data-Driven Clinical Decision Support Systems Theory and Research
Cherie Noteboom (Dakota State University, USA), David Zeng (Dakota State University, USA), Kruttika Sutrave (Dakota State University, USA), Andrew Behrens (Dakota State University, USA), Rajesh Godasu (Dakota State University, USA), and Akhilesh Chauhan (Dakota State University, USA)
Copyright: © 2023
|Pages: 18
DOI: 10.4018/978-1-7998-9220-5.ch081
Abstract
Computerized clinical decision support systems (CDSS) have evolved rapidly and are causing a radical transformation in healthcare. These systems are integrating artificial intelligence-enabled mechanisms to improve the quality of care, health of populations, and reduce the costs of healthcare. Some of these mechanisms include descriptive, predictive, and prescriptive analytics to assist with clinical decision making. However, there are significant barriers to CDSS adoption, implementation, and acceptance among physicians and clinicians who utilize the systems to provide high-quality patient care. The authors used systematic literature review guidelines to the research questions: “To what extent is data-driven decision making applied in CDSS?” “What prevalent theories are used in data-driven CDSS research?” and “What major system attributes contribute to the theoretical frameworks?”