www.sciedu.ca/jbar Journal of Business Administration Research Vol. 3, No. 2; 2014 Published by Sciedu Press 36 ISSN 1927-9507 E-ISSN 1927-9515 Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review Srikant Devaraj 1 , Sushil K. Sharma 1 , Dyan J. Fausto 2 , Sara Viernes 3 & Hadi Kharrazi 4 1 Ball State University, USA 2 Ascension Information Services, USA 3 ADA SCDI Working Group Member, USA 4 Johns Hopkins School of Public Health, USA Correspondence: Srikant Devaraj, Center for Business and Economic Research, Ball State University, USA. Tel: 1-765-285-4304. E-mail: sdevaraj@bsu.edu Received: June 28, 2014 Accepted: July 21, 2014 Online Published: July 24, 2014 doi:10.5430/jbar.v3n2p36 URL: http://dx.doi.org/10.5430/jbar.v3n2p36 Abstract The objective of the study was to identify potential barriers and facilitators to improve clinical practice using computer-based Clinical Decision Support System (CDSS). Studies published since 2000 were found using PubMed database, PsychInfo, CINAHL, EBSCOhost database, and Google scholar. Twenty-six relevant publications were examined. Thirty-five unique barriers and twenty-five unique facilitators were identified in the literature as important determinants of CDSS’s adoption in clinical practice. The list of barriers and facilitators collected from each study were then organized under the four dimensions of The Unified Theory of Acceptance and Use of Technology (UTAUT) model: performance expectancy, effort expectancy, social influence, and facilitating conditions. Some of the important barriers to CDSS use include; lack of time or time constraints, economic constraints (e.g., finance and resources), lack of knowledge of system or content, reluctance to use system in front of patients, obscure workflow issues, less authenticity or reliability of information, lack of agreement with the system, and physician or user attitude toward the system. The study contributes immensely to the literature by identifying the important barriers and facilitators of CDSS. Keywords: Decision support system, IT adoption, Clinical decision support system, UTAUT model, Barriers and facilitators 1. Introduction Information technology has become increasingly prevalent in all industries, especially in the healthcare arena. Recent decades have exhibited the development of a wide variety of information systems employed to aid clinicians in decision making. One such system is the computer-based Clinical Decision Support System (CDSS) developed to provide patient-specific, evidence-based advice in order to overcome some of the challenges facing the healthcare industry, such as diagnosing health problems, medication-prescribing effectiveness, and reducing medical errors, to name a few (Varonen, Kortteisto & Kaila, 2008). Despite convincing evidence for the effectiveness of CDSSs in medicine, they are, nevertheless, rarely used in clinical practice. Understanding the reasons behind this fact could be of help in finding tools that facilitate [its] further dissemination (Toth-Pal, Wardh, Strender & Nilsson, 2008). The aim of this paper is to identify the potential barriers and facilitators which hinder or enable CDSS use in clinical settings. Recognizing these factors will give management, along with Information Technology (IT) decision makers, a framework to successful CDSS implementation and use, enabling them to focus on specific factors which ensure definite outcomes. In order to provide well-defined constructs and fully exploit the findings of this paper, the Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to categorize the various barriers and facilitators to CDSS adoption. This model was developed through a review of eight theories or models which were found by earlier research on user acceptance: theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory (Venkatesh et al.). This model was validated and found to explain 70 percent of the variance in intention. According to Venkatesh,