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      A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping

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          Abstract

          Background

          The implementation of clinical decision support systems (CDSSs) as an intervention to foster clinical practice change is affected by many factors. Key factors include those associated with behavioral change and those associated with technology acceptance. However, the literature regarding these subjects is fragmented and originates from two traditionally separate disciplines: implementation science and technology acceptance.

          Objective

          Our objective is to propose an integrated framework that bridges the gap between the behavioral change and technology acceptance aspects of the implementation of CDSSs.

          Methods

          We employed an iterative process to map constructs from four contributing frameworks—the Theoretical Domains Framework (TDF); the Consolidated Framework for Implementation Research (CFIR); the Human, Organization, and Technology-fit framework (HOT-fit); and the Unified Theory of Acceptance and Use of Technology (UTAUT)—and the findings of 10 literature reviews, identified through a systematic review of reviews approach.

          Results

          The resulting framework comprises 22 domains: agreement with the decision algorithm; attitudes; behavioral regulation; beliefs about capabilities; beliefs about consequences; contingencies; demographic characteristics; effort expectancy; emotions; environmental context and resources; goals; intentions; intervention characteristics; knowledge; memory, attention, and decision processes; patient–health professional relationship; patient’s preferences; performance expectancy; role and identity; skills, ability, and competence; social influences; and system quality. We demonstrate the use of the framework providing examples from two research projects.

          Conclusions

          We proposed BEAR (BEhavior and Acceptance fRamework), an integrated framework that bridges the gap between behavioral change and technology acceptance, thereby widening the view established by current models.

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          Most cited references34

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          Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

          Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials. To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit. We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information. We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes. Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes. One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001). Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.
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            Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis

            Background Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall. Objective The purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance. Methods A critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician). Results Favorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs. Conclusions This research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients.
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              An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit).

              The realization of Health Information Systems (HIS) requires rigorous evaluation that addresses technology, human and organization issues. Our review indicates that current evaluation methods evaluate different aspects of HIS and they can be improved upon. A new evaluation framework, human, organization and technology-fit (HOT-fit) was developed after having conducted a critical appraisal of the findings of existing HIS evaluation studies. HOT-fit builds on previous models of IS evaluation--in particular, the IS Success Model and the IT-Organization Fit Model. This paper introduces the new framework for HIS evaluation that incorporates comprehensive dimensions and measures of HIS and provides a technological, human and organizational fit. Literature review on HIS and IS evaluation studies and pilot testing of developed framework. The framework was used to evaluate a Fundus Imaging System (FIS) of a primary care organization in the UK. The case study was conducted through observation, interview and document analysis. The main findings show that having the right user attitude and skills base together with good leadership, IT-friendly environment and good communication can have positive influence on the system adoption. Comprehensive, specific evaluation factors, dimensions and measures in the new framework (HOT-fit) are applicable in HIS evaluation. The use of such a framework is argued to be useful not only for comprehensive evaluation of the particular FIS system under investigation, but potentially also for any Health Information System in general.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                August 2020
                6 August 2020
                : 22
                : 8
                : e18388
                Affiliations
                [1 ] Department of Biomedical Informatics School of Medicine University of Pittsburgh Pittsburgh, PA United States
                [2 ] I&E Meaningful Research Bogotá Colombia
                [3 ] School of Medicine Pontificia Universidad Javeriana Bogotá Colombia
                [4 ] Department of Learning Health Sciences University of Michigan Ann Arbor, MI United States
                [5 ] Department of Pharmacy and Therapeutics School of Pharmacy University of Pittsburgh Pittsburgh, PA United States
                Author notes
                Corresponding Author: Jhon Camacho jjcamachosanchez@ 123456gmail.com
                Author information
                https://orcid.org/0000-0003-1805-9142
                https://orcid.org/0000-0002-2893-2374
                https://orcid.org/0000-0002-9117-9338
                https://orcid.org/0000-0001-7523-4846
                https://orcid.org/0000-0002-2993-2085
                Article
                v22i8e18388
                10.2196/18388
                7441385
                32759098
                7729a939-ffdc-49ad-a3d5-158d581c318b
                ©Jhon Camacho, Manuela Zanoletti-Mannello, Zach Landis-Lewis, Sandra L Kane-Gill, Richard D Boyce. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.08.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 2 March 2020
                : 18 March 2020
                : 11 May 2020
                : 3 June 2020
                Categories
                Original Paper
                Original Paper

                Medicine
                clinical decision support system,computerized decision support system,implementation science,technology acceptance,barriers,facilitators,determinants,decision support system

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