Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Mar 2, 2020
Date Accepted: Jun 3, 2020
BEAR - A conceptual framework to study the implementation of clinical decision support systems: Literature Review and Concept Mapping
ABSTRACT
Background:
The implementation of clinical decision support systems (CDSS) as an intervention to foster clinical practice change is affected by many factors. Key factors include those associated with behavioral change and also factors associated with the technology acceptance. However, the literature about these subjects is fragmented and originating from two traditionally separated disciplines: Implementation Science and Technology Acceptance.
Objective:
In this article, we propose an integrated framework (BEAR – BEhavior and Acceptance fRamework) that bridges the gap between behavioral change and technology acceptance aspects of the implementation of CDSS.
Methods:
We employed an iterative process to map constructs from four contributing frameworks (TDF - Theoretical Domains Framework, CFIR - Consolidated Framework For Implementation Research, HOT-fit - Human, Organization and Technology-fit, and UTAUT - Unified Theory of Acceptance and Use of Technology) and the findings of six 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, Competence; Social influences; and System quality. We demonstrate the use of the framework providing examples from two research projects.
Conclusions:
We propose an integrated framework that bridges the gap between behavioral change and technology acceptance widening the view established by current models.
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