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

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.

Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management

“…Liberati et al report that scientific evidence provided by the system can sometimes challenge deep-rooted beliefs concerning professional autonomy and hierarchies of authority in the clinical setting, resulting in scepticism regarding the use of CDSS [ 13 ]. Many studies have focused on technical appropriateness and users’ experience to understand factors related to CDSS adoption [ 21 , 22 , 23 ], but there is limited information on how end-users’ individual characteristics influence perceptions about adopting CDSS for antibiotic management.…”

Artificial intelligence in clinical decision support systems for oncology

“…The storage capacity of an AI-based CDSS is much stronger than that of the human brain. It can quickly collect and sort stored information to draw accurate conclusions faster than humans, such as diagnostic radiology and pathological imaging systems [69][70][71]. However, to adapt to a real native medical environment, the CDSS must be significantly improved.…”

Development of a Theory-Informed Behavior Change Intervention to Reduce Inappropriate Prescribing of Nephrotoxins and Renally Eliminated Drugs

“…Finally, this evaluation was based on individual implementation strategies selected; other considerations may be provided if sociotechnical frameworks were added such as the HOT-fit (Human, Organization and Technology-fit), and UTAUT (Unified Theory of Acceptance and Use of Technology). [54][55][56]…”