Real-world implementation of clinical decision support systems in emergency departments: a scoping review protocol
Hwayeon Danielle Shina b c✉, Kylie Payned, Helen W. Haskelle, Carrie G. Wadef, Tristan Glatardc, David Westfall Batesa b g, Patricia C. Dykesa b h, Ania Syrowatkaa b
- aBrigham and Women’s Hospital, Boston, MA, USA
- bHarvard Medical School, Harvard University, Boston, MA, USA
- cKrembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Abstract
OBJECTIVE: This scoping review will address one area of artificial intelligence (AI) applications: clinical decision support systems (CDSSs) implemented in emergency departments. Using implementation science frameworks, including the Consolidated Framework for Implementation Research, this review will characterize the reported implementation practices, determinants, equity considerations, outcomes, and evaluation measures. INTRODUCTION: AI has gained attention in health care for its potential to improve efficiency, precision care, and clinical decision-making through applications including CDSSs, but there is no clear guidance informed by implementation science theories to support its implementation. Furthermore, emergency departments have unique implementation requirements due to their distinctive and unpredictable patient presentations and clinical workflows, and the implementation characteristics of CDSSs in this area are largely unknown. This review will summarize and map the literature on this topic to inform future empirical inquiries. ELIGIBILITY CRITERIA: This review will include academic literature describing the implementation of CDSSs in emergency departments. All CDSSs, including both rule-based and next-generation systems, will be eligible for inclusion, but their distinctions will be noted and reported. METHODS: This review will follow the JBI scoping review methodology. PubMed, CINAHL, Embase, PsycINFO, and Web of Science will be comprehensively searched, limited to publications from 2015 to 2025 to reflect the rapid evolution of CDSSs and their relevance to health care. Two reviewers will independently assess search results against the eligibility criteria and extract data relevant to the review objective. This review will apply implementation science frameworks to map implementation practice characteristics reported in the literature for clear, consistent, and structured findings. REVIEW REGISTRATION: OSF osf.io/8gsmb/.