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Implementation of prediction models in the emergency department from an implementation science perspective—Determinants, outcomes and real-world impact: A scoping review protocol

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  • Sze Ling Chan
  • Jin Wee Lee
  • Marcus Eng Hock Ong
  • Fahad Javaid Siddiqui
  • Nicholas Graves
  • Andrew Fu Wah Ho
  • Nan Liu

Abstract

The number of prediction models developed for use in emergency departments (EDs) have been increasing in recent years to complement traditional triage systems. However, most of these models have only reached the development or validation phase, and few have been implemented in clinical practice. There is a gap in knowledge on the real-world performance of prediction models in the ED and how they can be implemented successfully into routine practice. Existing reviews of prediction models in the ED have also mainly focused on model development and validation. The aim of this scoping review is to summarize the current landscape and understanding of implementation of predictions models in the ED. This scoping review follows the Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. We will include studies that report implementation outcomes and/or contextual determinants according to the RE-AIM/PRISM framework for prediction models used in EDs. We will include outcomes or contextual determinants studied at any point of time in the implementation process except for effectiveness, where only post-implementation results will be included. Conference abstracts, theses and dissertations, letters to editors, commentaries, non-research documents and non-English full-text articles will be excluded. Four databases (MEDLINE (through PubMed), Embase, Scopus and CINAHL) will be searched from their inception using a combination of search terms related to the population, intervention and outcomes. Two reviewers will independently screen articles for inclusion and any discrepancy resolved with a third reviewer. Results from included studies will be summarized narratively according to the RE-AIM/PRISM outcomes and domains. Where appropriate, a simple descriptive summary of quantitative outcomes may be performed.

Suggested Citation

  • Sze Ling Chan & Jin Wee Lee & Marcus Eng Hock Ong & Fahad Javaid Siddiqui & Nicholas Graves & Andrew Fu Wah Ho & Nan Liu, 2022. "Implementation of prediction models in the emergency department from an implementation science perspective—Determinants, outcomes and real-world impact: A scoping review protocol," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-10, May.
  • Handle: RePEc:plo:pone00:0267965
    DOI: 10.1371/journal.pone.0267965
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    References listed on IDEAS

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    1. Chiara Longoni & Andrea Bonezzi & Carey K Morewedge, 2019. "Resistance to Medical Artificial Intelligence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(4), pages 629-650.
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