• Sze Ling Chan, LeeJin WeeJWCenter for Quantitative Medicine, Duke-NUS Medical School, Singapore., OngMarcus Eng HockMEHHealth Services Research Center, Singapore Health Services, Singapore; Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore., Fahad Javaid Siddiqui, Nicholas Graves, HoAndrew Fu WahAFWDepartment of Emergency Medicine, Singapore General Hospital, Singapore; Prehospital Emergency Research Center, Duke-NUS Medical School, Singapore., and Nan Liu.
• Health Services Research Center, Singapore Health Services, Singapore; Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore.
• Ann Emerg Med. 2023 Jul 1; 82 (1): 223622-36.

Study ObjectivePrediction models offer a promising form of clinical decision support in the complex and fast-paced environment of the emergency department (ED). Despite significant advancements in model development and validation, implementation of such models in routine clinical practice remains elusive. This scoping review aims to survey the current state of prediction model implementation in the ED and to provide insights on contributing factors and outcomes from an implementation science perspective.MethodsWe searched 4 databases from their inception to May 20, 2022: MEDLINE (through PubMed), Embase, Scopus, and CINAHL. Articles that reported implementation outcomes and/or contextual determinants under the Reach, Effectiveness, Adoption, Implementation Maintenance (RE-AIM)/Practical, Robust, Implementation, and Sustainability Model (PRISM) framework were included. Characteristics of studies, models, and results of the RE-AIM/PRISM domains were summarized narratively.ResultsThirty-six reports on 31 implementations were included. The most common prediction models implemented were early warning scores. The most common implementation strategies used were training stakeholders, infrastructural changes, and using evaluative or iterative strategies. Only one report examined ED patients' perspectives, whereas the rest were focused on the experience of health care workers or organizational stakeholders. Key determinants of successful implementation include strong stakeholder engagement, codevelopment of workflows and implementation strategies, education, and usability.ConclusionExamining ED prediction models from an implementation science perspective can provide valuable insights and help guide future implementations.Copyright © 2023 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

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