Implementation of Prediction Models in the Emergency Department from an Implementation Science Perspective-Determinants, Outcomes, and Real-World Impact: A Scoping Review

被引:15
作者
Chan, Sze Ling [1 ,2 ]
Lee, Jin Wee [3 ]
Ong, Marcus Eng Hock [1 ,4 ]
Siddiqui, Fahad Javaid [5 ]
Graves, Nicholas [2 ]
Ho, Andrew Fu Wah [4 ,5 ]
Liu, Nan [1 ,2 ,3 ,6 ,7 ]
机构
[1] Singapore Hlth Serv, Hlth Serv Res Ctr Chan, Singapore, Singapore
[2] Duke NUS Med Sch Chan, Program Hlth Serv & Syst Res, Singapore, Singapore
[3] Duke NUS Med Sch, Ctr Quantitat Med Lee, Singapore, Singapore
[4] Singapore Gen Hosp, Dept Emergency Med, Singapore, Singapore
[5] Duke NUS Med Sch, Prehosp Emergency Res Ctr, Singapore, Singapore
[6] SingHealth AI Off, Singapore HealthServ, Singapore, Singapore
[7] Natl Univ Singapore, Inst Data Sci, Singapore, Singapore
关键词
EARLY WARNING SCORE; COVID-19; PATIENTS; TRIAGE; TOOL; ACCEPTABILITY; VALIDATION; PHYSICIANS; DECISION; SYSTEM; RISK;
D O I
10.1016/j.annemergmed.2023.02.001
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Study objective: Prediction 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. Methods: We 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. Results: Thirty-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. Conclusion: Examining ED prediction models from an implementation science perspective can provide valuable insights and help guide future implementations.
引用
收藏
页码:22 / 36
页数:15
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