Bos K, van der Laan MJ, Dongelmans DA. Prioritising recommendations following analyses of adverse events in healthcare: a systematic review.
BMJ Open Qual 2020;
9:bmjoq-2019-000843. [PMID:
33037042 PMCID:
PMC7549482 DOI:
10.1136/bmjoq-2019-000843]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 07/01/2020] [Accepted: 09/18/2020] [Indexed: 11/16/2022] Open
Abstract
Purpose
The purpose of this systematic review was to identify an appropriate method—a user-friendly and validated method—that prioritises recommendations following analyses of adverse events (AEs) based on objective features.
Data sources
The electronic databases PubMed/MEDLINE, Embase (Ovid), Cochrane Library, PsycINFO (Ovid) and ERIC (Ovid) were searched.
Study selection
Studies were considered eligible when reporting on methods to prioritise recommendations.
Data extraction
Two teams of reviewers performed the data extraction which was defined prior to this phase.
Results of data synthesis
Eleven methods were identified that are designed to prioritise recommendations. After completing the data extraction, none of the methods met all the predefined criteria. Nine methods were considered user-friendly. One study validated the developed method. Five methods prioritised recommendations based on objective features, not affected by personal opinion or knowledge and expected to be reproducible by different users.
Conclusion
There are several methods available to prioritise recommendations following analyses of AEs. All these methods can be used to discuss and select recommendations for implementation. None of the methods is a user-friendly and validated method that prioritises recommendations based on objective features. Although there are possibilities to further improve their features, the ‘Typology of safety functions’ by de Dianous and Fiévez, and the ‘Hierarchy of hazard controls’ by McCaughan have the most potential to select high-quality recommendations as they have only a few clearly defined categories in a well-arranged ordinal sequence.
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