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Clin Microbiol Infect. 2017 Aug;23(8):524-532. doi: 10.1016/j.cmi.2017.02.028. Epub 2017 Mar 6.

A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?

Author information

1
National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College, London, UK. Electronic address: timothy.rawson07@ic.ac.uk.
2
National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College, London, UK.
3
Department of Electrical and Electronic Engineering, Imperial College, London, UK.
4
School of Public Health, Imperial College, London, UK.
5
Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK.

Abstract

OBJECTIVES:

Clinical decision support systems (CDSS) for antimicrobial management can support clinicians to optimize antimicrobial therapy. We reviewed all original literature (qualitative and quantitative) to understand the current scope of CDSS for antimicrobial management and analyse existing methods used to evaluate and report such systems.

METHOD:

PRISMA guidelines were followed. Medline, EMBASE, HMIC Health and Management and Global Health databases were searched from 1 January 1980 to 31 October 2015. All primary research studies describing CDSS for antimicrobial management in adults in primary or secondary care were included. For qualitative studies, thematic synthesis was performed. Quality was assessed using Integrated quality Criteria for the Review Of Multiple Study designs (ICROMS) criteria. CDSS reporting was assessed against a reporting framework for behaviour change intervention implementation.

RESULTS:

Fifty-eight original articles were included describing 38 independent CDSS. The majority of systems target antimicrobial prescribing (29/38;76%), are platforms integrated with electronic medical records (28/38;74%), and have a rules-based infrastructure providing decision support (29/38;76%). On evaluation against the intervention reporting framework, CDSS studies fail to report consideration of the non-expert, end-user workflow. They have narrow focus, such as antimicrobial selection, and use proxy outcome measures. Engagement with CDSS by clinicians was poor.

CONCLUSION:

Greater consideration of the factors that drive non-expert decision making must be considered when designing CDSS interventions. Future work must aim to expand CDSS beyond simply selecting appropriate antimicrobials with clear and systematic reporting frameworks for CDSS interventions developed to address current gaps identified in the reporting of evidence.

KEYWORDS:

Antimicrobial resistance; Antimicrobial stewardship; Decision algorithms; Electronic support

PMID:
28268133
DOI:
10.1016/j.cmi.2017.02.028
[Indexed for MEDLINE]
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