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Variation in approaches to antimicrobial use surveillance in high-income secondary care settings: a systematic review
INTRODUCTION: In secondary care, antimicrobial use (AMU) must be monitored to reduce the risk of antimicrobial resistance and infection-related complications. However, there is variation in how hospitals address this challenge, partly driven by each site’s level of digital maturity, expertise and re...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8283733/ https://www.ncbi.nlm.nih.gov/pubmed/33893502 http://dx.doi.org/10.1093/jac/dkab125 |
Sumario: | INTRODUCTION: In secondary care, antimicrobial use (AMU) must be monitored to reduce the risk of antimicrobial resistance and infection-related complications. However, there is variation in how hospitals address this challenge, partly driven by each site’s level of digital maturity, expertise and resources available. This systematic review investigated approaches to measuring AMU to explore how these structural differences may present barriers to engagement with AMU surveillance. METHODS: We searched four digital databases and the websites of relevant organizations for studies in high-income, inpatient hospital settings that estimated AMU in adults. Excluded studies focused exclusively on antiviral or antifungal therapies. Data were extracted data on 12 fields (study description, data sources, data extraction methods and professionals involved in surveillance). Proportions were estimated with 95% CIs. RESULTS: We identified 145 reports of antimicrobial surveillance from Europe (63), North America (53), Oceania (14), Asia (13) and across more than continent (2) between 1977 and 2018. Of 145 studies, 47 carried out surveillance based on digital data sources. In regions with access to electronic patient records, 26/47 studies employed manual methods to extract the data. The majority of identified professionals involved in these studies were clinically trained (87/93). CONCLUSIONS: Even in regions with access to electronic datasets, hospitals rely on manual data extraction for this work. Data extraction is undertaken by healthcare professionals, who may have conflicting priorities. Reducing barriers to engagement in AMU surveillance requires investment in methods, resources and training so that hospitals can extract and analyse data already contained within electronic patient records. |
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