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Review
. 2025 May 9;3(1):37.
doi: 10.1038/s44259-025-00105-3.

Tools and challenges in the use of routine clinical data for antimicrobial resistance surveillance

Kathryn E Holt  1   2 , Megan E Carey  3   4 , Clare Chandler  3 , James H Cross  3 , Zoe A Dyson  3   5 , Nicholas Furnham  3 , Rebecca E Glover  3 , Mollie Virgo  3 , Gwenan M Knight  3
Affiliations
Review

Tools and challenges in the use of routine clinical data for antimicrobial resistance surveillance

Kathryn E Holt et al. NPJ Antimicrob Resist. .

Abstract

Routine clinical microbiology data are widely used for antimicrobial resistance (AMR) surveillance, but data availability and quality vary. In this Perspective, we explore the technical challenges of utilising routine data to inform action at various levels, and summarise emerging open-source technical solutions for hospital-level data collection, aggregation, and sharing. We highlight a need for agreed-upon data standards, and tools that support both facility-level and public health surveillance.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. The AMR data journey, from facility-level data collection and utilisation, to informing global policy.
Abbreviations: Antimicrobial Resistance (AMR), Antimicrobial stewardship (AMS), Antimicrobial Consumption (AMC), Antimicrobial Usage (AMU), AWaRe (Access, Watch, Reserve), Emerging AMR Reporting (EAR), Essential Medicines List (EML), Global Antimicrobial Surveillance System (GLASS), Healthcare-Associated Infections (HCAI), Infection Prevention and Control (IPC), Laboratory Information Management System (LIMS), National Action Plan (NAP), Reporting and Monitoring System (RMS), SDG (Sustainable Development Goal), Tuberculosis (TB), World Health Organization (WHO), Weighted-incidence syndromic combination antibiogram (WISCA).
Fig. 2
Fig. 2
Routine AMR data represent a subset of total infections.
See this image and copyright information in PMC

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