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<p>Antimicrobial resistance (AMR) is a major public health issue that, combined with healthcare-associated infections (HAIs) threaten the quality and safety of hospital care. Monitoring AMR and HAIs is one of the cornerstones of preventing these phenomena with the use of indicators. Various monitoring networks and indicators exist for this type of surveillance, yet the landscape is cluttered with a confusing array of them, making it unclear why so many are used or how they were chosen. We provide a comprehensive overview of the diversity indicators employed in monitoring AMR and HAI from local to international networks. One challenge is the variation in case definitions between networks, which complicates direct comparisons. Standardized infection rates help adjust for confounding factors such as demographics (age, sex) and other infection-related risks, but obtaining such detailed data remains complex. Benchmarking hospital indicators involves comparing performance metrics with those of peer institutions, offering valuable insights to improve care quality, patient safety, and overall healthcare efficiency. However, to drive meaningful improvements, comprehensive feedback must be shared to guide targeted corrective actions.</p><p>The emergence of health data warehouses (HDWs) and artificial intelligence (AI) provides new opportunities to refine and develop indicators, better addressing the challenges of contemporary healthcare monitoring.</p>

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Mapping antimicrobial resistance and healthcare-associated infections indicators for actionable benchmarking: a cross-network overview in a data-driven era

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月7日

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  • Rishma Amarsy,
  • Caroline Thomas,
  • Sandra Fournier,
  • Vincent Jarlier,
  • Jérôme Robert

摘要

Antimicrobial resistance (AMR) is a major public health issue that, combined with healthcare-associated infections (HAIs) threaten the quality and safety of hospital care. Monitoring AMR and HAIs is one of the cornerstones of preventing these phenomena with the use of indicators. Various monitoring networks and indicators exist for this type of surveillance, yet the landscape is cluttered with a confusing array of them, making it unclear why so many are used or how they were chosen. We provide a comprehensive overview of the diversity indicators employed in monitoring AMR and HAI from local to international networks. One challenge is the variation in case definitions between networks, which complicates direct comparisons. Standardized infection rates help adjust for confounding factors such as demographics (age, sex) and other infection-related risks, but obtaining such detailed data remains complex. Benchmarking hospital indicators involves comparing performance metrics with those of peer institutions, offering valuable insights to improve care quality, patient safety, and overall healthcare efficiency. However, to drive meaningful improvements, comprehensive feedback must be shared to guide targeted corrective actions.

The emergence of health data warehouses (HDWs) and artificial intelligence (AI) provides new opportunities to refine and develop indicators, better addressing the challenges of contemporary healthcare monitoring.