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<p>Healthcare-associated infections affect around six percent of all hospital stays in Austria. Urinary tract, respiratory and surgical site infections are most prevalent, and their economic burden is estimated in the high hundreds of millions of euros per year. Modern surveillance is evolving from manual spreadsheets towards automated, partly AI-driven systems that link laboratory, movement and patient data to detect transmission chains in (near) real time. Technologies such as machine learning, IoT sensor networks and rapid typing methods broaden the preventive toolbox but do not replace clinical expertise. Data quality, interoperability, transparency and clearly defined escalation pathways are key for effective prevention. Surveillance should be viewed not primarily as an instrument of control, but as a&#xa0;shared responsibility and a&#xa0;core component of learning healthcare organisations.</p>

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Wenn Daten Hygiene lernen

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月14日

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  • Markus Wallner

摘要

Healthcare-associated infections affect around six percent of all hospital stays in Austria. Urinary tract, respiratory and surgical site infections are most prevalent, and their economic burden is estimated in the high hundreds of millions of euros per year. Modern surveillance is evolving from manual spreadsheets towards automated, partly AI-driven systems that link laboratory, movement and patient data to detect transmission chains in (near) real time. Technologies such as machine learning, IoT sensor networks and rapid typing methods broaden the preventive toolbox but do not replace clinical expertise. Data quality, interoperability, transparency and clearly defined escalation pathways are key for effective prevention. Surveillance should be viewed not primarily as an instrument of control, but as a shared responsibility and a core component of learning healthcare organisations.