ArticleBMC infectious diseases2021
Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital.
Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Hospital-wide pathogen transmission surveillance combining epidemiological, genetic, and spectroscopic approaches.Microbiology spectrum · 2026Article
- Efficacy of cluster care strategies and blood transfusion protocols in reducing nosocomial infections associated with triple catheter use in intensive care units.BMC infectious diseases · 2026Article
- Toward a human-centered digital ecosystem: bridging the "AI Chasm" in intelligent nosocomial infection monitoring.Frontiers in public health · 2026Article
- An openEHR based infection control system to support monitoring of nosocomial bacterial clusters and contacts.NPJ digital medicine · 2025Article
- Compliance with transmission-based precautions, and associated factors among healthcare providers in Cameroon: a cross-sectional study.Antimicrobial resistance and infection control · 2025Article
- Article
- Association between infection prevention and control safety culture and healthcare workers' compliance with infection control measures: a cross-sectional study.Frontiers in public health · 2025Article
- A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals.Antimicrobial resistance and infection control · 2024Article
- Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings.Antimicrobial resistance and infection control · 2024Article
- Risk factors for transmission of carbapenem-resistant Acinetobacter baumannii in outbreak situations: results of a case-control study.BMC infectious diseases · 2024Article
- Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- From wild-type to Omicron: changes in SARS-CoV-2 hospital cluster dynamics. Observations from a German tertiary care hospital.GMS hygiene and infection control · 2024Article
- Importance of Hospital Infection Control.Cureus · 2023Review
- Factors Influencing Clinicians' Use of Hospital Information Systems for Infection Prevention and Control: Cross-Sectional Study Based on the Extended DeLone and McLean Model.Journal of medical Internet research · 2023Article
- Risk factors for nosocomial SARS-CoV-2 infections in patients: results from a retrospective matched case-control study in a tertiary care university center.Antimicrobial resistance and infection control · 2022Article
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9 authors.
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Abstract
backgroundEarly detection of clusters of pathogens is crucial for infection prevention and control (IPC) in hospitals. Conventional manual cluster detection is usually restricted to certain areas of the hospital and multidrug resistant organisms. Automation can increase the comprehensiveness of cluster surveillance without depleting human resources. We aimed to describe the application of an automated cluster alert system (CLAR) in the routine IPC work in a hospital. Additionally, we aimed to provide information on the clusters detected and their properties.
methodsCLAR was continuously utilized during the year 2019 at Charité university hospital. CLAR analyzed microbiological and patient-related data to calculate a pathogen-baseline for every ward. Daily, this baseline was compared to data of the previous 14 days. If the baseline was exceeded, a cluster alert was generated and sent to the IPC team. From July 2019 onwards, alerts were systematically categorized as relevant or non-relevant at the discretion of the IPC physician in charge.
resultsIn one year, CLAR detected 1,714 clusters. The median number of isolates per cluster was two. The most common cluster pathogens were Enterococcus faecium (n = 326, 19 %), Escherichia coli (n = 274, 16 %) and Enterococcus faecalis (n = 250, 15 %). The majority of clusters (n = 1,360, 79 %) comprised of susceptible organisms. For 906 alerts relevance assessment was performed, with 317 (35 %) alerts being classified as relevant.
conclusionsCLAR demonstrated the capability of detecting small clusters and clusters of susceptible organisms. Future improvements must aim to reduce the number of non-relevant alerts without impeding detection of relevant clusters. Digital solutions to IPC represent a considerable potential for improved patient care. Systems such as CLAR could be adapted to other hospitals and healthcare settings, and thereby serve as a means to fulfill these potentials.
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