Providing a Roadmap for Automated Infection Surveillance in Europe


Research Network: 2019-01-01 - 2020-10-31
Total sum awarded: €50 000

Surveillance of healthcare-associated infections (HAI), including surgical site infections (SSI) and central line associated bloodstream infections (CLABSI), is a key component of national surveillance programs. Identifying infections – as opposed to colonisation – allows for the quantification of the burden of infections by antimicrobial-resistant (AMR) pathogens and evaluation of the effectiveness of interventions. Traditional surveillance by manual chart review is time-consuming and prone to error, making large-scale standardised surveillance unachievable in many European countries. In recent years automated HAI surveillance systems using data routinely stored in hospital electronic health records have been developed for among others SSI and CLABSI. Advantages of (semi-)automated surveillance include higher quality of surveillance through better standardisation and a 75-95% reduction of manual chart review workload. Automated surveillance is promising, but most of the currently available systems were developed in individual hospitals, and are hence heterogeneous in design, aims, methods and definitions used. In addition, within each centre, many similar challenges and barriers are encountered, but knowledge on how to address them is not widely disseminated, thus making inefficient use of resources and repeatedly requiring considerable investments. Within the PRAISE network, we aim to design a shared roadmap to move automated surveillance from the research setting to large-scale implementation. PRAISE will deliver: 1.A roadmap to automated HAI surveillance, describing requirements of automated surveillance systems and one or more possible trajectories towards their design. 2.A research agenda to support future development efforts. 3.Guidance documents regarding regulatory and governance barriers, IT and data management solutions and training needs. PRAISE will organise two workshops and divide tasks among subgroups. The PRAISE network uniquely brings together experts working in the field of surveillance, with representatives from hospitals as well as public health institutes. The output of the network will improve AMR surveillance by providing the guidance necessary to develop high-quality automated surveillance tools for HAI, caused by AMR and susceptible pathogens.

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  • Maaike Van Mourik, University Medical Centre Utrecht, Netherlands (Coordinator)
  • Zaira Palacios, Universitario Virgen Macarena, Spain (Observer)
  • Petra Gastmeier, Charité University Medicine, Germany (Observer)
  • Michael Behnke, Charité University Medicine, Germany (Observer)
  • Evelina Tacconelli, University Hospital Tübingen, Germany (Observer)
  • Brian Kristensen, Statens Serum Institute, Denmark (Observer)
  • Sophie Gubbels, Statens Serum Institute, Denmark (Observer)
  • Hilary Humphreys, Royal College of Surgeons in Ireland, Ireland (Observer)
  • Thomas Tängdén, Uppsala University, Sweden (Observer)
  • Olov Aspervall, Public Health Agency of Sweden, Sweden (Observer)
  • Elisabeth Presterl, Medical University of Vienna, Austria (Observer)
  • Marc Bonten, National Institute for Public Health and the Environment (RIVM), Netherlands (Observer)
  • Jacqui Reilly, Glasgow Caledonian University, United Kingdom (Observer)
  • Stephanie van Rooden, University Medical Centre Utrecht, Netherlands (Observer)
  • Mohammed Abbas, University Hospitals Geneva, Switzerland (Observer)
  • Aina Gomila Grange, Bellevitge University Hospital, Spain (Observer)
  • Anders Johansson, Umeå University, Sweden (Observer)
  • Daniel Teixeira, University Hospitals Geneva, Switzerland (Observer)
  • Siddharth Mookerjee, Imperial College London, United Kingdom (Observer)
  • John Karlsson Valik, Karolinska University Hospital, Sweden (Observer)
  • Sabine de Greeff, National Institute for Public Health and the Environment (RIVM), Netherlands (Observer)
  • Pontus Naucler, Karolinska University Hospital, Sweden (Observer)
  • Christopher Roberts, Public Health Wales, United Kingdom (Observer)
  • Wendy Harrison, Public Health Wales, United Kingdom (Observer)
  • Mayke Koek, National Institute for Public Health and the Environment (RIVM), Netherlands (Observer)
  • Stephan Harbarth, University Hospitals Geneva, Switzerland (Observer)
  • Jean-Christophe Lucet, Hôpitaux de Paris & Sorbonne University, France (Observer)
  • Pascal Astagneau, Hôpitaux de Paris & Sorbonne University, France (Observer)
  • Alain Lepape, Centre Hospitalier Universitaire Lyon Sud, France (Observer)
  • Miquel Pujol, Bellevitge University Hospital, Spain (Observer)

Healthcare-associated infections (HAI) are infections that arise during the process of medical care, for example surgical site infections or central line-associated bloodstream infections. Such infections can be caused by both antimicrobial resistant (AMR) and non-AMR pathogens. Surveillance of HAI, systematically monitoring their incidence, is a cornerstone of infection prevention programmes and allows for the assessment of the effect of interventions. Conventional surveillance is performed by manually reviewing patient charts; this process is cumbersome and prone to error. These limitations, along with the increasing adoption of electronic health records, has driven the development of automated HAI surveillance. However, these initiatives have often been limited to individual hospitals and are mainly used in the research setting. This stand-alone development of systems makes inefficient use of resources and brings the risk of losing comparability across surveillance networks. Implementing large-scale automated surveillance requires a coordinated effort and redesign of surveillance methods. The PRAISE network brings together 30 experts with HAI surveillance or IT expertise from 10 Western European countries and aims to provide guidance for large-scale implementation of reliable automated HAI surveillance. The main product of the network is a roadmap for large-scale implementation of automated surveillance. It does not provide a point-by-point checklist, but aims provide high-level guidance and outline the underlying principles. The roadmap discusses the selection of surveillance targets (what infections to detect), different organisational and methodological approaches to large-scale automation of surveillance and their advantages, disadvantages and risks. It defines key performance requirements of automated surveillance systems and suggestions for their design and provides guidance on how to achieve successful implementation, including the involvement of all relevant stakeholders. The roadmap also addresses areas of future research and lists training requirements for the infection control community and related disciplines. The roadmap is supported by two accompanying papers. In the Governance Guidance document, the governance and data protection aspects are explored in more depth. The document aims to support collaboration between surveillance experts and legal and/or governance specialists. The IT Guidance document, serves to explain the basics of medical informatics and to outline the steps required to technically implement automated surveillance within healthcare facilities. It provides more in-depth information on among others data sources, interoperability, data storage, and communication. In all, the roadmap and guidance documents can be used by surveillance networks together with individual healthcare facilities to develop a strategy for implementing automated surveillance in line with the possibilities within their local setting.