Michael Behnke1, John Karlsson Valik2, Sophie Gubbels3, Daniel Teixeira4, Brian Kristensen5, Mohamed Abbas4, Stephanie M van Rooden6, Petra Gastmeier7, Maaike S M van Mourik8. 1. National Reference Center for Surveillance of Nosocomial Infections, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany. Electronic address: michael.behnke@charite.de. 2. Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet and Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden. 3. Data Integration and Analysis Secretariat, Statens Serum Institut, Copenhagen, Denmark. 4. Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland. 5. Department of Infectious Disease Epidemiology and Prevention, Statens Serum Institut, Copenhagen, Denmark. 6. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Centre for Infectious Disease Epidemiology and Surveillance National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands. 7. National Reference Center for Surveillance of Nosocomial Infections, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany. 8. Department of Medical Microbiology and Infection Control, University Medical Center Utrecht, Utrecht, the Netherlands.
Abstract
INTRODUCTION: Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed. METHODS: This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries. RESULTS: The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed. CONCLUSIONS: With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.
INTRODUCTION: Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed. METHODS: This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries. RESULTS: The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed. CONCLUSIONS: With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.
Authors: José A García-Berná; Raimel Sobrino-Duque; Juan M Carrillo de Gea; Joaquín Nicolás; José L Fernández-Alemán Journal: Int J Environ Res Public Health Date: 2022-07-22 Impact factor: 4.614