TY - JOUR
T1 - Automated surveillance systems for healthcare-associated infections
T2 - results from a European survey and experiences from real-life utilization
AU - Verberk, J. D.M.
AU - Aghdassi, S. J.S.
AU - Abbas, M.
AU - Nauclér, P.
AU - Gubbels, S.
AU - Maldonado, N.
AU - Palacios-Baena, Z. R.
AU - Johansson, A. F.
AU - Gastmeier, P.
AU - Behnke, M.
AU - van Rooden, S. M.
AU - van Mourik, M. S.M.
N1 - Funding Information:
The PRAISE network has been supported under 7th transnational call within the Joint Programming Initiative on Antimicrobial Resistance ( JPIAMR ), Network Call on Surveillance (2018) and was thereby funded by ZonMw (grant number 549007001 ).
Funding Information:
Dr Aghdassi is participant in the Charité Digital Clinician Scientist Program funded by the DFG, the Charité Universitätsmedizin – Berlin, and the Berlin Institute of Health at Charité (BIH).
Funding Information:
The national innovation project ‘HAI-Proactive’, supported by the Swedish Innovation Agency (VINNOVA), aims to develop fully automated surveillance tools for HAIs. The project, headed by Karolinska University Hospital (KUH) and Region Stockholm, is organized in three phases: (i) collaboration building between healthcare providers, academic institutions, and industry (2015); (ii) prototype development (2016–2018); and (iii) implementation (2018–2021).
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/4
Y1 - 2022/4
N2 - Background: As most automated surveillance (AS) methods to detect healthcare-associated infections (HAIs) have been developed and implemented in research settings, information about the feasibility of large-scale implementation is scarce. Aim: To describe key aspects of the design of AS systems and implementation in European institutions and hospitals. Methods: An online survey was distributed via e-mail in February/March 2019 among (i) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; (ii) corresponding authors of peer-reviewed European publications on existing AS systems; and (iii) the mailing list of national infection prevention and control focal points of the European Centre for Disease Prevention and Control. Three AS systems from the survey were selected, based on quintessential features, for in-depth review focusing on implementation in practice. Findings: Through the survey and the review of three selected AS systems, notable differences regarding the methods, algorithms, data sources, and targeted HAIs were identified. The majority of AS systems used a classification algorithm for semi-automated surveillance and targeted HAIs were mostly surgical site infections, urinary tract infections, sepsis, or other bloodstream infections. AS systems yielded a reduction of workload for hospital staff. Principal barriers of implementation were strict data security regulations as well as creating and maintaining an information technology infrastructure. Conclusion: AS in Europe is characterized by heterogeneity in methods and surveillance targets. To allow for comparisons and encourage homogenization, future publications on AS systems should provide detailed information on source data, methods, and the state of implementation.
AB - Background: As most automated surveillance (AS) methods to detect healthcare-associated infections (HAIs) have been developed and implemented in research settings, information about the feasibility of large-scale implementation is scarce. Aim: To describe key aspects of the design of AS systems and implementation in European institutions and hospitals. Methods: An online survey was distributed via e-mail in February/March 2019 among (i) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; (ii) corresponding authors of peer-reviewed European publications on existing AS systems; and (iii) the mailing list of national infection prevention and control focal points of the European Centre for Disease Prevention and Control. Three AS systems from the survey were selected, based on quintessential features, for in-depth review focusing on implementation in practice. Findings: Through the survey and the review of three selected AS systems, notable differences regarding the methods, algorithms, data sources, and targeted HAIs were identified. The majority of AS systems used a classification algorithm for semi-automated surveillance and targeted HAIs were mostly surgical site infections, urinary tract infections, sepsis, or other bloodstream infections. AS systems yielded a reduction of workload for hospital staff. Principal barriers of implementation were strict data security regulations as well as creating and maintaining an information technology infrastructure. Conclusion: AS in Europe is characterized by heterogeneity in methods and surveillance targets. To allow for comparisons and encourage homogenization, future publications on AS systems should provide detailed information on source data, methods, and the state of implementation.
KW - Automation
KW - Healthcare-associated infection
KW - Surveillance
KW - Hospitals
KW - Delivery of Health Care
KW - Urinary Tract Infections/epidemiology
KW - Humans
KW - Cross Infection/epidemiology
KW - Infection Control/methods
UR - http://www.scopus.com/inward/record.url?scp=85125399585&partnerID=8YFLogxK
U2 - 10.1016/j.jhin.2021.12.021
DO - 10.1016/j.jhin.2021.12.021
M3 - Article
C2 - 35031393
SN - 0195-6701
VL - 122
SP - 35
EP - 43
JO - The journal of Hospital Infection
JF - The journal of Hospital Infection
ER -