Federated systems for automated infection surveillance: a perspective.
Automated surveillance
Ethical Legal Societal Implications
Federated systems
Governance
Healthcare associated infections
Implementation
Interoperability
Severe acute respiratory infections
Standards
Journal
Antimicrobial resistance and infection control
ISSN: 2047-2994
Titre abrégé: Antimicrob Resist Infect Control
Pays: England
ID NLM: 101585411
Informations de publication
Date de publication:
27 Sep 2024
27 Sep 2024
Historique:
received:
29
05
2024
accepted:
08
09
2024
medline:
28
9
2024
pubmed:
28
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
Automation of surveillance of infectious diseases-where algorithms are applied to routine care data to replace manual decisions-likely reduces workload and improves quality of surveillance. However, various barriers limit large-scale implementation of automated surveillance (AS). Current implementation strategies for AS in surveillance networks include central implementation (i.e. collecting all data centrally, and central algorithm application for case ascertainment) or local implementation (i.e. local algorithm application and sharing surveillance results with the network coordinating center). In this perspective, we explore whether current challenges can be solved by federated AS. In federated AS, scripts for analyses are developed centrally and applied locally. We focus on the potential of federated AS in the context of healthcare associated infections (AS-HAI) and of severe acute respiratory illness (AS-SARI). AS-HAI and AS-SARI have common and specific requirements, but both would benefit from decreased local surveillance burden, alignment of AS and increased central and local oversight, and improved access to data while preserving privacy. Federated AS combines some benefits of a centrally implemented system, such as standardization and alignment of an easily scalable methodology, with some of the benefits of a locally implemented system including (near) real-time access to data and flexibility in algorithms, meeting different information needs and improving sustainability, and allowance of a broader range of clinically relevant case-definitions. From a global perspective, it can promote the development of automated surveillance where it is not currently possible and foster international collaboration.The necessary transformation of source data likely will place a significant burden on healthcare facilities. However, this may be outweighed by the potential benefits: improved comparability of surveillance results, flexibility and reuse of data for multiple purposes. Governance and stakeholder agreement to address accuracy, accountability, transparency, digital literacy, and data protection, warrants clear attention to create acceptance of the methodology. In conclusion, federated automated surveillance seems a potential solution for current barriers of large-scale implementation of AS-HAI and AS-SARI. Prerequisites for successful implementation include validation of results and evaluation requirements of network participants to govern understanding and acceptance of the methodology.
Identifiants
pubmed: 39334278
doi: 10.1186/s13756-024-01464-8
pii: 10.1186/s13756-024-01464-8
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
113Informations de copyright
© 2024. The Author(s).
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