How does a national blood operator detect early infectious disease signals affecting blood and cellular therapy product safety and sufficiency?

Authors

Leisa Bruneau, Carmen L Charlton, Sandra Ramirez-Arcos, Dilini Kumaran, David Allan, Olivia Norman, Matthew Seftel, Evan M Bloch, Chantale Pambrun, Sheila F O'Brien, Cynthia Cranney, Steven J Drews

Published in

Transfusion. Sep 19, 2026. Epub Sep 19, 2026.

Abstract

Public access to up-to-date information on infectious diseases and outbreaks has grown rapidly with the advent of artificial intelligence platforms. While this information can improve patient care and support research, the sheer volume of available data coupled with the challenge of identifying misinformation and disinformation can be overwhelming. A key challenge is managing large information volumes and identifying early, subtle signals that may indicate risks or opportunities to prevent infection. The aim of this manuscript is to explain how a national blood operator identifies and evaluates subtle infectious disease signals generated by horizon scanning to proactively protect the blood supply, including biotherapy products.
Horizon scanning protocols were developed to detect early signals of change (emerging/re-emerging pathogens, geopolitical/environmental changes, scientific and technological advances, new therapies/treatments for infectious diseases, and blood operator transfusion medicine changes). Communication strategies were created to alert national and international transfusion medicine decision makers of blood safety-related events.
Several key components of a horizon scanning approach are presented to illustrate how novel information can be identified, processed, and formulated into knowledge translation products. Efficient and cost-effective approaches are presented to enable horizon scanning activities with limited resources and time commitments.
Horizon scanning is an efficient and necessary activity to safeguard the blood supply. Here we present a collaborative group-based approach, which reduces operator bias and improves overall examination of multiple data sources. This approach does not require expensive technology, can focus on open-source information, and engages new scientists in learning about infectious diseases.

PMID:
42762172
Bibliographic data and abstract were imported from PubMed on 20 Sep 2026.

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