HMN 2026: How AI tool can predict which trauma patients need blood transfusions before they reach the hospital

Severe bleeding is one of the most common and preventable causes of death after traumatic injury, yet currently available tools have poor ability to determine which patients urgently need blood transfusions. A new multinational study, just published in Lancet Digital Health, suggests artificial intelligence (AI) may help close that gap.

Researchers have developed and validated machine-learning models that can accurately predict whether trauma patients will require blood transfusions, using only information available before they reach the hospital such as vital signs, injury patterns, and medication history.

Co-author Prof Patricia Maguire from University College Dublin (UCD), Director of UCD AI Healthcare Hub and UCD Institute for Discovery, said, “These findings show that AI-driven decision support could enable earlier and more precise identification of patients at highest risk of hemorrhagic shock, using data already available to emergency services. This has clear potential to support more timely transfusion decisions, although prospective evaluation will be needed before clinical implementation.”

The study analyzed trauma registry data from 364,350 patients in the United States and tested the models in 54,210 additional patients from Germany, Austria, Switzerland, Ireland, and Canada. The AI system achieved high predictive accuracy for identifying any transfusion need and the need for packed red blood cells.

Importantly, the models relied exclusively on the pre-hospital data available to emergency medical teams and, compared with traditional risk classification used after arrival at an emergency department, the AI approach more accurately identified patients who went on to require transfusion, emergency surgery for bleeding control, or who died from hemorrhage.

Prof Maguire said, “This work shows how AI can use prehospital data to anticipate transfusion needs before arrival, enabling trauma teams to prepare earlier and respond faster when minutes matter most.”

While the results are promising, the researchers emphasize that the work represents a development and validation phase, not a ready-to-use clinical tool. Further studies are needed to test how such AI tools perform in real-time decision making, how clinicians interact with them, and whether their use improves patient outcomes in prospective trials.

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