AI Tool Identifies Trauma Patients Requiring Blood Transfusions Before Reaching Hospital

However, current tools often fail to accurately identify which patients urgently require blood transfusions, delaying life-saving treatment. Now, a new multinational study suggests that artificial intelligence (AI) could improve early decision-making by predicting transfusion needs before patients reach the hospital.

In the study led by an international team, with contributions from University College Dublin (Dublin, Ireland), investigators developed machine-learning models using trauma registry data to estimate transfusion requirements based solely on pre-hospital information. The models analyzed vital signs, injury patterns, and medication history available to emergency medical teams. Researchers trained the system on data from 364,350 patients in the United States and validated it in an additional 54,210 patients across Germany, Austria, Switzerland, Ireland, and Canada.

The AI models demonstrated high predictive accuracy for identifying patients who required any transfusion, as well as those needing packed red blood cells. Importantly, the system outperformed traditional risk classification methods applied after arrival at emergency departments. The findings, published in Lancet Digital Health, show that the models more accurately identified patients who later required transfusion, emergency surgery for bleeding control, or who died from hemorrhage, using only data available before hospital admission.

By enabling earlier identification of patients at risk of hemorrhagic shock, the approach could allow trauma teams to prepare blood products and interventions sooner, potentially improving outcomes when time is critical. Researchers emphasize that the study represents development and validation rather than immediate clinical implementation. Prospective trials will be needed to assess real-time performance, clinician interaction, and overall impact on patient survival.

“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,” said UCD Professor Patricia Maguire, co-author of the study. “This has clear potential to support more timely transfusion decisions, although prospective evaluation will be needed before clinical implementation.”

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