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Background: Delirium is a complex neuropsychiatric syndrome whose severe and lasting consequences in hospitalized patients are largely considered preventable. Prevention primarily relies on the early identification of high-risk patients prior to syndrome onset. However, this remains challenging in clinical practice due to the absence of a diagnostic gold standard and the considerable administrative burden associated with established screening tools such as the 4AT score. Objective: The aim of this study is to compare the 4AT score with an established AI-supported prognostic tool in applied clinical practice. Our goal is to demonstrate that shifting from a purely diagnostic approach toward AI-supported delirium risk prediction enables more efficient clinical workflows, reduces misclassification rates, facilitates earlier and more targeted interventions, and ultimately improves long-term patient outcomes. Methods: In this prospective study, a total of 2002 patients from a trauma ward were screened and assessed for delirium risk using the 4AT score and the AI-based tool at admission, on the evening of admission, and on the following day, with each assessment predicting the risk of delirium occurrence at any point during the remainder of the hospital stay. The AI tool based its predictions on clinical data automatically derived from electronic health records. The AI-based tool applied a previously developed and validated random forest model (not developed as part of this study). Results: Of the 2002 processed patients, the AI prediction tool correctly predicted 87.3% (165/189) of patients with delirium onset as at-risk, compared to an early detection rate of 28.0% (5/189) achieved by the 4AT score. Moreover, the AI-based tool correctly classified 73.9% (1340/1813) patients without delirium as not-at-risk, compared to 96.3% (1746/1813) for the 4AT score. Therefore, the AI-based tool achieved very good delirium predictions, with an area under the receiver operating characteristic curve of 0.884 (95% CI 0.8634‐0.9039). Conclusions: The AI-based delirium prognostic tool demonstrated superior early predictive capacity for delirium risk at admission, compared with the 4AT score. The AI-based tool enables proactive risk management and targeted risk reduction.

The Commissioners Court unanimously adopted a resolution urging protection of Brooke Army Medical Center (BAMC) and coordinated regional trauma capacity after testimony from military and health-system leaders who warned that a nearby civilian trauma expansion could dilute critical training volume.

The Public Health Trust Board approved a set of subcommittee items by voice vote, including continuing Ryder as a Level 1 trauma center, lease amendments with the University of Miami and Quest Diagnostics, credentialing and the FY27 audit work plan; the board also approved interior naming of the West Wing patient tower.

A public commenter representing emergency preparedness urged council to approve an interlocal agreement between the City of El Paso and University Medical Center to rotate and use whole blood for prehospital transfusions, citing trauma mortality data and past mass‑casualty needs.

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MOH, national trauma, network, patients, care, Ma

Alabama Network of Children's Advocacy Centers told the committee that federal grant cuts and reduced in-house clinicians have eroded access to trauma-specific mental health services; they urged statewide MOUs and investments to ensure specialized care reaches children in every county.

This cohort study investigates how accurately a simplified judgment-based binary field triage system can identify severely injured trauma casualties during combat operations.

Where expertise meets real-world impact Join leading clinicians, specialists, and frontline professionals for a dynamic, one-day conference dedicated to advancing major trauma care across the East...

Officials said a $71,338 Maryland EMS (MIMS) grant will purchase blood products and equipment to carry whole blood on ambulances and chase vehicles, expanding pre-hospital transfusion beyond State Police helicopters; the board approved acceptance.