Early and accurate triage of traumatic brain injury is critical for guiding treatment decisions that optimize patient outcomes. A major early clinical decision point occurs in the emergency department, where providers must decide whether to admit or discharge patients with head injuries, yet these decisions are often inconsistent and rarely supported by case-based frameworks. Here, we introduce RAPID-TBI (Retrieval Augmented Prediction for Informed Disposition in Traumatic Brain Injury), a multimodal system that predicts emergency department disposition using example-based retrieval to emulate clinical case-based reasoning. RAPID-TBI achieves state-of-the-art classification performance while enhancing interpretability by retrieving similar patients to inform predictions. Using a large multimodal TBI dataset from a major U.S. hospital system, RAPID-TBI integrates head CT scans, radiology reports, exam findings, laboratory values, vitals, and demographics through an attention-based encoder that generates patient embeddings for disposition classification. We further assessed RAPID-TBI across institutional and temporal generalizability, showing consistent performance and resilience to shifts in data distribution. Finally, we explored small language models as prompt-based classifiers for retrieval-guided prediction without fine-tuning. Together, these components enable RAPID-TBI to deliver consistent, individualized, and clinically grounded predictions, a promising step toward trustworthy, personalized decision support in TBI care.
Manjunath et al. (Mon,) studied this question.