Background. Trauma care requires rapid and accurate clinical documentation and auditing to ensure patient safety. Currently, manual auditing of patient records is highly time consuming, which contributes to workflow delays and administrative burden on physicians in high volume emergency departments.

Objective. This study aims to evaluate the time efficiency and diagnostic accuracy of a Large Language Model artificial intelligence system compared to standard manual clinical auditing by physicians.

Methods. This is a prospective randomized controlled trial conducted at the emergency medicine department of a tertiary care teaching hospital in rural Kerala. Eligible trauma patient records will be randomized into two groups. In the intervention arm, clinical triage notes will be audited concurrently by the artificial intelligence system. In the comparator arm, notes will be manually audited by trained emergency medicine physicians. Both arms will evaluate the clinical notes for adherence to the standardized World Health Organization Trauma Checklist.

Outcomes. The primary outcome is the total time taken in seconds to complete the clinical audit per patient record. Secondary outcomes include evaluating the diagnostic accuracy of the artificial intelligence system in identifying missed clinical protocols. This includes calculating the sensitivity, specificity, positive predictive value, and negative predictive value when compared against a blinded gold standard expert assessment.

Significance. The findings of this trial will help determine if artificial intelligence can successfully optimize emergency medical workflows, act as a reliable clinical decision support system, and prevent socio economic impacts caused by treatment delays in resource limited settings.