This is a prospective, unmasked, randomized, multicenter clinical trial evaluating the impact of point-of-care large language model (LLM)-based decision support on diagnostic accuracy and clinical outcomes in adult medical intensive care unit (MICU) patients.
Consecutive adult ICU admissions at participating community hospitals (initially MetroWest Medical Center and St. Vincent Hospital) will be screened for eligibility. Eligible patients will be randomized 1:1 to standard care or an AI-assisted group. In both arms, initial evaluation and management will follow usual practice. For patients randomized to AI assistance, de-identified admission data (history and physical, labs, imaging reports, and other relevant documentation) will be formatted and submitted to a state-of-the-art LLM (ChatGPT-5) at the time of admission. The AI-generated differential diagnosis and therapeutic recommendations will be provided to the admitting team for consideration. For the standard care arm, LLM output will be generated but not shared with clinicians.
After discharge, a masked chart review will determine the "ground truth" primary diagnosis and extract outcomes including: Primary Outcome - a composite of medical errors (from time of ICU admission through day 7 of ICU stay, or ICU discharge, whichever comes first); Secondary Outcomes - 90-day mortality, ICU and hospital length of stay, and ventilator-free days.
Adult patients (≥ 18 years) admitted to the medical intensive care unit (MICU) at participating hospitals.
Direct admissions from the emergency department or transfers from medical wards to the MICU.
Critically ill patients meeting local ICU admission criteria.
Transfers to the MICU from outside hospitals, operating room, or post-anesthesia care unit.
Age < 18 years.
Incomplete or missing essential clinical information at admission (e.g., key labs or documentation not yet available).
Primary surgical or cardiac (e.g., STEMI) patients.