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.
Parallel
Point-of-care large language model decision support (ChatGPT-5)
Other interventionUse of a large language model (ChatGPT-5) to analyze de-identified ICU admission data (history, physical examination, laboratory results, imaging reports, and other documentation) at the time of admission. The model generates diagnostic and therapeutic recommendations that are shared with clinicians in the AI-assisted arm only.
Composite of Medical Errors
Proportion of patients with at least one clinically important diagnostic or therapeutic error identified by masked chart review (e.g., missed or delayed critical diagnosis, major guideline-discordant therapy with potential for harm).
90-day All-Cause Mortality
All-cause mortality within 90 days of index ICU admission, as determined by chart review and available follow-up records.
ICU Length of Stay
Total number of days spent in the ICU during the index hospitalization.
Ventilator-Free Days
Number of days alive and free from invasive mechanical ventilation during the first 28 days after ICU admission.
Hospital Length of Stay
Total number of days from hospital admission to hospital discharge during the index hospitalization.
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