Point-of-Care AI Assistance and Critical Care Outcomes: A Randomized Trial

Conditions

• Acute Kidney Injury
• Acute Respiratory Failure (ARF)
• Critical Illness
• Delirium Confusional State
• Multi-organ Failure
• Sepsis
• Shock

Eligibility Criteria

Sex

ALL

Age

18 Years - N/A

Healthy Volunteers

Not accepted

Interventions

• Point-of-care large language model decision support (ChatGPT-5) — OTHER
  Use 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.

Study Details

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.

Key Dates

Start date

Jan 1, 2026

Status verified

Dec 2025

Primary completion

Dec 31, 2028

Completion

Jun 30, 2029

Study Design

Enrollment

1,000 participants (estimated)

Allocation

RANDOMIZED

Intervention model

PARALLEL

Primary purpose

TREATMENT

Arms

• No Intervention: Standard Care
  Patients receive usual ICU care per local practice. De-identified admission data may be processed and submitted to the LLM for research purposes, but AI output is not shared with treating clinicians and does not influence real-time management.
• Other: AI-Assisted Care
  Patients receive standard ICU care plus point-of-care LLM-based decision support at admission. De-identified admission data are formatted and submitted to an LLM (ChatGPT-5). The model returns a primary diagnosis, ranked differential diagnosis list, suggested additional information, and prioritized therapeutic recommendations. This output is provided to the admitting team for consideration in ongoing management.

Primary Outcome Measure

Composite of Medical Errors [ Time Frame: From the time of ICU admission through day 7 of ICU stay or ICU discharge, whichever comes first. ]

Central Contacts

• Eric Silverman, M.D. principal Investigator, M.D.
  508-344-5680
  [email protected]

Locations (1)

Find similar trials in Framingham, MA

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