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

NCT ID: NCT07293078

Last Updated: 2025-12-18

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: PHASE1/PHASE2
• Total Enrollment: 1000 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2026-01-01
• Study Completion Date: 2029-06-30

Brief Summary

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.

Detailed Description

The rapid development of large language models (LLMs) such as ChatGPT has created new opportunities and risks for their use in medicine. Although early studies suggest high diagnostic accuracy in complex clinical scenarios and ICU admissions, the impact of LLMs on real-world clinical outcomes and the optimal mode of physician-AI interaction remain uncertain. Published work from our group showed that ChatGPT-4 achieved diagnostic accuracy comparable to board-certified intensivists for ICU admissions in a retrospective study. However, prospective, randomized data on clinical outcomes are lacking.

This trial will evaluate a pragmatic paradigm for integrating LLMs at the time of ICU admission (point-of-care AI). All eligible adult MICU admissions at participating sites will be prospectively randomized to: (1) standard care, or (2) AI-assisted care in which an LLM receives standardized, de-identified admission data and returns a proposed primary diagnosis, ranked differential diagnosis (up to five conditions), suggested additional information, and prioritized therapeutic interventions. Admitting clinicians in the AI-assisted arm will be asked to review and optionally incorporate the AI recommendations and will complete a brief questionnaire regarding perceived utility and any changes in diagnosis or management.

A masked clinical adjudication panel will perform longitudinal chart review to define the "ground truth" primary diagnosis and assess error rates and outcomes. The primary endpoint is a composite of medical errors. The specific time frame will be from the time of ICU admission through day 7 of ICU stay, or ICU discharge, whichever comes first. Secondary endpoints will include 90-day mortality, ICU and hospital length of stay, and ventilator-free days. Other exploratory secondary endpoints will be considered. The trial is designed to enroll approximately 1000 patients across multiple MICUs, with interim analysis at 12 months to assess feasibility, integrity, and futility. The study is minimal risk, uses de-identified data for AI queries, and does not alter standard diagnostic testing or therapeutic options.

Conditions

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

Keywords

Critical Care; Intensive Care Unit; Large Language Model; Artificial Intelligence; Diagnostic Accuracy; Clinical Decision Support; Critical Care Outcomes; Sepsis; Shock; Acute Respiratory Failure; Multiorgan Failure

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: DOUBLE

Study Groups

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.

Group Type: NO_INTERVENTION

No interventions assigned to this group

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.

Group Type: OTHER

Point-of-care large language model decision support (ChatGPT-5)

Intervention Type: 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.

Interventions

Point-of-care large language model decision support (ChatGPT-5)

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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Adult patients (≥ 18 years) admitted to the medical intensive care unit (MICU) at participating hospitals.

2. Direct admissions from the emergency department or transfers from medical wards to the MICU.

3. Critically ill patients meeting local ICU admission criteria.

Exclusion Criteria

1. Transfers to the MICU from outside hospitals, operating room, or post-anesthesia care unit.

2. Age < 18 years.

3. Incomplete or missing essential clinical information at admission (e.g., key labs or documentation not yet available).

4. Primary surgical or cardiac (e.g., STEMI) patients.

5. Pregnant or postpartum women.

6. Prisoners.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

MetroWest Artificial Intelligence Research Workgroup

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Eric Silverman, M.D.

Role: PRINCIPAL_INVESTIGATOR

MetroWest Medical Center and St. Vincent Hospital

Locations

Framingham Union Hospital/MetroWest Medical Center

Framingham, Massachusetts, United States

Countries

United States

Central Contacts

Eric Silverman, M.D. principal Investigator, M.D.

Role: CONTACT

Phone: 508-344-5680

Email: esilverman@pamw.org

Facility Contacts

Eric Silverman, M.D.

Role: primary

Chih-Hsien Wu, M.D.

Role: backup

References

Singh J, Bohra R, Mukhtiar V, Fernandes W, Bhanushali C, Chinnamuthu R, Kanamgode SS, Ellis J, Silverman E. Diagnostic Accuracy of a Large Language Model (ChatGPT-4) for Patients Admitted to a Community Hospital Medical Intensive Care Unit: A Retrospective Case Study. J Intensive Care Med. 2025 Aug 17:8850666251368270. doi: 10.1177/08850666251368270. Online ahead of print.

Reference Type: BACKGROUND

PMID: 40820407 (View on PubMed)

Other Identifiers

IRB#2025-067

Identifier Type: OTHER

Identifier Source: secondary_id

POC-AI-ICU-001

Identifier Type: -

Identifier Source: org_study_id