Generative AI-Assisted Clinical Decision Support for Medical Intensive Care Unit Physicians: A Pilot Cluster-Randomized Crossover Study

Evaluation of the Feasibility and Effectiveness of Generative AI-Assisted Multidisciplinary Decision Support in Medical Intensive Care: A Pilot Randomized Controlled Trial

This pilot study evaluated the feasibility and usefulness of generative artificial intelligence (AI) as a clinical decision-support tool for physicians working in a medical intensive care unit. Participating physicians were assigned by work period to either use a generative AI system in addition to usual clinical information resources or to use usual resources without generative AI. The assigned condition was then switched so that participants experienced both approaches. During the AI-assisted periods, physicians used de-identified clinical information and considered the AI-generated responses as reference information. All final clinical decisions remained the responsibility of the treating physicians. The study assessed acceptability, usability, satisfaction, perceived decision support, workload, confidence, and learning experience through repeated questionnaires.

Study Overview

• Status: Completed
• Conditions: Clinical Decision-making; Clinical Decision Support
• Intervention / Treatment: Other: Generative AI-Assisted Clinical Decision Support; Other: Usual Clinical Information Resources

Detailed Description

This was a single-center, open-label, pilot cluster-randomized crossover study involving physicians working in a medical intensive care unit. Each participating physician was observed during a scheduled one-month rotation in the medical intensive care unit. At the beginning of each monthly rotation, participating physicians were divided into two clusters. The clusters were randomized to begin with either the ChatGPT-assisted condition or the control condition. After approximately two weeks, each cluster crossed over to the alternate condition for the remainder of the one-month rotation. This design allowed participating physicians to experience both study conditions within the same rotation.

During the AI-assisted condition, physicians were encouraged to use ChatGPT (OpenAI) as a reference tool to support clinical information review and decision-making. Only non-identifiable clinical information was permitted to be entered into ChatGPT. Patient names, medical record numbers, contact information, and other information that could directly identify an individual patient were not entered. Physicians summarized clinically relevant information in their own words and considered the responses generated by ChatGPT when planning patient management. The Situation-Background-Assessment-Recommendation framework was recommended as an optional structure for organizing clinical information, but its use was not mandatory. Physicians were otherwise free to formulate their queries and interact with ChatGPT according to their clinical needs. A suggested prompt encouraged ChatGPT to present multiple management options, together with their rationale, potential benefits and risks, relevant supporting evidence, and areas of uncertainty. ChatGPT did not make or implement clinical decisions.

During the control condition, physicians used usual information resources, including discussions with other clinicians, multidisciplinary rounds, consultations, textbooks, clinical practice guidelines, PubMed, and other established clinical reference services, without using ChatGPT or other generative AI tools for study-related clinical decision support.

All diagnostic and treatment decisions were made independently by the treating physicians. Repeated questionnaires assessed satisfaction, decision-making experience, confidence, perceived efficiency, workload, educational value, and other aspects of clinical decision support. At study completion, participants also evaluated usability, satisfaction, perceived learning, reliance on ChatGPT, intention for future use, and the extent to which ChatGPT-generated suggestions were reflected in their clinical plans.

• Study Type: Interventional
• Enrollment (Actual): 15
• Phase: Not Applicable

Contacts and Locations

Study Locations

• South Korea
  • Seoul
    • Seoul, Seoul, South Korea, 03080
      • Seoul National University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

Age 19 years or older. Physicians, including residents, fellows, and attending physicians, working in the medical intensive care unit at Seoul National University Hospital.

Scheduled to work as a primary treating physician for at least 5 days during a planned observation period.

Able and willing to provide written informed consent.

Exclusion Criteria:

Declined or did not provide written informed consent. Withdrew consent from study participation.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: ChatGPT-Assisted Condition First, Then Control Condition; Physician clusters used ChatGPT-assisted clinical decision support during the first approximately two weeks of their one-month medical intensive care unit rotation. They then crossed over to the control condition and used usual clinical information resources without generative AI for the remainder of the rotation.	Other: Generative AI-Assisted Clinical Decision Support; During the assigned period, physicians were encouraged to use ChatGPT (OpenAI) as a generative AI-based reference tool to support clinical information review and decision-making.; Other: Usual Clinical Information Resources; During the control period, physicians used usual clinical information resources, including discussions with other clinicians, multidisciplinary rounds, specialty consultations, textbooks, clinical practice guidelines, PubMed, and established clinical reference services. No generative AI tool was used for clinical decision support during this period.
Experimental: Control Condition First, Then ChatGPT-Assisted Condition; Physician clusters used usual clinical information resources without generative AI during the first approximately two weeks of their one-month medical intensive care unit rotation. They then crossed over to the ChatGPT-assisted clinical decision-support condition for the remainder of the rotation.	Other: Generative AI-Assisted Clinical Decision Support; During the assigned period, physicians were encouraged to use ChatGPT (OpenAI) as a generative AI-based reference tool to support clinical information review and decision-making.; Other: Usual Clinical Information Resources; During the control period, physicians used usual clinical information resources, including discussions with other clinicians, multidisciplinary rounds, specialty consultations, textbooks, clinical practice guidelines, PubMed, and established clinical reference services. No generative AI tool was used for clinical decision support during this period.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Daily Physician Satisfaction Score	The score of 10 questionnaire items assessing physicians' satisfaction with their daily clinical work, modified from Shore and Franks (1986) and Suchman et al. (1993). Each item was rated on a 5-point Likert scale from -2 (strongly disagree) to +2 (strongly agree). Negatively worded items were reverse-scored. The mean score ranges from -2 to +2, with higher scores indicating greater satisfaction.	At the end of each working day during the one-month medical intensive care unit rotation
Daily Clinical Decision-Making Score	The score of 6 questionnaire items assessing satisfaction with the clinical decision-making process, perceived decision difficulty, clarity of the preferred treatment, availability of relevant information, and identification of factors affecting the decision. Items were modified from Gedney (1994) and Dolan (1999) and rated on a 5-point Likert scale from -2 (strongly disagree) to +2 (strongly agree). Negatively worded items were reverse-scored. The mean score ranges from -2 to +2, with higher scores indicating a more favorable decision-making experience.	At the end of each working day during the one-month medical intensive care unit rotation
Perceived Quality Score for ChatGPT	The score of 8 questionnaire items assessing the perceived information quality, system quality, and service quality of ChatGPT, modified from Pillong et al. (2025). Each item was rated on a 5-point Likert scale from -2 (strongly disagree) to +2 (strongly agree). The negatively worded response-time item was reverse-scored. The mean score ranges from -2 to +2, with higher scores indicating better perceived quality.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Generative AI Usability Score	The score of 3 questionnaire items assessing ease of use, ease of learning, and clarity of interaction with Generative AI (ChatGPT), modified from Pillong et al. (2025). Each item was rated on a 5-point Likert scale from -2 (strongly disagree) to +2 (strongly agree). The mean score ranges from -2 to +2, with higher scores indicating greater usability.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Satisfaction Score for Generative AI Use	The score of 6 questionnaire items assessing the perceived usefulness, productivity, effectiveness, overall satisfaction, appropriateness, and intention to reuse Generative AI (ChatGPT), modified from Pillong et al. (2025). Each item was rated on a 5-point Likert scale from -2 (strongly disagree) to +2 (strongly agree). The mean score ranges from -2 to +2, with higher scores indicating greater satisfaction.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Confidence	A single questionnaire item assessing whether use of Generative AI (ChatGPT) increased the physician's confidence in clinical decision-making. The item was rated on a 5-point Likert scale from -2 (strongly disagree) to +2 (strongly agree), with higher scores indicating a greater perceived increase in confidence.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Perceived Acquisition of New Knowledge or Clinical Insight	A single questionnaire item assessing whether the physician acquired new knowledge or clinical insight while using Generative AI (ChatGPT). The item was rated on a 5-point Likert scale from -2 to +2, with higher scores indicating greater perceived learning.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Application of Generative AI-Derived Knowledge to Other Clinical Situations	A single questionnaire item assessing whether knowledge obtained through Generative AI (ChatGPT) was applied to the care of other patients. The item was rated on a 5-point Likert scale from -2 to +2, with higher scores indicating greater transfer of learning.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Intention to Continue Using Generative AI (ChatGPT) in Future Clinical Practice	A single questionnaire item assessing the physician's intention to continue using Generative AI (ChatGPT) in future clinical practice. The item was rated on a 5-point Likert scale from -2 to +2, with higher scores indicating stronger intention for continued use.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Perceived Reliance on Generative AI (ChatGPT)	A single questionnaire item assessing whether the physician perceived increased reliance on Generative AI (ChatGPT) during clinical care. The item was rated on a 5-point Likert scale from -2 to +2, with higher scores indicating greater perceived reliance.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Perceived Reduction in Clinical Workload	A single questionnaire item assessing whether use of Generative AI (ChatGPT) reduced the physician's perceived clinical workload. The item was rated on a 5-point Likert scale from -2 to +2, with higher scores indicating greater perceived workload reduction.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Clinical Specialty Area in Which Generative AI (ChatGPT) Was Most Helpful	The clinical specialty area in which the physician perceived the greatest practical benefit from Generative AI (ChatGPT), selected from predefined categories or reported as free text, including cardiology, infectious diseases, pulmonology, and nephrology.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods
Percentage of Generative AI (ChatGPT) Suggestions Reflected in Clinical Plans	The physician-reported percentage of Generative AI (ChatGPT)-generated suggestions that were reflected in actual clinical management plans. Scores range from 0% to 100%, with higher percentages indicating greater incorporation of ChatGPT suggestions.	At the end of the one-month medical intensive care unit rotation, after completion of both crossover periods

Collaborators and Investigators

• Sponsor: Seoul National University Hospital
• Investigators: Principal Investigator: Minju Han, M.D., Seoul National University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2025
• Primary Completion (Actual): May 31, 2026
• Study Completion (Actual): May 31, 2026

Study Registration Dates

• First Submitted: July 12, 2026
• First Submitted That Met QC Criteria: July 12, 2026
• First Posted (Actual): July 16, 2026

Study Record Updates

• Last Update Posted (Actual): July 16, 2026
• Last Update Submitted That Met QC Criteria: July 12, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: Clinical Decision Support; Acceptability; Critical Care; Usability; Large Language Model; ChatGPT; Generative Artificial Intelligence; Physician Decision-Making; Medical Intensive Care Unit; Cluster-Randomized Crossover Study
• Other Study ID Numbers: 2510-145-1689

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Individual participant data will not be shared because of the small sample size, the limited number of physicians working in the study setting, and the potential risk of re-identification even after removal of direct identifiers. External sharing of individual-level data was not included in the participant consent or institutional review board-approved data management plan.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No