N/A N=108 Health Services Research

Artificial Intelligent Clinical Decision Support System Simulation Center Study for Technology Acceptance

Gastrointestinal Hemorrhage

Bottom Line

View on ClinicalTrials.gov: NCT05816473 ↗

Enrolled (actual)

108

Serious AEs

0.0%

Results posted

May 2026

Primary outcome: Primary: Median Change in Attitudes Towards Machine Learning Algorithms in Clinical Care Using UTAUT — 0.0; 0.0; 0.0; 0.3 units on a scale

Study Design & Population

Study type: Interventional
Phase: N/A
Interventions: LLM (Other)
Age: Pediatric, Adult, Older Adult
Sex: All
Sponsor: Yale University
Primary completion: Dec 2024

Outcome Measures

Outcome	Result	p-value
PRIMARY Median Change in Attitudes Towards Machine Learning Algorithms in Clinical Care Using UTAUT	0.0; 0.0; 0.0; 0.3; 0.6; 0.3	—
SECONDARY Clinician Decision Making of Triage of GI Bleeding	91.7; 92.1	—

Summary

The purpose of this research study is to measure the effect on of a large language model interface on the usability, attitudes, and provider trust when using a machine learning algorithm-based clinical decision support system in the setting of bleeding from the upper gastrointestinal tract (upper GIB). Specifically, the investigators are looking to assess the optimal implementation of such machine learning algorithms in simulation scenarios to best engender trust and improve usability. Participants will be randomized to either machine learning algorithm alone or algorithm with a large language model interface and exposed to simulation cases of upper GIB.

Eligibility Criteria

Inclusion Criteria

Internal Medicine residency trainees at study institution
Emergency Medicine residency trainees at study institution

Exclusion Criteria

N/A

View full record on ClinicalTrials.gov →

Data sourced from ClinicalTrials.gov (NCT05816473). Outcome figures and adverse-event rates are extracted automatically from the registry's posted results and are provided for clinician reference, not as a substitute for the primary publication.