N/A
Completed N=108
Artificial Intelligent Clinical Decision Support System Simulation Center Study for Technology Acceptance
Gastrointestinal Hemorrhage
Source: ClinicalTrials.gov NCT05816473 ↗
Enrolled (actual)
108
Serious AEs
0.0%
Results posted
May 2026
Primary outcomePrimary: 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
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.
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 | — |
Eligibility Criteria
Inclusion Criteria
- Internal Medicine residency trainees at study institution
- Emergency Medicine residency trainees at study institution
Exclusion Criteria
- N/A
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. Informational only — not medical advice.