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

NCT05816473 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 108

Last updated 2026-05-22

Study results available
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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.

Conditions

  • Gastrointestinal Hemorrhage

Interventions

OTHER

LLM

Use of a Large Language Model (LLM) chatbot interface to Interact with the Machine Learning Algorithm and interpretability dashboard.

Sponsors & Collaborators

  • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

    collaborator NIH

  • Yale University

    lead OTHER

Principal Investigators

  • Dennis Shung, MD · Yale School of Medicine Section of Digestive Diseases

Study Design

Allocation
NA
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-05-23
Primary Completion
2024-12-31
Completion
2024-12-31

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05816473 on ClinicalTrials.gov