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Artificial Intelligent Clinical Decision Support System Simulation Center Study for Technology Acceptance
Sponsor: Yale University
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.
Official title: Artificial Intelligent Clinical Decision Support System Simulation Center Study: Trust and Usefulness of Machine Learning Risk Stratification Tool for Acute Gastrointestinal Bleeding Using the Technology Acceptance Model
Key Details
Gender
All
Age Range
Any - Any
Study Type
INTERVENTIONAL
Enrollment
108
Start Date
2023-05-23
Completion Date
2024-12-31
Last Updated
2026-05-22
Healthy Volunteers
Yes
Conditions
Interventions
LLM
Use of a Large Language Model (LLM) chatbot interface to Interact with the Machine Learning Algorithm and interpretability dashboard.
Locations (1)
Yale New Haven Hospital
New Haven, Connecticut, United States