At a glance
ClinicalIndex Comparison RecordStandardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.
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
In Brief
A clinical study evaluating LLM for Gastrointestinal Hemorrhage. Completed, enrolled 108 participants across 1 site.
Detailed 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.
Study Details
Timeline
Interventions
Use of a Large Language Model (LLM) chatbot interface to Interact with the Machine Learning Algorithm and interpretability dashboard.