NCT05816473

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

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

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 106 (actual)
• Sponsor: Yale University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

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.

Detailed description

The experiment will deploy a previously validated machine learning algorithm trained on existing clinical datasets within simulation scenarios in which a patient with acute gastrointestinal bleeding (at low, moderate, and high risk for poor outcome) is evaluated. Prior to the simulation, a baseline educational module about artificial intelligence, machine learning, and clinical decision support will be provided to all participants. The investigators will establish psychological safety by detailing what is available in the room, the opportunity to call a consultant, and availability of laboratory and radiographic studies. Each clinical scenario will run for approximately 10 minutes based on real patient cases where vital signs change over time and laboratory values are made available at specific points in the assessment. The study will evaluate the effect of a large language model-based interaction with the machine learning algorithm with interpretability dashboard compared to the machine learning algorithm with interpretability dashboard alone. Each participant will receive three scenarios in randomized order of risk. For the large language model interaction arm, participants will be provided the computer workstation a LLM chatbot interface of the algorithm and interpretability dashboard For the machine learning dashboard arm, participants will be provided the computer workstation with the algorithm and interpretability dashboard.

Conditions

• Gastrointestinal Hemorrhage

Interventions

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

Timeline

• Start date: 2023-05-23
• Primary completion: 2024-12-31
• Completion: 2024-12-31
• First posted: 2023-04-18
• Last updated: 2025-03-10

Locations

1 site across 1 country: United States