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

Study Status: RECRUITING
Recruit Status: RECRUITING
Study Type: INTERVENTIONAL

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 Technol

Brief 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 Summary:

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.

Sponsor: Yale University

Current Primary Outcome: The study will use a common set of dependent variables to assess baseline and post-intervention attitudes towards machine learning algorithms in clinical care using an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) survey assessing perceived usefulness of the system, perceived ease of use, attitudes towards using it, behavioral intentions, and trust, measured with a 5-point Likert scale.
Change in UTAUT survey response at recruitment prior to administration of scenarios and immediately after completion of scenarios.
The difference in time between the two will be approximately 60 minutes.

Original Primary Outcome: The study will use a common set of dependent variables to assess baseline and post-intervention attitudes towards machine learning algorithms in clinical care using an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) survey assessing perceived usefulness of the system, perceived ease of use, attitudes towards using it, behavioral intentions, and trust, measured with a 5-point Likert scale.
Change in UTAUT survey response at recruitment prior to administration of scenarios and immediately after completion of scenarios.
The difference in time between the two will be approximately 60 minutes.

Current Secondary Outcome:

Original Secondary Outcome:

Information By: Yale University

Dates:
Date Received: March 13, 2023
Date Started: May 23, 2023
Date Completion: May 23, 2023
Last Updated: November 08, 2023
Last Verified: November 01, 2023