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Clinical Language Evaluation With AI for Residents

NCT07222644 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 64

Last updated 2025-10-30

No results posted yet for this study

Summary

The purpose of this study is to refine and test existing enterprise-grade large language model (LLM) based on generative artificial intelligence (AI), to assess the feasibility and acceptability of LLM-based feedback, to assess the ability of LLM-based feedback to improve residents' communications,to explore the ability of standardized patients to assess residents' communication and to explore the ability of residents to self-assess their communication complexity

Conditions

  • Patient Communication

Interventions

BEHAVIORAL

educational LLM-based feedback tool

Participants will have their verbal communications with standardized patients (SP) regarding 3 different scenarios recorded, transcribed, and analyzed in real-time by the large language model (LLM) and will receive feedback as suggestions and alternative scripts. These will be reviewed by residents between SP scenarios

Sponsors & Collaborators

  • Health Science Education Small Grants Program

    collaborator UNKNOWN

  • The University of Texas Health Science Center, Houston

    lead OTHER

Principal Investigators

  • Krislynn M Mueck, MD, MS, MPH · The University of Texas Health Science Center, Houston

Study Design

Allocation
RANDOMIZED
Purpose
OTHER
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
50 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-10-23
Primary Completion
2026-03-26
Completion
2026-05-28

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 NCT07222644 on ClinicalTrials.gov