Clinical Language Evaluation With AI for Residents (CLEAR2)

The University of Texas System (UT)

Status: Not yet enrolling

Conditions: Patient Communication

Treatments: Behavioral: educational LLM-based feedback tool

Study type: Interventional

Funder types: Other

Identifiers

NCT07222644

HSC-MS-25-0920

Details and patient eligibility

About

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

Enrollment: 64 estimated patients

Sex: All

Ages: 18 to 50 years old

Volunteers: Accepts Healthy Volunteers

Inclusion and exclusion criteria

Inclusion Criteria:

• McGovern Medical School (MMS) general surgery residents
• postgraduate year (PGY) 1-5

Trial design

Primary purpose: Other

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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64 participants in 2 patient groups

Educational LLM-based feedback tool

Experimental group

Treatment:

Behavioral: educational LLM-based feedback tool

Control

No Intervention group