Effect of Large Language Model in Assisting Discharge Summary Notes Writing for Hospitalized Patients

Not Applicable

• Conditions: Large Language Model

• Registration Number: NCT06263855
• Lead Sponsor: Mayo Clinic

Brief Summary

This pilot study aims to assess the feasibility of carrying out a full-scale pragmatic, cluster-randomized controlled trial which will investigate whether discharge summary writing assisted by a large language model (LLM), called CURE (Checker for Unvalidated Response Errors), improves care delivery without adversely impacting patient outcomes.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2024-02-09
• Study First Submitted QC: 2024-02-09
• Study First Posted: 2024-02-16
• Status Verified: 2025-01
• Last Update Submitted: 2025-01-22
• Last Update Posted: 2025-01-24
• Study Start: 2026-01-01
• Primary Completion: 2026-07-01
• Study Completion: 2026-10-01

Recruitment & Eligibility

• Status: ENROLLING_BY_INVITATION
• Sex: All
• Target Recruitment: 1015

Inclusion Criteria

• Adult patients admitted to one of three participating cardiology services at Mayo Clinic in Rochester, MN

Exclusion Criteria

• Minor patients (<18)
• Patients admitted to a hospital service where CURE is not implemented

CLINICIANS

Inclusion Criteria:

• Clinicians who provide care to randomized patients included in this pilot

Exclusion Criteria:

• Clinicians who do not provide care to randomized patients included in this pilot

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Rate of patient accrual	Three months	The first feasibility outcome will be the rate of patient accrual. An accrual of one patient per day will be considered acceptable, i.e., 91 patients discharged from a 91-day period who are appropriately randomized and can be included in the analyses.

Trial Locations

Locations (1)

Mayo Clinic; 🇺🇸 Rochester, Minnesota, United States