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

Mayo Clinic

Overview

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Conditions

Large Language Model

Interventions

CUREOTHER

A large language model (LLM) to help clinicians prepare discharge summaries for hospitalized patients.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

PATIENTS 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

Locations (1)

Mayo Clinic

Rochester, Minnesota, United States

Outcomes

Primary Outcomes

Rate of patient accrual

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.

Time frame: Three months

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.