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

Mayo Clinic

Status: Invitation-only

Conditions: Large Language Model

Treatments: Other: CURE

Study type: Interventional

Funder types: Other

Identifiers

NCT06263855

24-000352

Details and patient eligibility

About

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.

Enrollment: 1,015 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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1,015 participants in 2 patient groups

Intervention

Experimental group

Description:

Clinicians who care for patients randomized to intervention will have access to CURE to assist with discharge summary writing.

Treatment:

Other: CURE

Control

No Intervention group

Description:

Clinicians who care for patients randomized to control will continue with standard practice for discharge summary writing.