Large Language Model
Conditions
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.
Interventions
OTHERCURE
A large language model (LLM) to help clinicians prepare discharge summaries for hospitalized patients.
Sponsors
Mayo Clinic
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORXiaoxi Yao
Mayo Clinic
Outcome results
None listed