1015 Participants Needed

Language Model Assistance for Hospitalized Patients

Age: 18+
Sex: Any
Trial Phase: Academic
Sponsor: Mayo Clinic
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)

What You Need to Know Before You Apply

What is the purpose of this trial?

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.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

How does the treatment CURE differ from other treatments for hospitalized patients?

CURE is unique because it involves using large language models (LLMs) like ChatGPT to assist in medical documentation and patient communication, potentially improving health literacy and treatment adherence. Unlike traditional treatments, it focuses on enhancing clinical workflow and patient interaction through AI technology.12345

What data supports the effectiveness of the treatment CURE for hospitalized patients?

The research shows that language models like ChatGPT can assist in medical decision-making, as seen in a study where ChatGPT's recommendations for breast cancer treatment were similar to those of a tumor board in 70% of cases. This suggests that language models can be effective tools in supporting treatment decisions, potentially benefiting hospitalized patients.56789

Who Is on the Research Team?

XY

Xiaoxi Yao

Principal Investigator

Mayo Clinic

Are You a Good Fit for This Trial?

This trial is for hospitalized patients with aphasia, a condition that affects communication. The study will explore if using a large language model to assist in writing discharge summaries can improve care without negatively affecting patient outcomes.

Inclusion Criteria

Adult patients admitted to one of three participating cardiology services at Mayo Clinic in Rochester, MN
Clinicians who provide care to randomized patients included in this pilot

Exclusion Criteria

Patients admitted to a hospital service where CURE is not implemented
Clinicians who do not provide care to randomized patients included in this pilot
I am under 18 years old.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Clinicians use the CURE model to assist with discharge summary writing

3 months

Control

Clinicians continue with standard practice for discharge summary writing

3 months

Follow-up

Participants are monitored for safety and effectiveness after intervention

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

  • CURE
Trial Overview The intervention being tested is CURE, a large language model designed to help write discharge summaries for patients. The trial will determine its effectiveness and safety in clinical settings through a pragmatic, cluster-randomized controlled design.
How Is the Trial Designed?
2Treatment groups
Experimental Treatment
Active Control
Group I: InterventionExperimental Treatment1 Intervention
Group II: ControlActive Control1 Intervention

Find a Clinic Near You

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials
3,427
Recruited
3,221,000+

Citations

Large language model (ChatGPT) as a support tool for breast tumor board. [2023]
Harnessing language models for streamlined postcolonoscopy patient management: a novel approach. [2023]
Predicting health-related quality of life change using natural language processing in thyroid cancer. [2023]
Extracting findings from narrative reports: software transferability and sources of physician disagreement. [2017]
Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries. [2018]
[Clinical application of large language models : Does ChatGPT replace medical report formulation? An experience report]. [2023]
Why Patient Portal Messages Indicate Risk of Readmission for Patients with Ischemic Heart Disease. [2020]
Developing prompts from large language model for extracting clinical information from pathology and ultrasound reports in breast cancer. [2023]
Evaluating the Performance of Different Large Language Models on Health Consultation and Patient Education in Urolithiasis. [2023]
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