Language Model Assistance for Hospitalized Patients
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial tests a new tool called CURE, which uses a large language model to write hospital discharge summaries. The goal is to determine if CURE can improve the discharge process without negatively impacting patient health. One group of doctors will use CURE, while another will continue with the usual methods. The trial seeks adult patients admitted to specific cardiology services at Mayo Clinic. As an unphased trial, it offers patients the chance to contribute to innovative research that could enhance hospital discharge processes.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What prior data suggests that this method is safe for patients?
Research has shown that using a large language model (LLM) like the CURE system to write discharge summaries is safe. Studies have examined how well these models assist doctors without harming patients. One study found that using LLMs for discharge instructions produced summaries that were both safe and accurate.
Another study compared discharge summaries created by LLMs with those written by doctors. The results indicated that summaries from LLMs were just as safe and of similar quality. This suggests that using CURE should not negatively affect patient care.
Overall, these studies indicate that CURE is well-accepted and does not harm patients.12345Why are researchers excited about this trial?
Researchers are excited about the trial for CURE because it focuses on enhancing how clinicians write discharge summaries for hospitalized patients. Traditional methods rely on clinicians manually crafting these summaries, which can be time-consuming and inconsistent. CURE, however, assists by using language model technology to streamline and potentially improve the accuracy and efficiency of this process. This could lead to faster patient discharges, clearer communication, and better continuity of care, making it a promising development in hospital management practices.
What evidence suggests that CURE is effective for improving discharge summary writing?
Research has shown that large language models (LLMs), such as CURE, which is being tested in this trial, can improve the writing of discharge summaries in hospitals. One study found that LLMs made these summaries clearer and easier to read. Another study discovered that an LLM assistant helped write these summaries more quickly. These improvements ensure effective sharing of important health information when patients leave the hospital. Overall, evidence suggests that CURE can enhance the quality and speed of preparing discharge summaries without compromising patient care.12367
Who Is on the Research Team?
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
Exclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Intervention
Clinicians use the CURE model to assist with discharge summary writing
Control
Clinicians continue with standard practice for discharge summary writing
Follow-up
Participants are monitored for safety and effectiveness after intervention
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?
2
Treatment groups
Experimental Treatment
Active Control
Clinicians who care for patients randomized to intervention will have access to CURE to assist with discharge summary writing.
Clinicians who care for patients randomized to control will continue with standard practice for discharge summary writing.
Find a Clinic Near You
Who Is Running the Clinical Trial?
Mayo Clinic
Lead Sponsor
Citations
Large language model discharge summary preparation using ...
Our study aimed to test the efficacy of two LLMs to generate DC summaries which were scored using a validated discharge summary scoring metric.
Effect of Large Language Model in Assisting Discharge ...
Clinicians who care for patients randomized to intervention will have access to CURE to assist with discharge summary writing. Intervention/Treatment, Other : ...
Large Language Model Assistant for Emergency ...
Conclusion In this comparative effectiveness study, use of an on-site LLM assistant was associated with reduced writing time for ED discharge ...
Evaluation of a large language model to simplify discharge ...
This study explored using a large language model (LLM) to enhance discharge summary readability and augment it with lifestyle recommendations.
Large Language Model Assistant for Emergency ...
In this comparative effectiveness study, use of an on-site LLM assistant was associated with reduced writing time for ED discharge notes ...
The quality and safety of using generative AI to produce ...
Using GPT-3.5 to generate patient-centred discharge instructions, we evaluated responses for safety, accuracy and language simplification.
and Large Language Model-Generated Hospital Discharge ...
Objective: To determine whether LLM-generated discharge summary narratives are of comparable quality and safety to those of physicians. Design, ...
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