The Application of Large Language Model in Emergency Chest Pain Triage

Peking University Third Hospital1,189 enrolled

Overview

This study will evaluate the accuracy and efficiency of large language model in emergency triage.

The study is to evaluate the value of large language model in emergency triage, their accuracy and efficiency were evaluated and compared with traditional triage. To explore whether the model can effectively reduce the workload of medical staff, while improving the speed and quality of triage. In addition, the ability of the model to predict serious medical events such as acute heart events and strokes was evaluated. It also included surveys of patients; acceptance and satisfaction with the use of the artificial intelligence-assisted triage system. Analyze the economic benefits of adopting this technology, including cost saving and optimal allocation of resources.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

1,189

Conditions

Chest Pain

Interventions

Application of large language model in emergency chest pain triage.DIAGNOSTIC_TEST

The large language model MedGuide-V5 is able to quickly extract key information from a patients description, and by analyzing these descriptions, it provides physicians with a possible initial diagnosis to help them quickly prioritize the treatment of patients.

According to the normal procedures to receive medical treatmentDIAGNOSTIC_TEST

After the artificial intelligence system evaluation, the patients will receive the diagnosis and treatment according to the normal procedure. The overall time of artificial triage, the triage of patients, and other data will be recorded. Patient visits should not be delayed by the use of artificial intelligence systems for evaluation.

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. All patients with chest pain entered the emergency triage procedure. 2. patients aged 18 and above. Exclusion Criteria: 1. Patients with severe cognitive impairment or inability to communicate. 2. There are patients who have been explicitly referred to specific departments (for example, some of the 120 transfer patients, who may go directly to the green channel) . 3. Patients with unstable vital signs . 4. Patients with potential medical problems. 5. Is participating in other clinical trials. 6. Failure to follow test procedures. 7. Those who refuse to sign the informed consent form.

Locations (1)

Peking University Third Hospital

Beijing, Beijing Municipality, China

Outcomes

Primary Outcomes

The Diagnostic Accuracy Rate of MedGuide-V5

To assess the consistency of the diagnosis of chest pain made by physicians with the assistance of large language models with the actual diagnosis made by patients after all examinations were completed.

Time frame: through study completion, an average of 10 months

Secondary Outcomes

The Satisfaction of Medical Personnel

To evaluate the satisfaction and acceptance of medical personnel with the use of large language models in assisting triage systems through methods such as questionnaire surveys. The name of this questionnaire is: Researcher Evaluation Form, with scores ranging from 1 to 10. The higher the score, the more helpful the large language model is to researchers.

Time frame: during evaluation

Medical Personnel Treatment Plan Adjustment Rate

The number of times medical personnel adjust treatment plans after receiving feedback from MedGuide V5's results and referring to the suggestions provided by the large language model.

Time frame: during evaluation

Emergency Department Revisit Rate within 30 Days

Evaluate the occurrence of patients revisiting the emergency department or being readmitted within 30 days after large language model-assisted triage and traditional triage.

Time frame: during evaluation

Data from ClinicalTrials.gov

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