Using AI to improve emergency triage for chest pain

The Application of Large Language Model in Emergency Chest Pain Triage

This study tests whether using AI can make it faster and easier for doctors to assess patients with chest pain in the emergency room.

Quick facts

• Phase: NA
• Study type: Interventional
• Enrollment: 2000 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: Peking University Third Hospital (other)
• Locations: 1 site (Beijing, Beijing Municipality)
• Trial ID: NCT06493175 on ClinicalTrials.gov

What this trial studies

This study evaluates the effectiveness of a large language model in the emergency triage of patients presenting with chest pain. It compares the accuracy and efficiency of this AI-assisted approach to traditional triage methods. The study aims to determine if the model can reduce the workload of medical staff while enhancing the speed and quality of patient assessment. Additionally, it assesses the model's ability to predict serious medical events and gathers patient feedback on the use of this technology. Economic benefits, including cost savings and resource allocation, will also be analyzed.

Who should consider this trial

Good fit: Ideal candidates include adults aged 18 and above who present with chest pain in an emergency setting.

Not a fit: Patients with severe cognitive impairments, unstable vital signs, or those explicitly referred to specific departments may not benefit from this study.

Why it matters

Potential benefit: If successful, this approach could lead to faster and more accurate triage for patients with chest pain, potentially improving outcomes.

How similar studies have performed: While the application of AI in medical triage is a growing field, this specific use of a large language model for chest pain triage is relatively novel.

Eligibility criteria

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.

Where this trial is running

Beijing, Beijing Municipality

• Peking University Third Hospital — Beijing, Beijing Municipality, China (RECRUITING)

Study contacts

• Principal investigator: Yi-Da Tang, MD, PhD — Peking University Third Hospital
• Study coordinator: Xiangbin Meng

Conditions: Chest Pain, large language model