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NCT Number: NCT06493175

The Application of Large Language Model in Emergency Chest Pain Triage

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

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Key information

Conditions

Chest Pain Neurologic Manifestations Pain Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Peking University Third Hospital

Beijing, Beijing Municipality, China

Location status: Recruiting

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • All patients with chest pain entered the emergency triage procedure.
  • patients aged 18 and above.

Exclusion criteria

  • Patients with severe cognitive impairment or inability to communicate.
  • 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) .
  • Patients with unstable vital signs .
  • Patients with potential medical problems.
  • Is participating in other clinical trials.
  • Failure to follow test procedures.
  • Those who refuse to sign the informed consent form.

Treatment and study plan

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 treatment

Diagnostic 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.

Primary outcomes

  1. The Diagnostic Accuracy Rate of MedGuide-V5

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

    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.

Secondary outcomes

  1. The Satisfaction of Medical Personnel

    Time frame: during evaluation

    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.

  2. Medical Personnel Treatment Plan Adjustment Rate

    Time frame: during evaluation

    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.

  3. Emergency Department Revisit Rate within 30 Days

    Time frame: during evaluation

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

Study contacts

Contact information is provided by the study sponsor or research team.

Xiangbin Meng

CONTACT

[email protected]

17600220171

Sponsors and collaborators

Lead sponsor

Peking University Third Hospital

Other

Collaborators

  • Jinan Central Hospital
  • Qingdao Municipal Hospital
  • The First Hospital of Hebei Medical University
  • Tianjin Medical University General Hospital

Registry information

Acronym: ALERT

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Jul 9, 2024
Registry last updated
Jul 9, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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