Chest Pain
Conditions
Keywords
large language model, Chest Pain
Brief summary
This study will evaluate the accuracy and efficiency of large language model in emergency triage.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTApplication of large language model in emergency chest pain triage.
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.
DIAGNOSTIC_TESTAccording to the normal procedures to receive medical treatment
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.
Sponsors
Peking University Third Hospital
Beijing Friendship Hospital
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The Diagnostic Accuracy Rate of MedGuide-V5 | 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
| Measure | Time frame | Description |
|---|---|---|
| The Satisfaction of Medical Personnel | 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. |
| Medical Personnel Treatment Plan Adjustment Rate | 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. |
| Emergency Department Revisit Rate within 30 Days | 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. |
Countries
China
Contacts
PRINCIPAL_INVESTIGATORYi-Da Tang, MD, PhD
Peking University Third Hospital
PRINCIPAL_INVESTIGATORWen-Yao Wang, MD, PhD
Peking University Third Hospital
Outcome results
None listed