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Build-up Computed Assisted History Taking, Physical Examination and Diagnosis System of Emergency Patient Through Machine Learning (II)

NCT05596929 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 3000

Last updated 2023-02-22

No results posted yet for this study

Summary

In emergency department（ED）, physicians need to complete patient evaluation and management in a short time, which required different history taking, and physical examination skill in healthcare system.

Natural language processing（NLP） became easily accessible after the development of machine learning（ML）. Besides, electronic medical record（EMR） had been widely applied in healthcare systems. There are more and more tools try to capture certain information from the EMR help clinical workers handle increasing patient data and improving patient care.

However, to err is human. Physicians might omit some important signs or symptoms, or forget to write it down in the record especially in a busy emergency room. It will lead to an unfavorable outcome when there were medical legal issue or national health insurance review. The condition could be limited by a EMR supporting system. The quality of care will also improve.

The investigators are planning to analyze EMR of emergency room by NLP and machine learning. To establish the linkage between triage data, chief complaint, past history, present illness and physical examination. The investigators will try to predict the tentative diagnosis and patient disposition after the relationship being found. Thereafter, the investigators could try to predict the key element of history taking and physical examination of the patient and inform the physician when the miss happened. The investigators hope the system may improve the quality of medical recording and patient care.

Conditions

  • Internal Disease

Interventions

DIAGNOSTIC_TEST

Artificial intelligence

After the patients under triage classification to which randomly allocates in two groups. The group with AI intervention and the other without AI intervention.

Sponsors & Collaborators

  • National Taiwan University Hospital

    lead OTHER

Principal Investigators

  • Huang, Dr. · National Taiwan University Hospital

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
TRIPLE
Model
PARALLEL

Eligibility

Min Age
20 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-12-12
Primary Completion
2023-03-30
Completion
2023-03-30

Countries

  • Taiwan

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05596929 on ClinicalTrials.gov