Build-up Computed Assisted History Taking, Physical Examination and Diagnosis System of Emergency Patient Through Machine Learning (II) (MLD)
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Details and patient eligibility
About
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.
Enrollment
Sex
Ages
Volunteers
Inclusion criteria
- Over twenty years old
- Non-traumatic patient
Exclusion criteria
- Excluding the patients for administration reasons (issuing a medical certificate)
- Excluding the patients for non-emergency reasons like simply acupuncture, virus screening and prescription for medication.
- Excluding Patients who allocated to critical care station
Trial design
Primary purpose
Allocation
Interventional model
Masking
3,000 participants in 2 patient groups
Trial contacts and locations
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Central trial contact
Hsin-Hsi Chen, Dr.; Hui-Chih Wang, Dr.
Data sourced from clinicaltrials.gov
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