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

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.

Study Overview

• Status: Recruiting
• Conditions: Internal Disease
• Intervention / Treatment: Diagnostic test: Artificial intelligence

• Study Type: Interventional
• Enrollment (Anticipated): 3000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Hui-Chih Wang, Dr.; Phone Number: +88623123456; Email: ticoer@ntuh.gov.tw
• Study Contact Backup: Name: Hsin-Hsi Chen, Dr.; Phone Number: 311 +886233664888; Email: hhchen@ntu.edu.tw

Study Locations

• Taiwan
  • Taipei, Taiwan, 100
    • Recruiting
    • National Taiwan University Hospital
    • Contact: Wang, Dr.; Phone Number: 265659 886-2-23123456; Email: ticoer@ntuh.gov.tw

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Control
Experimental: Experimental	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Senior doctor appraisal	Senior doctor appraisal which measured by an established questionnaire. Senior doctor will fill an expert-verified clinical note quality evaluation questionnaire after junior doctor finished patient interview and clinical note recording. The questionnaire is designed to use 5 points likert scale and higher scores mean a better outcome.	24 hours

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of diagnosis prediction	The percentage of predicted diagnosis match the final diagnosis.	patient discharge from ED, up to 1 week
Rationality of diagnosis prediction	Senior doctors will assess rationality of predicted diagnosis.	24 hours

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital
• Investigators: Study Chair: Huang, Dr., National Taiwan University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): December 12, 2022
• Primary Completion (Anticipated): March 30, 2023
• Study Completion (Anticipated): March 30, 2023

Study Registration Dates

• First Submitted: October 10, 2022
• First Submitted That Met QC Criteria: October 25, 2022
• First Posted (Actual): October 27, 2022

Study Record Updates

• Last Update Posted (Estimate): February 22, 2023
• Last Update Submitted That Met QC Criteria: February 20, 2023
• Last Verified: December 1, 2022

More Information

Terms related to this study

• Keywords: Artificial intelligence
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Emergencies
• Other Study ID Numbers: 202110012RIND

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No