Artificial Intelligence in Children's Clinic

June 28, 2021 updated by: Sijia Gu, Shanghai Jiao Tong University School of Medicine

Application of Artificial Intelligence in Children's Clinic

In China, the number of children's medical services is still far behind the growing demand for children's health care. The phenomenon of children's parents queuing overnight for registration is no longer surprising. This is because of the increase in the number of children and the shortage of pediatric talents. In the department of pediatrics, the number of patients increases year by year, but pediatrician is short of supply from beginning to end. In addition to outpatient service, pediatricians in large hospitals also perform operations, scientific research and other tasks. As a result, many doctors have to give up their vacations, which makes them miserable and reduces their enthusiasm for work. The long queuing time also reduced the satisfaction of patients, resulting in the intensification of the conflict between pediatric doctors and patients.

This research project aims to create a human-computer integrated system and develop a new diagnosis process embedded with artificial intelligence (AI). The function of AI system mainly includes 3 aspects. (1) The patient uses a mobile phone application embedded with AI that allows him to have check-up before see a doctor. The program will ask the patient a number of questions. Then, based on the patient's answers, AI will recommend a series of examination, all of which would be reviewed by the physician beforehand. After the patient pays for it, he could go straight to do the examination. So, next he could go to the doctor with the examination report which saves the patient the trouble of queuing. (2) At the same time, the AI system could also automate the medical history. The patient would complete self-help history collection in the spare time. The AI system collects the medical history and automatically import it to the doctor's computer. Doctors' main job is to modify the medical history generated by AI. To some extent, it lightens the burden of doctors. (3) During the visit, the AI system automatically captures the information in the patient's electronic medical record and generates the possible diagnosis. This process is of great help to junior doctors, and may serve as a cue.

In short, this study is helpful to effectively reduce the waiting time of patients and greatly increase their medical experience. While reducing the work intensity of doctors, the outpatient procedure of our hospital has been effectively optimized to alleviate the shortage of pediatricians to some extent.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Relying on mobile application and computer software, it would achieve:

  1. Intelligent guidance and matching the department;
  2. Intelligent medical history collection, and AI medical record generation;
  3. Automatically recommend examination items;
  4. Assist in clinical diagnosis and make intelligent diagnosis suggestion.

Study Type

Interventional

Enrollment (Actual)

626

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
      • Shanghai, China
        • Shanghai Children's Medical Center
    • Shanghai
      • Shanghai, Shanghai, China, 200127
        • Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

2 months to 18 years (Child, Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

Patients aged 2 months to 18 years old and will go to Shanghai children's medical center for treatment.

Exclusion Criteria:

  1. People who don't agree to participate.
  2. People who can't cooperate.
  3. People who are difficult to follow up.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Patients with routine outpatient service process
After registration, the patient waits in line at the door of the doctor's office. His doctor uses traditional methods to enter medical records by hand and make diagnosis independently. Then the patient waits in line to pay the bill and queues up for examination. Finally, the patient would take the examination report back to the doctor.
Other: Routine diagnostic process
Patients follow the procedures of registration, waiting, attendance, waiting, examination, waiting, attendance.
Experimental: Patients with AI assisted outpatient service process
After registration, the patient binds his information to the mobile phone application through outpatient' number. First, AI system would ask the patient a series of questions. Then it would make a judgment based on the patient's response. The system transmits the examination items to the doctor's computer and, with the doctor's approval, sends items back to the patient. So, patient could go straight to do the examination. While waiting for his turn, the patient enters the phone program again, and the AI system collects his medical history. The information is sent back to the doctor. When the patient goes to the doctor's office with the examination report, the doctor's computer already has his medical records. The doctor only needs to adjust the history according to the actual situation. After writing the medical history, the AI system could automatically make the diagnosis. Doctor uses the AI' results and his own judgment to make a comprehensive diagnosis.
Other: Artificial intelligence assisted diagnosis process
Patients follow the procedures of registration, AI recommended examination items, Self-service medical history collection ,examination, waiting, AI-assisted attendance.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Evaluate the efficiency of the two processes
Time Frame: up to 1 months
Compare the average waiting time for single patient and average visiting time for single patient.
up to 1 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Evaluate patients' rate of satisfaction for medical processes
Time Frame: up to 1 months
The satisfaction questionnaire would be used to compare the rate of satisfaction between the two processes.
up to 1 months
Economic measurements
Time Frame: up to 1 months
Spend money of outpatient, spend money of examination et al.
up to 1 months
Work efficiency of doctors
Time Frame: up to 1 months
Using historical data for before-and-after comparisons, to compare the influence of intelligent medical history collection on the visit time of each patient.
up to 1 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shanghai Jiao Tong University School of Medicine

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 21, 2020

Primary Completion (Actual)

June 29, 2021

Study Completion (Actual)

June 29, 2021

Study Registration Dates

First Submitted

November 12, 2019

First Submitted That Met QC Criteria

December 2, 2019

First Posted (Actual)

December 4, 2019

Study Record Updates

Last Update Posted (Actual)

July 1, 2021

Last Update Submitted That Met QC Criteria

June 28, 2021

Last Verified

June 1, 2021

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • SCMCIRB-K2019020-2

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

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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