Artificial IntelligenceNCT04186104
Artificial Intelligence in Children's Clinic
A study testing Routine diagnostic process and Artificial intelligence assisted diagnosis process in people with Artificial Intelligence and Outpatient, run by Shanghai Jiao Tong University School of Medicine at 2 sites in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 2 months to 18 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Outpatient
- What is being tested?
- Routine diagnostic process and Artificial intelligence assisted diagnosis process
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 626 participants
- Where
- 2 sitesChina
- Study dates
- 21 Mar 2020to 29 Jun 2021
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 2 months to 18 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Who may be able to join
- Patients aged 2 months to 18 years old and will go to Shanghai children's medical center for treatment.
Who cannot join
- People who don't agree to participate.
- People who can't cooperate.
- People who are difficult to follow up.
Trial locations and contacts
2 sites in 1 country.
China2 sites
Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200127
Shanghai Children's Medical Center
Shanghai
About this study
Official title: 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.
Read the detailed description
Relying on mobile application and computer software, it would achieve:
- Intelligent guidance and matching the department;
- Intelligent medical history collection, and AI medical record generation;
- Automatically recommend examination items;
- Assist in clinical diagnosis and make intelligent diagnosis suggestion.
- Conditions
- Artificial Intelligence, Outpatient
- Being tested
- Routine diagnostic process, Artificial intelligence assisted diagnosis process
- Keywords
- Artificial Intelligence, Outpatient, process improvement
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Routine diagnostic processOther
Patients follow the procedures of registration, waiting, attendance, waiting, examination, waiting, attendance.
Artificial intelligence assisted diagnosis processOther
Patients follow the procedures of registration, AI recommended examination items, Self-service medical history collection ,examination, waiting, AI-assisted attendance.
| Study group | Role | What they receive |
|---|---|---|
| Patients with routine outpatient service process | Experimental | 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. Receives: Other: Routine diagnostic process |
| Patients with AI assisted outpatient service process | Experimental | 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. Receives: Other: Artificial intelligence assisted diagnosis process |
What is being measured
The primary outcome is the main question the study is designed to answer.
Evaluate the efficiency of the two processes
Compare the average waiting time for single patient and average visiting time for single patient.
Measured: up to 1 months
Secondary outcomes (3)
Evaluate patients' rate of satisfaction for medical processes
The satisfaction questionnaire would be used to compare the rate of satisfaction between the two processes.
Measured: up to 1 months
Economic measurements
Spend money of outpatient, spend money of examination et al.
Measured: up to 1 months
Work efficiency of doctors
Using historical data for before-and-after comparisons, to compare the influence of intelligent medical history collection on the visit time of each patient.
Measured: up to 1 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Dec 2019
- Study start21 Mar 2020
- Primary completion29 Jun 2021
- Study completion29 Jun 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Shanghai Jiao Tong University School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 626 participants (actual)
Sponsor and investigators
- Lead sponsor
- Shanghai Jiao Tong University School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sijia Gu, Shanghai Jiao Tong University School of Medicine
- Investigators
- Shijian Liu, Ph.D, Principal investigator, Shanghai Children's Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04186104
- Sponsor's protocol ID
- SCMCIRB-K2019020-2
- Status last verified
- Jun 2021
- First submitted
- 12 Nov 2019
- Last updated
- 1 Jul 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, He M. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep. 2018 Mar 12;8(1):4330. doi: 10.1038/s41598-018-22612-2.
PubMed 29531299Shen TL, Fu XL. [Application and prospect of artificial intelligence in cancer diagnosis and treatment]. Zhonghua Zhong Liu Za Zhi. 2018 Dec 23;40(12):881-884. doi: 10.3760/cma.j.issn.0253-3766.2018.12.001. Chinese.
PubMed 30605975Kantarjian H, Yu PP. Artificial Intelligence, Big Data, and Cancer. JAMA Oncol. 2015 Aug;1(5):573-4. doi: 10.1001/jamaoncol.2015.1203. No abstract available.
PubMed 26181906Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr;69S:S36-S40. doi: 10.1016/j.metabol.2017.01.011. Epub 2017 Jan 11.
PubMed 28126242Szolovits P, Patil RS, Schwartz WB. Artificial intelligence in medical diagnosis. Ann Intern Med. 1988 Jan;108(1):80-7. doi: 10.7326/0003-4819-108-1-80.
PubMed 3276267Kreps GL, Neuhauser L. Artificial intelligence and immediacy: designing health communication to personally engage consumers and providers. Patient Educ Couns. 2013 Aug;92(2):205-10. doi: 10.1016/j.pec.2013.04.014. Epub 2013 May 15.
PubMed 23683341Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med. 2018 Mar;24(2):117-123. doi: 10.1097/MCP.0000000000000459.
PubMed 29251699Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol. 2019 Mar-Apr;64(2):233-240. doi: 10.1016/j.survophthal.2018.09.002. Epub 2018 Sep 22.
PubMed 30248307Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make doctors obsolete? BMJ. 2018 Nov 7;363:k4563. doi: 10.1136/bmj.k4563. No abstract available.
PubMed 30404897Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 2014 Jan;16(1):441. doi: 10.1007/s11886-013-0441-8.
PubMed 24338557Singh G, Al'Aref SJ, Van Assen M, Kim TS, van Rosendael A, Kolli KK, Dwivedi A, Maliakal G, Pandey M, Wang J, Do V, Gummalla M, De Cecco CN, Min JK. Machine learning in cardiac CT: Basic concepts and contemporary data. J Cardiovasc Comput Tomogr. 2018 May-Jun;12(3):192-201. doi: 10.1016/j.jcct.2018.04.010. Epub 2018 Apr 30.
PubMed 29754806Huang Q, Zhang F, Li X. Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey. Biomed Res Int. 2018 Mar 4;2018:5137904. doi: 10.1155/2018/5137904. eCollection 2018.
PubMed 29687000Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2020 Feb 28;471:61-71. doi: 10.1016/j.canlet.2019.12.007. Epub 2019 Dec 10.
PubMed 31830558Wall J, Krummel T. The digital surgeon: How big data, automation, and artificial intelligence will change surgical practice. J Pediatr Surg. 2020 Jan;55S:47-50. doi: 10.1016/j.jpedsurg.2019.09.008. Epub 2019 Nov 16.
PubMed 31767194Adamson AS, Welch HG. Machine Learning and the Cancer-Diagnosis Problem - No Gold Standard. N Engl J Med. 2019 Dec 12;381(24):2285-2287. doi: 10.1056/NEJMp1907407. No abstract available.
PubMed 31826337
Study at a glance
From the registry record, last updated by the sponsor on 1 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Artificial Intelligence in Children's Clinic
- ClinicalTrials.gov ID
- NCT04186104
- Acronym / sponsor ID
- SCMCIRB-K2019020-2
- Conditions
- Artificial Intelligence, Outpatient
- Keywords
- Artificial Intelligence, Outpatient, process improvement
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 626 participants (actual)
- Lead sponsor
- Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Study start
- 21 Mar 2020 (actual)
- Primary completion
- 29 Jun 2021 (actual)
- Study completion
- 29 Jun 2021 (actual)
- First posted
- 4 Dec 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Relying on mobile application and computer software, it would achieve:
- Intelligent guidance and matching the department;
- Intelligent medical history collection, and AI medical record generation;
- Automatically recommend examination items;
- Assist in clinical diagnosis and make intelligent diagnosis suggestion.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients with routine outpatient service process | Experimental | Other: Routine diagnostic processAfter 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. |
| Patients with AI assisted outpatient service process | Experimental | Other: Artificial intelligence assisted diagnosis processAfter 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. |
Who may qualify
- Ages
- 2 months to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
Key inclusion criteria (1 listed)
- Patients aged 2 months to 18 years old and will go to Shanghai children's medical center for treatment.
Key exclusion criteria (3 listed)
- People who don't agree to participate.
- People who can't cooperate.
- People who are difficult to follow up.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Routine diagnostic processPatients follow the procedures of registration, waiting, attendance, waiting, examination, waiting, attendance. | Other | Patients with routine outpatient service process |
| Artificial intelligence assisted diagnosis processPatients follow the procedures of registration, AI recommended examination items, Self-service medical history collection ,examination, waiting, AI-assisted attendance. | Other | Patients with AI assisted outpatient service process |
Primary outcome
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.
3 secondary outcomes
Evaluate patients' rate of satisfaction for medical processes
Time frame: up to 1 months
Economic measurements
Time frame: up to 1 months
Work efficiency of doctors
Time frame: up to 1 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Jul 2021.
- Background: PubMed 29531299Keel S, Lee PY, Scheetz J, Li Z, Kotowicz MA, MacIsaac RJ, He M. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep. 2018 Mar 12;8(1):4330. doi: 10.1038/s41598-018-22612-2.
- Background: PubMed 30605975Shen TL, Fu XL. [Application and prospect of artificial intelligence in cancer diagnosis and treatment]. Zhonghua Zhong Liu Za Zhi. 2018 Dec 23;40(12):881-884. doi: 10.3760/cma.j.issn.0253-3766.2018.12.001. Chinese.
- Background: PubMed 26181906Kantarjian H, Yu PP. Artificial Intelligence, Big Data, and Cancer. JAMA Oncol. 2015 Aug;1(5):573-4. doi: 10.1001/jamaoncol.2015.1203. No abstract available.
- Background: PubMed 28126242Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr;69S:S36-S40. doi: 10.1016/j.metabol.2017.01.011. Epub 2017 Jan 11.
- Background: PubMed 3276267Szolovits P, Patil RS, Schwartz WB. Artificial intelligence in medical diagnosis. Ann Intern Med. 1988 Jan;108(1):80-7. doi: 10.7326/0003-4819-108-1-80.
- Background: PubMed 23683341Kreps GL, Neuhauser L. Artificial intelligence and immediacy: designing health communication to personally engage consumers and providers. Patient Educ Couns. 2013 Aug;92(2):205-10. doi: 10.1016/j.pec.2013.04.014. Epub 2013 May 15.
- Background: PubMed 29251699Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med. 2018 Mar;24(2):117-123. doi: 10.1097/MCP.0000000000000459.
- Background: PubMed 30248307Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Surv Ophthalmol. 2019 Mar-Apr;64(2):233-240. doi: 10.1016/j.survophthal.2018.09.002. Epub 2018 Sep 22.
- Background: PubMed 30404897Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make doctors obsolete? BMJ. 2018 Nov 7;363:k4563. doi: 10.1136/bmj.k4563. No abstract available.
- Background: PubMed 24338557Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 2014 Jan;16(1):441. doi: 10.1007/s11886-013-0441-8.
- Background: PubMed 29754806Singh G, Al'Aref SJ, Van Assen M, Kim TS, van Rosendael A, Kolli KK, Dwivedi A, Maliakal G, Pandey M, Wang J, Do V, Gummalla M, De Cecco CN, Min JK. Machine learning in cardiac CT: Basic concepts and contemporary data. J Cardiovasc Comput Tomogr. 2018 May-Jun;12(3):192-201. doi: 10.1016/j.jcct.2018.04.010. Epub 2018 Apr 30.
- Background: PubMed 29687000Huang Q, Zhang F, Li X. Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey. Biomed Res Int. 2018 Mar 4;2018:5137904. doi: 10.1155/2018/5137904. eCollection 2018.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Shijian Liu, Ph.D principal investigator
Shanghai Children's Medical Center
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Routine diagnostic process and Artificial intelligence assisted diagnosis process in people with Artificial Intelligence and Outpatient, run by Shanghai Jiao Tong University School of Medicine at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Evaluate the efficiency of the two processes (time frame: up to 1 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04186104, which also lists 16 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Jul 2021.