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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04186104
Other study ID # SCMCIRB-K2019020-2
Secondary ID
Status Completed
Phase N/A
First received
Last updated
Start date March 21, 2020
Est. completion date June 29, 2021

Study information

Verified date June 2021
Source Shanghai Jiao Tong University School of Medicine
Contact n/a
Is FDA regulated No
Health authority
Study type Interventional

Clinical 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. 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.


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.


Recruitment information / eligibility

Status Completed
Enrollment 626
Est. completion date June 29, 2021
Est. primary completion date June 29, 2021
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 2 Months to 18 Years
Eligibility 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 Design


Related Conditions & MeSH terms


Intervention

Other:
Routine diagnostic process
Patients follow the procedures of registration, waiting, attendance, waiting, examination, waiting, attendance.
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.

Locations

Country Name City State
China Shanghai Children's Medical Center Shanghai
China Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine Shanghai Shanghai

Sponsors (1)

Lead Sponsor Collaborator
Shanghai Jiao Tong University School of Medicine

Country where clinical trial is conducted

China, 

References & Publications (16)

Adamson 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. — View Citation

Das 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. Review. — View Citation

Dilsizian 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. Review. — View Citation

Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make doctors obsolete? BMJ. 2018 Nov 7;363:k4563. doi: 10.1136/bmj.k4563. — View Citation

Hamet 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. Review. — View Citation

Huang 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. Review. — View Citation

Huang 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. Review. — View Citation

Kantarjian H, Yu PP. Artificial Intelligence, Big Data, and Cancer. JAMA Oncol. 2015 Aug;1(5):573-4. doi: 10.1001/jamaoncol.2015.1203. — View Citation

Kapoor 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. Review. — View Citation

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. — View Citation

Kreps 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. — View Citation

Shen 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. — View Citation

Singh 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. Review. — View Citation

Szolovits P, Patil RS, Schwartz WB. Artificial intelligence in medical diagnosis. Ann Intern Med. 1988 Jan;108(1):80-7. Review. — View Citation

Wall 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. Review. — View Citation

Wulsin DF, Gupta JR, Mani R, Blanco JA, Litt B. Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. J Neural Eng. 2011 Jun;8(3):036015. doi: 10.1088/1741-2560/8/3/036015. Epub 2011 Apr 28. — View Citation

* Note: There are 16 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Evaluate the efficiency of the two processes Compare the average waiting time for single patient and average visiting time for single patient. up to 1 months
Secondary Evaluate patients' rate of satisfaction for medical processes The satisfaction questionnaire would be used to compare the rate of satisfaction between the two processes. up to 1 months
Secondary Economic measurements Spend money of outpatient, spend money of examination et al. up to 1 months
Secondary 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. up to 1 months
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