Actively Recruiting
Artificial Intelligence-driven Virtual Standardized Pediatric Patients Trial
Led by Southern Medical University, China · Updated on 2024-12-19
459
Participants Needed
3
Research Sites
4 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Background: China's healthcare system for children faces significant challenges, particularly due to the limited pediatric service capacity of primary healthcare institutions. A shortage of effective and accessible training tools for primary care doctors further hinders progress in addressing this gap. Technological advancements, especially in artificial intelligence, offer a potential solution to improve pediatric care. Artificial intelligence-driven virtual standardized patients (VSPs), leveraging internet and virtual simulation technologies, simulate clinical cases with specific disease characteristics, providing an innovative, efficient, and flexible training method. VSPs are increasingly utilized in medical education, clinical reasoning, and licensure exams. This study focuses on using VSPs to improve the management of common pediatric conditions, which are major health concerns for children and impose significant psychological and financial burdens on families. Methods: This study will involve a three-arm randomized controlled trial to evaluate the effectiveness of a virtual pediatric standardized patient platform in enhancing primary care doctors' management of common pediatric diseases. At least 459 participants, including general practitioners, internal medicine practitioners, surgeons, and pediatricians from more than 10 provinces across China, will be randomly assigned to one of three groups: the virtual patient platform group, the case teaching manual group, or the case teaching video group. Five virtual patient cases covering pneumococcal pneumonia, rotavirus enteritis with hypovolemic shock, hand-foot-and-mouth disease, acute appendicitis, and respiratory failure will be developed, along with corresponding case teaching materials. After a two-week learning period, participants' disease management abilities will be assessed using clinical vignettes. The primary outcome is adherence to best clinical practice guidelines, categorized into full adherence, partial adherence, and nonadherence. Discussion: This study aims to leverage artificial intelligence for capacity enhancement, targeting the shortcomings of primary care pediatrics and using VSP to help enhance primary care pediatrics capacity. It is a randomized controlled trial involving over 300 primary healthcare institutions across more than 10 provinces in China, ensuring broad and representative participation from both developed and underdeveloped regions.
CONDITIONS
Official Title
Artificial Intelligence-driven Virtual Standardized Pediatric Patients Trial
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Work at a primary or secondary hospital, community health center, township health center, or village health clinic
- Be a practicing (assistant) doctor or rural doctor in the eligible institutions
- Have a scope of practice including general practice, internal medicine, surgery, or pediatrics
You will not qualify if you...
- Work at specialized medical institutions such as specialized hospitals or dental clinics
- Work at public health prevention and treatment institutions such as tuberculosis prevention centers
- Work at ethnic medicine institutions such as Mongolian or Tibetan hospitals
- Work at hospitals above the secondary level
- Work at hospitals that have not yet been graded due to short establishment or unstable operations
AI-Screening
AI-Powered Screening
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Trial Site Locations
Total: 3 locations
1
Yuntan Street Community Health Center
Guiyang, Guizhou, China, 550081
Actively Recruiting
2
Jinxi County People's Hospital
Fuzhou, Jiangxi, China, 344899
Actively Recruiting
3
Honghe County People's Hospital
Yisa, Yunnan, China, 654499
Actively Recruiting
Research Team
L
Luo
CONTACT
How is the study designed?
Study Type
INTERVENTIONAL
Masking
SINGLE
Allocation
RANDOMIZED
Model
PARALLEL
Primary Purpose
HEALTH_SERVICES_RESEARCH
Number of Arms
3
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