Artificial Intelligence-driven Virtual Standardized Pediatric Patients Trial

Summary

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

• Pneumococcal Pneumonia
• Rotavirus Enteritis With Hypovolemic Shock
• Hand-foot-and-mouth Disease
• Acute Appendicitis
• Respiratory Failure (Pediatric Patients)

Interventions

BEHAVIORAL

Virtual standardized patients (VSPs)

Virtual standardized patients (VSPs), utilizing internet and virtual simulation technology, emulate patients with specific disease characteristics and clinical manifestations. With advantages in safety, flexibility, convenience, and efficiency, VSPs are used in medical education, clinical reasoning training, and licensure examinations. Doctors will interact with VSPs to conduct clinical simulations and training, including consultations, physical examinations, auxiliary examinations, and treatment decision-making, to enhance their capabilities for managing common pediatric diseases

BEHAVIORAL

Case teaching manuals

Doctors will use case teaching manuals to enhance their capabilities for managing common pediatric diseases

BEHAVIORAL

Case teaching videos

Doctors will use case teaching videos to enhance their capabilities for managing common pediatric diseases

Sponsors & Collaborators

• Southern Medical University, China

  lead OTHER

Principal Investigators

• Yao Zhao, Doctor · National Clinical Research Center for Child Health and Disorders, Chongqing, China

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

SINGLE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-12-31

Primary Completion

2024-12-31

Completion

2025-01-31

Countries

• China

Study Locations