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.
Inclusion and
Primary and secondary hospitals
Community health centers (stations) and clinics, as well as township health centers and village health clinics
Specialized medical institutions (such as specialized hospitals and dental clinics)
Public health prevention and treatment institutions (such as tuberculosis prevention centers)
Parallel
Virtual standardized patients (VSPs)
BehavioralVirtual 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
Case teaching manuals
BehavioralDoctors will use case teaching manuals to enhance their capabilities for managing common pediatric diseases
Case teaching videos
BehavioralDoctors will use case teaching videos to enhance their capabilities for managing common pediatric diseases
Doctor adherence to best clinical practice guidelines
Doctor adherence to best clinical practice guidelines, i.e., the extent to which doctors consistently make judgments and treatments based on best clinical practice guidelines and progression of the disease. Ordered categorical variable, consisting of three grades: full adherence, partial adherence, and nonadherence. It will be measured using clinical vignette method.
Dichotomous variable of doctor adherence to best clinical practice guidelines
Dichotomous variable of doctor adherence to best clinical practice guidelines. Dichotomous variable, consisting of two categories: full adherence, partial adherence or no adherence. It will be measured using clinical vignette method.
The degree of accuracy of a doctor's diagnosis according to best clinical practice guidelines
The degree of accuracy of a doctor's diagnosis according to best clinical practice guidelines. Ordered categorical variable, consisting of three categories: fully correct, partially correct, incorrect. It will be measured using clinical vignette method.
Doctor score of examination that is directly related to handling the disease
Doctor score of examination that is directly related to handling the disease. It is a continuous variable. It will be measured using examination paper called Disease Handling Capacity Scale. The lowest score is 0, the highest score is 5, and higher scores mean a better outcome.
Doctor score of examination that is related to expansion skills of handling the disease
Doctor score of examination that is related to expansion skills of handling the disease. It is a continuous variable. It will be measured using examination paper called Expanded Disease Handling Capacity Scale. The lowest score is 0, the highest score is 5, and higher scores mean a better outcome.
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