Artificial Intelligence-driven Virtual Standardized Pediatric Patients Trial

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.


Eligibility Criteria:
1. Inclusion and Exclusion Criteria for Research Institutions

   Inclusion Criteria:
   * Primary and secondary hospitals
   * Community health centers (stations) and clinics, as well as township health centers and village health clinics

   Exclusion Criteria:
   * Specialized medical institutions (such as specialized hospitals and dental clinics)
   * Public health prevention and treatment institutions (such as tuberculosis prevention centers)
   * Ethnic medicine institutions (such as Mongolian and Tibetan hospitals)
   * Hospitals above the secondary level
   * Hospitals that have not yet been graded (due to their short establishment time and potentially unstable operations)
2. Inclusion and exclusion criteria for research subjects

Inclusion Criteria:

* Practicing (assistant) doctors and rural doctors working in medical institutions that meet the above conditions
* With a scope of practice only including general practice, internal medicine, surgery, and pediatrics

Exclusion Criteria:

-None