Clinical trial NCT06699433: Artificial Intelligence-driven Virtual Standardized Pediatric Patients Trial
Official title: Utilizing Artificial Intelligence-driven Virtual Standardized Pediatric Patients to Enhance the Capabilities of Primary Healthcare Doctors in China for Managing Common Pediatric Diseases: a Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Pneumococcal Pneumonia
Rotavirus Enteritis With Hypovolemic Shock
Hand-foot-and-mouth Disease
Acute Appendicitis
Respiratory Failure (Pediatric Patients)
Lead sponsor: Southern Medical University, China
Sponsor class: OTHER
First posted: 2024-11-21
Enrollment: 459
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief 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.


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

Primary outcomes:
measure: Doctor adherence to best clinical practice guidelines
timeFrame: Through study completion, an average of 6 months
Secondary outcomes:
measure: Dichotomous variable of doctor adherence to best clinical practice guidelines
timeFrame: Through study completion, an average of 6 months
measure: The degree of accuracy of a doctor's diagnosis according to best clinical practice guidelines
timeFrame: Through study completion, an average of 6 months
measure: Doctor score of examination that is directly related to handling the disease
timeFrame: Through study completion, an average of 6 months
measure: Doctor score of examination that is related to expansion skills of handling the disease
timeFrame: Through study completion, an average of 6 months
measure: The level at which the doctor focuses on meeting the actual needs of the patient and gives due consideration to the patient's feelings
timeFrame: Through study completion, an average of 6 months
measure: Adoption of training by doctors
timeFrame: Through study completion, an average of 6 months
measure: Costs to researchers of developing and implementing three types of training
timeFrame: Through study completion, an average of 6 months
measure: Doctor score of acceptability of three types of training
timeFrame: Through study completion, an average of 6 months
Other outcomes:
Locations:
city: Guiyang
country: China
facility: Yuntan Street Community Health Center
state: Guizhou
city: Fuzhou
country: China
facility: Jinxi County People's Hospital
state: Jiangxi
city: Honghe
country: China
facility: Honghe County People's Hospital
state: Yunnan
Study officials:
affiliation: National Clinical Research Center for Child Health and Disorders, Chongqing, China
name: Yao Zhao, Doctor
role: PRINCIPAL_INVESTIGATOR
References: