About this trial
The study builds and applies an AI model to help doctors predict patient diagnoses and outcomes, such as survival or hospital stay. Real-time, multimodal data (labs, vital signs, history, imaging) from hospital records will be used. Patients will be tracked to compare the AI's performance with standard care. The goal is to improve diagnosis and treatment accuracy in a real-world, prospective study.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Patients admitted to any department of the hospital (e.g., ICU, general wards, emergency, outpatient services) during the study period.
Patients with available real-time electronic health record (EHR) data, including at least two of the following: laboratory results, vital signs, medical history, and imaging data.
Disqualifiers
None
Trial population
This study includes all patients admitted to any hospital department, with real-time electronic health record data (e.g., labs, vital signs, history, imaging). Participants must consent to data collection.
Trial design
Cohort
Prospective
Treatments tested in this trial
AI-associated strategy
Other interventionThe intervention in this study involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of diseases. Patients in this cohort will undergo standard examinations, with clinical decisions guided by the recommendations generated by the AI system.
Treatment groups
Trial outcomes
Primary outcomes
Area Under the Curve (AUC)
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
Overall Hospital Resource Utilization Improvement
The percentage reduction in overall hospital resource use (e.g., bed days, ICU admissions, diagnostic tests) attributed to AI-assisted decision-making, expressed as a percentage.
Population-Level Diagnostic Accuracy Enhancement
The overall improvement in diagnostic accuracy across all hospital patients (e.g., percentage of correct diagnoses or reduction in misdiagnoses) facilitated by the AI model, expressed as a percentage or ratio.
System-Wide Reduction in Adverse Event Rates
The percentage reduction in major adverse events (e.g., mortality, severe complications, or prolonged stays) across all hospital patients due to AI-assisted decision-making, expressed as a percentage.
Secondary outcomes
Overall Improvement in Hospital Patient Outcomes
The aggregate improvement in key patient outcomes (e.g., mortality, morbidity, recovery rates) across the entire hospital population due to AI-assisted decision-making, expressed as a composite score or percentage.
Enhancement of Healthcare System Efficiency
he overall improvement in hospital operational efficiency (e.g., reduced wait times, optimized resource allocation, decreased staff workload) attributed to the AI model, expressed as a percentage or qualitative rating.
Population Health Impact Score
A composite score reflecting the AI model's effect on population health within the hospital's catchment area (e.g., reduced disease burden, improved chronic disease management), expressed as a standardized index or percentage change.
Long-Term Public Health Benefit Index
A composite index measuring the AI model's long-term contribution to public health (e.g., reduced disease prevalence, improved life expectancy), expressed as a standardized score or percentage improvement.
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
The Eye Hospital of Wenzhou Medical University
Lead sponsor
Wenzhou Medical University
Sponsor institution