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.
This study aims to build and apply an artificial intelligence (AI) model to assist doctors in predicting patient diagnoses and outcomes, such as survival or hospital stay length. Patients will be enrolled across the hospital, and real-time, multimodal health data-including lab results, vital signs, medical history, and imaging-from electronic health records will be used. The study will follow participants to evaluate the AI model's performance against standard practice. The goal is to improve the accuracy and speed of diagnoses and treatments, enhancing patient care. This prospective study tests the model in real-world hospital settings.
Study Type
OBSERVATIONAL
Enrollment
50,000,000
The 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.
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
RECRUITINGSecond Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
RECRUITINGArea 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).
Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 1 year
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.
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Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 1 year