Real-world Study
Conditions
Keywords
diagnosis, prediction, AI, outcome
Brief summary
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.
Detailed description
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.
Interventions
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.
Sponsors
The Eye Hospital of Wenzhou Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
1. Patients admitted to any department of the hospital (e.g., ICU, general wards, emergency, outpatient services) during the study period. 2. 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.
Exclusion criteria
Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| System-Wide Reduction in Adverse Event Rates | 1 year | 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. |
| Area Under the Curve (AUC) | 1 year | 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 | 1 year | 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 | 1 year | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Overall Improvement in Hospital Patient Outcomes | 1 year | 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 | 1 year | 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 | 1 year | 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 | 1 year | 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. |
Countries
China
Contacts
Primary ContactFei Liu, MD
Outcome results
None listed