AI-Assisted Medical Decision-Making
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-associated strategy.
- Who it may be relevant to
- Registry conditions: Real-world Study. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
A Cohort Study to Evaluate an Artificial Intelligence Model for Assisting Medical Decision-Making Using Real-Time Hospital-Wide Electronic Health Record Data
Overview
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
- Other AI-associated strategy
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.
Primary outcome measures
- Area Under the Curve (AUC) [Time frame: 1 year]
- Overall Hospital Resource Utilization Improvement [Time frame: 1 year]
- Population-Level Diagnostic Accuracy Enhancement [Time frame: 1 year]
- System-Wide Reduction in Adverse Event Rates [Time frame: 1 year]
Secondary outcome measures (4)
- Overall Improvement in Hospital Patient Outcomes [Time frame: 1 year]
- Enhancement of Healthcare System Efficiency [Time frame: 1 year]
- Population Health Impact Score [Time frame: 1 year]
- Long-Term Public Health Benefit Index [Time frame: 1 year]
Eligibility criteria
Inclusion criteria
- 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.
Exclusion criteria
Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 2 centers
- First Affiliated Hospital of Wenzhou Medical University — Wenzhou
- Second Affiliated Hospital of Wenzhou Medical University — Wenzhou
Identifiers
NCT: NCT06846229 · AI Prediction