AI-Assisted Medical Decision-Making
A Cohort Study to Evaluate an Artificial Intelligence Model for Assisting Medical Decision-Making Using Real-Time Hospital-Wide Electronic Health Record Data
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Fei Liu, MD
- Phone Number: +86 13810512704
- Email: liufei_2359@163.com
Study Locations
-
-
Zhejiang
-
Wenzhou, Zhejiang, China
- Recruiting
- First Affiliated Hospital of Wenzhou Medical University
-
Contact:
- Cheng Tang
- Phone Number: +86-0577-88002888
- Email: c249325687@163.com
-
Wenzhou, Zhejiang, China
- Recruiting
- Second Affiliated Hospital of Wenzhou Medical University
-
Contact:
- Linfeng Liu
- Email: llfwfe@163.com
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Hospital-Wide Patient Cohort
|
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.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Area Under the Curve (AUC)
Time Frame: 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).
|
1 year
|
|
Overall Hospital Resource Utilization Improvement
Time Frame: 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.
|
1 year
|
|
Population-Level Diagnostic Accuracy Enhancement
Time Frame: 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.
|
1 year
|
|
System-Wide Reduction in Adverse Event Rates
Time Frame: 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.
|
1 year
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Overall Improvement in Hospital Patient Outcomes
Time Frame: 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.
|
1 year
|
|
Enhancement of Healthcare System Efficiency
Time Frame: 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.
|
1 year
|
|
Population Health Impact Score
Time Frame: 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.
|
1 year
|
|
Long-Term Public Health Benefit Index
Time Frame: 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.
|
1 year
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
Other Study ID Numbers
- AI Prediction
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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