Real-world StudyNCT06846229
AI-Assisted Medical Decision-Making
An observational study of Real-world Study, run by The Eye Hospital of Wenzhou Medical University at 2 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Real-world Study
- What is being tested?
- AI-associated strategy
- How many people
- 50,000,000 participants
- Where
- 2 sitesChina
- Study dates
- 24 Feb 2025to Jun 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study 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.
Who may be able to join
- 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.
Who cannot join
- Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Cheng Tang, +86-0577-88002888, c249325687@163.com
- Recruiting
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Linfeng Liu, llfwfe@163.com
About this study
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
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.
Read the 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.
- Conditions
- Real-world Study
- Being tested
- AI-associated strategy
- Keywords
- diagnosis, prediction, AI, outcome
- Medical subject headings
- Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-associated strategyOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Hospital-Wide Patient Cohort | Not described Receives: Other: AI-associated strategy |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 1 year
Secondary outcomes (4)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Feb 2025
- Study start24 Feb 2025
- Primary completionJun 2026 (estimated)
- Study completionJun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Real-world StudyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06846229
- Sponsor's protocol ID
- AI Prediction
- Status last verified
- Feb 2025
- First submitted
- 24 Feb 2025
- Last updated
- 3 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT06846229
- Acronym / sponsor ID
- AI Prediction
- Conditions
- Real-world Study
- Keywords
- diagnosis, prediction, AI, outcome
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 50,000,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 24 Feb 2025 (actual)
- Primary completion
- Jun 2026 (estimated)
- Study completion
- Jun 2026 (estimated)
- First posted
- 26 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Hospital-Wide Patient Cohort | – | Other: AI-associated strategy |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study 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.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (1 listed)
- Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-associated strategyThe 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. | Other | Hospital-Wide Patient Cohort |
Primary outcome
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).
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.
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.
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.
4 secondary outcomes
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Mar 2025.
- Individual participant dataSharing planned: No
Related trials for Real-world Study
RecruitingPhase 4
ASsessing The REAl-world Safety & Effectiveness of Spinal Muscular Atrophy Participants Treated With Intrathecal Onasemnogene Abeparvovec-brve (OAV101B) (ITVISMA®): A U.S. Pragmatic Multicenter Study (STREAM)
Spinal Muscular Atrophy
RecruitingPhase 3
Study to Assess the Injection Burden, Adverse Events, Change in Disease Activity, and Long-Term Preservation of Visual Acuity of Surabgene Lomparvovec in Adult Participants With Neovascular Age-Related Macular Degeneration (nAMD)
Neovascular Age-related Macular Degeneration
Enrolling by invitationPhase 4
INSIGHT-IBD Pragmatic Study
Ulcerative Colitis (UC), Crohn Disease (CD)
RecruitingPhase 4
BRAVO is a Phased Outbreak-response Study in the DRC Evaluating ERVEBO® Vaccination in ~20,000 Frontline Workers During the 2026 BDV Outbreak, Step A Assesses Real-world Effectiveness, Safety, and Impact on Severe Disease and Mortality, While Generating Evidence for Future BDV Vaccination Strategies
Ebola, Bundibugyo Virus Disease (BVD)
Not yet recruitingObservational study
A Study About Treatment Use in Adults With Narcolepsy Type 1 (NT1) in Europe and Canada
Narcolepsy Type 1
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Real-world Study, run by The Eye Hospital of Wenzhou Medical University at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 3 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Fei Liu, MD (+86 13810512704, liufei_2359@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (AUC) (time frame: 1 year). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Mar 2025.