Using artificial intelligence to analyze clinical data from electronic medical records
The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records (CardioMining-AI)
AHEPA University Hospital · NCT05176769
This study is testing whether using artificial intelligence can help doctors quickly and accurately analyze patient notes from hospital records to improve heart care.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 60000 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | AHEPA University Hospital (other) |
| Locations | 9 sites (Alexandroupoli and 8 other locations) |
| Trial ID | NCT05176769 on ClinicalTrials.gov |
What this trial studies
This study aims to demonstrate the effectiveness of artificial intelligence and machine learning in automating the extraction and processing of large volumes of unstructured clinical data from electronic medical records. By leveraging AI algorithms, the study seeks to enhance the speed, reliability, and accuracy of data analysis, which is crucial for improving risk stratification models in healthcare. The focus is on collecting clinical notes from hospitalized patients in cardiology departments across various hospitals in Greece, addressing the challenges posed by manual data processing. The ultimate goal is to improve diagnostic and therapeutic accuracy while ensuring patient privacy.
Who should consider this trial
Good fit: Ideal candidates for this study are hospitalized patients in cardiology departments whose medical records are electronically stored.
Not a fit: Patients who died during hospitalization and do not have a discharge letter will not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could significantly streamline data processing in healthcare, leading to better patient outcomes and more efficient healthcare systems.
How similar studies have performed: Other studies have shown promise in using AI for data analysis in healthcare, indicating that this approach could be effective.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Hospitalised patients in Cardiology Departments in Greece * Patients whose medical records are electronically stored in each hospital's computer/information systems Exclusion Criteria: * Patients that died during hospitalization, and thus no discharge letter was issued
Where this trial is running
Alexandroupoli and 8 other locations
- University Cardiology Clinic, Democritus University of Thrace — Alexandroupoli, Greece (NOT_YET_RECRUITING)
- 1st Department of Cardiology, Hippokration General Hospital — Athens, Greece (RECRUITING)
- Department of Cardiology, Heraklion University Hospital — Heraklion, Greece (NOT_YET_RECRUITING)
- University General Hospital of Larissa, University of Thessaly — Larissa, Greece (RECRUITING)
- Department of Cardiology, University of Patras Medical School — Pátrai, Greece (RECRUITING)
- 1st Cardiology Department, AHEPA University Hospital — Thessaloniki, Greece (RECRUITING)
- 3rd Cardiology Department, Hippokration Hospital — Thessaloniki, Greece (NOT_YET_RECRUITING)
- Cardiology Department, George Papanikolaou General Hospital — Thessaloniki, Greece (RECRUITING)
- Laboratory of Medical Physics, Aristotle University of Thessaloniki — Thessaloniki, Greece (RECRUITING)
Study contacts
- Study coordinator: George Giannakoulas, MD, PhD
- Email: ggiannakoulas@auth.gr
- Phone: 2310994830
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Artificial Intelligence, Machine Learning, Electronic Medical Records, artificial intelligence, machine learning, medical records, digital health