Development of cardiovascular clinical research data warehouse and real-world research.
10.26599/1671-5411.2025.07.006
- Author:
Dan-Dan LI
1
;
Ya-Ni YU
1
;
Zhi-Jun SUN
1
;
Chang-Fu LIU
1
;
Tao CHEN
1
;
Dong-Kai SHAN
1
;
Xiao-Dan TUO
1
;
Jun GUO
1
;
Yun-Dai CHEN
1
Author Information
1. Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing, China.
- Publication Type:Journal Article
- From:
Journal of Geriatric Cardiology
2025;22(7):678-689
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors.
METHODS:The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients.
RESULTS:This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts.
CONCLUSION:The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.
