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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Development and Implementation of a Dynamically Updated Big Data Intelligence Platform Using Electronic Medical
Nanfang Li1, Qing Zhu1, Yujie Dang1
1Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region; Xinjiang Hypertension Institute; National Health Committee Key Laboratory of Hypertension Clinical Research; Key Laboratory of Xinjiang Uygur Autonomous Region "Hypertension Research Laboratory"; Xinjiang Clinical Medical Research Center for Hypertension (Cardio-Cerebrovascular) Diseases, 830001 Urumqi, Xinjiang, China.
Insights
A new big data platform, UHDATA, was developed to identify secondary hypertension using electronic medical records. This dynamic database aids future clinical research on hypertension and its cardiovascular impacts.
Area of Science:
- Cardiology
- Medical Informatics
- Big Data Analytics
Background:
- Secondary hypertension is critical to diagnose, as cardiovascular disease remains a leading cause of death.
- Accurate diagnosis of secondary hypertension is essential for effective patient management and treatment.
- Developing advanced data platforms is crucial for improving research in hypertension.
Purpose of the Study:
- To develop a big data intelligence platform for secondary hypertension using electronic medical records.
- To facilitate future basic and clinical research on secondary hypertension.
- To create a dynamically updating database for hypertension research.
Main Methods:
- The Hypertension DATAbase at Urumchi (UHDATA) platform was developed using hospital data from December 2004.
- Electronic data acquisition, database synchronization, and ETL technologies were employed.
- The platform integrates demographic, medical, and sample data, including blood and tissue specimens.
Main Results:
- The UHDATA platform includes 295,297 hypertensive patients, with 14% diagnosed with secondary hypertension.
- Of 75,802 patients visiting the Hypertension Center, 43% were diagnosed with secondary hypertension.
- The database contains 1458 elements with a 90% fill rate and updates bi-weekly.
Conclusions:
- A dynamically updating electronic medical record database for hypertension was developed using computer information technology.
- This platform is valuable for advancing future research on secondary hypertension.
- The UHDATA platform supports continuous data inclusion and updates for ongoing research.
Background:
The accurate identification and diagnosis of secondary hypertension is critical,especially while cardiovascular heart disease continues to be the leading cause of death. To develop a big data intelligence platform for secondary hypertension using electronic medical records to contribute to future basic and clinical research.
Methods:
Using hospital data, the platform, named Hypertension DATAbase at Urumchi (UHDATA), included patients diagnosed with hypertension at the People's Hospital of Xinjiang Uygur Autonomous Region since December 2004. The electronic data acquisition system, the database synchronization technology, and data warehouse technology (extract-transform-load, ETL) for the scientific research big data platform were used to synchronize and extract the data from each business system in the hospital. Standard data elements were established for the platform, including demographic and medical information. To facilitate the research, the database was also linked to the sample database system, which includes blood samples, urine specimens, and tissue specimens.
Results:
From December 17, 2004, to August 31, 2022, a total of 295,297 hypertensive patients were added to the platform, with 53.76% being males, with a mean age of 59 years, and 14% with secondary hypertension. However, 75,802 patients visited the Hypertension Center at our hospital, with 43% (32,595 patients) being successfully diagnosed with secondary hypertension. The database contains 1458 elements, with an average fill rate of 90%. The database can continuously include the data for new hypertensive patients and add new data for existing hypertensive patients, including post-discharge follow-up information, and the database updates every 2 weeks. Presently, some studies that are based on the platform have been published.
Conclusions:
Using computer information technology, we developed and implemented a big database of dynamically updating electronic medical records for patients with hypertension, which is helpful in promoting future research on secondary hypertension.
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