Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jul 13, 2024
Date Accepted: Jan 23, 2025
The Construction and Application of Clinical Decision Support System for Cardiovascular Diseases: Multi-modal Data-driven Development and Validation
ABSTRACT
Background:
Due to the acceleration of the aging population and the prevalence of unhealthy lifestyles, the incidence of cardiovascular diseases (CVD) in China continues to grow. However, due to uneven distribution of medical resources across regions and significant disparities in diagnostic and treatment levels, the diagnosis and management of CVD face considerable challenges. This study proposes a new technology based on CDSS (Clinical Decision Support Systems) to offer new ideas for the prevention and management of cardiovascular diseases.
Methods:
This study designed a CDSS system with data, learning, knowledge, and application layers. It integrates multi-modal data from hospital LIS (Laboratory Information System), HIS (Hospital Information System), EMR (Electronic Medical Records), electrocardiography, nursing, and other systems to build a knowledge model. It refers to the GRACE (Global Registry of Acute Coronary Events) assessment indicators to design quality control strategies and an intelligent treatment plan recommendation engine map, establishing a big data analysis platform to achieve multi-dimensional, visualized data statistics for management decision support.
Results:
The CDSS system is embedded and interfaced with the physician workstation, triggering in real-time during the clinical diagnosis and treatment process. It establishes a three-tier assessment card control for patient admission and pre-discharge reminders, pop-up windows, and screen domination operations. Based on the intelligent diagnostic and treatment reminders of the CDSS, patients are given intervention treatments. The implementation of this system improves the efficiency of risk assessment and the diagnosis rate of CVD, enhancing the standardization of clinical diagnosis and treatment. Conclusion: This study establishes a specialized knowledge base for cardiovascular diseases, combined with clinical multimodal information, to intelligently assess and stratify cardiovascular patients. It intelligently recommends intervention treatments based on assessments and clinical characterizations, proving to be an effective exploration in utilizing CDSS to build a specialized and disease-specific intelligent system.
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Copyright
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