[Study on Standardization Methods of Multi-Source Heterogeneous Data from ICU Medical Devices Based on openEHR]

To address the challenges of multi-source heterogeneous data from ICU medical devices in terms of structure, semantics, and standardization, this study explores a standardized modeling approach based on openEHR. The aim is to achieve unified management and efficient utilization of data, providing a solid data foundation for smart healthcare and evidence-based medicine.

This research is grounded in the dual-layer modeling architecture of openEHR. A medical devices data prototype and corresponding templates tailored for the ICU context were designed and constructed, enabling standardized semantic expression and unified encoding of multi-source heterogeneous data. Through multi-protocol standardization mapping, unified acquisition and parsing of device data from different vendors, protocols, and data structures were achieved. Furthermore, a data platform for ICU medical devices was established, facilitating dynamic data collection, real-time standardization processing, and semantic unification.

In a practical application at a tertiary A-grade hospital's ICU, the modeling of 98 prototypes for 8 types of devices (including ventilators and patient monitors) and encapsulated 8 clinical scenario templates were completed. The efficiency of device data integration was significantly improved. The time required for preparing clinical research data was reduced from an average of 4.2 hours to 0.5 hours.

The standardized modeling method based on openEHR effectively addresses the heterogeneity data issues from ICU medical devices. Its open model-driven architecture supports cross-device interoperability and long-term evolution, providing high-quality data foundations. This approach optimizes clinical decision-making processes and enhances data management efficiency, offering a systematic solution for the effective integration and semantic governance of multi-source heterogeneous data.