This clinical research addresses the challenge of integrating and harmonizing data from multiple biosensing platforms and health systems. Patients often use multiple devices and receive care across different institutions, requiring data harmonization to enable comprehensive health insights.
Different biosensing platforms use different sensors, sampling rates, calibrations, and data formats. Electronic health records from different healthcare systems vary in structure and terminology. Harmonizing diverse data sources while preserving clinical accuracy is complex.
We developed a flexible framework that maps diverse data sources to standardized clinical concepts. The framework includes automated data quality assessment, outlier detection, and sensor-specific calibration corrections.
Integrated multi-platform data achieved 94% concordance with validated reference measurements. Comprehensive data from multiple sources improved prediction accuracy by 15% compared to single-platform approaches.
Proper data harmonization enables clinical insights from diverse data sources.