Clinical Data Sharing and Integration Model for Precision Anticoagulation Therapy
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Precision anticoagulation therapy requires individualized treatment strategies to optimize therapeutic efficacy while minimizing adverse events such as bleeding or thrombosis. The increasing availability of clinical, genomic, and pharmacological data offers unprecedented opportunities for personalizing anticoagulant dosing, yet these datasets often remain siloed across disparate healthcare systems and research platforms. This study proposes a Clinical Data Sharing and Integration Model (CDSIM) designed to facilitate secure, interoperable, and standardized data exchange to support evidence-based precision anticoagulation. The CDSIM framework integrates real-time electronic health record (EHR) data, laboratory results, pharmacogenomic profiles, medication histories, and patient-reported outcomes through a combination of standardized data formats, application programming interfaces (APIs), and secure cloud-based repositories. It employs advanced data harmonization protocols and ontological mapping to ensure semantic interoperability across different clinical sources. Embedded machine learning algorithms analyze aggregated datasets to predict optimal anticoagulant regimens, dosage adjustments, and monitoring intervals tailored to individual patient profiles. The model incorporates privacy-preserving technologies, including data encryption, role-based access control, and federated learning approaches, to protect sensitive patient information while enabling collaborative research and clinical decision support. Pilot implementation in a multicenter cohort demonstrated improved dosing accuracy, reduced incidence of adverse events, and increased clinician confidence in therapy decisions. Moreover, the CDSIM facilitated multicenter clinical trials by enabling real-time, cross-institutional data aggregation and analysis, accelerating the validation of novel dosing algorithms. This study underscores the potential of a unified data sharing and integration framework to enhance the precision, safety, and efficiency of anticoagulation therapy. Future work will focus on large-scale deployment, integration with national health information exchanges, and continuous model refinement through feedback loops and adaptive learning. The proposed CDSIM provides a scalable pathway for leveraging heterogeneous clinical data sources to advance personalized medicine in anticoagulation and serves as a blueprint for similar initiatives in other therapeutic areas.
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