Solving Healthcare Data Exchange Challenges using FHIR Standards: Partnership between Infor Cloverleaf and Health-Comm
Interopérabilité vs Intégration : Une Révolution pour la Santé Numérique - DSIH
Summary
Infor Cloverleaf and Health-Comm aim to industrialize and secure data exchange in France using the FHIR standard. They offer a comprehensive solution based on open standards to healthcare institutions facing complex medical data handling, paving the way for more personalized and high-performing medicine.
Details
As healthcare facilities face increasingly complex data flows, distinguishing between 'integration' and 'interoperability' is crucial. Traditional system connections link hospital information systems via specific interfaces (such as HL7 v2 or CDA), depending on each system's unique specifications, leading to high costs and complex maintenance. In contrast, this partnership proposes a revolutionary approach: 'industrial interoperability.' This does not merely connect systems but guarantees mutual understanding of the data itself based on open standards like FHIR. To solve this challenge, Infor Cloverleaf and Health-Comm provide a comprehensive FHIR suite consisting of a FHIR Bridge, a FHIR data store, and an industrial iPaaS platform (Infor OS). This enables 150 types of resources (Patient, Practitioner, Diagnosis, etc.), each with 400 fields, resulting in 60,000 standardized translations. It can also generate bundles tailored to specific use cases. This interoperability offers three major benefits to healthcare institutions: First, medical professionals gain real-time access to standardized and usable data. Second, a comprehensive dataset using the FHIR model supports clinical and academic research (including models like OMOP or CDISC). Third, access to Real-World Data (RWD) for CROs conducting observational studies is simplified. This initiative promotes the standardization and urbanization of medical data in France, aiming to reduce technical debt and technological dependency. It represents a critical step toward achieving data-driven personalized medicine.
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