3,748 FHIR-related articles (latest first)
The National Health Authority (NHA) has upgraded the app 'Aarogya Setu 2.0' using Googleโs open-source AI model, Gemma 4, and its Medical Data Toolkit. This allows users to automatically generate comprehensive, standardized health record profiles from unstructured medical reports.
Healthcare data is siloed and difficult to utilize. This article compares and introduces key healthcare analytics platforms required in 2026, which feature FHIR interoperability and advanced AI capabilities.
Many healthcare AI deployments fail not due to model flaws, but because of architectural shortcomings. Key issues include 'integration debt' and 'data drift,' which arise when multiple tools read from and write to the same data.
API Evangelist discusses the Cerner Millennium (now Oracle Health) FHIR R4 API, arguing it must be understood as a sequence of workflows rather than just a list of resources. The article details three specific workflows, including patient retrieval and clinical data write-back.
HL7 Europe has released the mapping specification for the 'Address model,' based on FHIR standards. This is provided as a technical reference within the Europe Base and Core FHIR IG, useful for developers.
AnyBio and Medplum launched a joint reference architecture for biosignal-driven care programs. By integrating wearable device data with clinical workflows using the FHIR Observation resource, they significantly reduce deployment time from months to weeks.
Data engineering firm Sonra has released 'Flexter,' a free online tool designed to make legacy enterprise XML data usable by modern analytics platforms. The tool automatically converts complex, hierarchical XML files into flat structures like CSV, aiding in the utilization of historical data.
The National Health Authority (NHA) of India has integrated Google's AI capabilities into the new 'Aarogya Setu 2.0' application. This allows users to create comprehensive digital health profiles by processing unstructured medical records.
Google has integrated its Gemma 4 AI models and Medical Data Toolkit into the Aarogya Setu 2.0 app. This enables the extraction of crucial clinical information from unstructured medical records and converts it into the FHIR standard format, thereby enhancing interoperability in India's digital health sector.
Canvas MedicalใฏใๅใชใFHIRใชใฝใผในๆไพใซ็ใพใใชใใๅฎ้ใฎ่จๅบใฏใผใฏใใญใผใ้่ฆใใAPI่จญ่จใฎ้่ฆๆงใๆๆใSMART on FHIRใๆฃ่ ็ป้ฒใปๆ่ฌใชใฉใๅ ทไฝ็ใชไธ้ฃใฎๆตใ๏ผใทใผใฑใณใน๏ผใจใใฆEHR้ฃๆบใๆใใในใใจๆ่จใใฆใใใ
FHIR Profiles define how base FHIR resources (like Patient, Observation) should be used in specific contexts or according to regional requirements. This process eliminates data exchange ambiguity and achieves reliable, testable interoperability.
In data exchange, using 'FHIR Documents' is recommended when a complete clinical context is needed, while utilizing 'FHIR APIs' is necessary for accessing specific, real-time operational data. Since the two have different purposes and technical characteristics, proper differentiation of use is required.
A FHIR Bundle groups multiple related healthcare resources (like patient data, lab results, etc.) into one structured package. This allows data to move safely and consistently between systems while preserving context.
This article explains that the FHIR (Fast Healthcare Interoperability Resources) server serves as the backbone for structured data exchange between various healthcare systems. It details how multiple components, such as API Gateways and Authorization Servers, collaborate to ensure security and reliability.
bonfireDB has announced a suite of development tools designed to extract and format only the necessary information from large FHIR records or search queries, optimizing them for AI agent input. This capability prevents context window overflow and enables data utilization based on clinical context.
InterSystems presented a video detailing how to combine HL7 FHIR and Agentic AI to build intelligent healthcare applications that operate directly on structured clinical data. The presentation covered specific technical elements, including the architecture of AI agents and the importance of terminology normalization.
The National Health Authority (NHA) integrated Google's AI capabilities, including Gemma 4 and the Medical Data Toolkit, into the Aarogya Setu 2.0 app. This allows users to process unstructured medical records, build digital health profiles, and enhance interoperability.
The National Health Authority (NHA) has incorporated Google's AI technology into the 'Aarogya Setu 2.0' application. This enhancement allows users to create detailed digital health profiles by analyzing unstructured medical records, thereby supporting electronic health record sharing and interoperability across India.
Google utilized the AI model 'Gemma 4' and a 'Medical Data Toolkit' to enhance the 'Aarogya Setu 2.0' application. This enhancement allows for the generation of digital health profiles from unstructured medical records.
Indiaโs National Health Authority (NHA) launched the new 'Aarogya Setu 2.0' app. Utilizing Google's open Medical Data Toolkit and Gemma 4 models, the app processes and structures complex medical records, thereby strengthening the digital public infrastructure.