1,676 FHIR-related articles (latest first)
Users on Google Skills are sharing their experiences and evaluations regarding the topic 'Ingesting FHIR Data with the Healthcare API'. Multiple users posted reviews at various times, such as four months ago, noting that the process requires time for troubleshooting and internalization.
Google Cloud provides technical reference information on utilizing the Cloud Healthcare API to handle multiple standards, such as converting HL7v2 to FHIR and converting FHIR to OMOP. This allows EHR data and research data to be utilized in a unified format.
This article details how to build a semantic search-enabled vector repository for FHIR healthcare data using InterSystems IRIS as the foundation, leveraging Python libraries like IRIStool. It explains creating schemas for various FHIR resources (e.g., Patient) and storing embedding vectors to enable advanced natural language querying.
A review page on Google Skills introduces a method for streaming the conversion of HL7 data into FHIR format, utilizing Dataflow and the Healthcare API. This technique provides a concrete approach for migrating legacy medical data (HL7) to modern standard specifications (FHIR).
A FHIR ValueSet defining standard concepts for specific biopsy results. This ValueSet references SNOMED CT and includes codes such as 'Biopsy result normal (finding)' and 'Normal histology findings (finding)', representing normal pathological findings.
Dicom Systems offers the 'Unifier Medical Image Workflow Cloud Appliance,' strengthening data connectivity between medical imaging systems (PACS/MIMPS) and Electronic Health Records (EHR/EMR). The product supports major protocols like DICOM, HL7, and FHIR, achieving overall workflow automation and enhanced security.
AirGate Technologies offers an integrated data analytics solution combining Synapse Analytics and FHIR Store. This enables healthcare providers and payers to analyze data from multiple sources, aiming for personalized care delivery and cost reduction.
TCS offers a consulting service utilizing Microsoft Azure Health Data Services to solve data interoperability challenges in healthcare organizations. The service provides comprehensive assessment and PoV development based on the FHIR standard, including data transformation from various formats like HL7v2 and CDA.
HL7 released global survey results from 2025, revealing key drivers and barriers to FHIR adoption. Diagnostic orders and reports emerged as the leading use case, with broad adoption by EHR vendors and care providers. However, significant challenges persist, including lack of knowledge and unclear benefits.
AWS HealthLake allows users to efficiently import FHIR data stored in Amazon S3, making it available for analysis. The service supports setting various validation levels (strict, structure-only, minimal) to ensure data integrity during the process.
According to an HL7 survey, the adoption of FHIR is primarily driven by innovation and regulatory requirements. Diagnostic orders and reports are emerging as leading use cases, surpassing document exchange and referrals. While broad industry engagement is evident, knowledge gaps and regulatory uncertainties remain significant challenges.
AWS HealthLake utilizes the FHIR Bundle structure for handling resources. This feature distinguishes between two processing modes: 'Batch' mode, which processes independent data groups, and 'Transaction' mode, ensuring all operations succeed or fail together.
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FHIR (Fast Healthcare Interoperability Resources) is the modern global standard for exchanging healthcare data. This article explains how FHIR enables information sharing across different systems, solving challenges in clinical practice and research. It highlights that regulatory bodies worldwide are mandating its adoption, establishing it as a foundation.
Cambridge Prisms reports on the i-HD Task Force's initiative to scale up eSource use in oncology clinical trials. This effort aims to solve challenges related to EHR-to-EDC data migration, KPI tracking, and overall data collection efficiency.
Developed by HL7, FHIR is a standard for electronic health information exchange. This article explains FHIR's mechanism and its business impact, demonstrating how it contributes to seamless data sharing and improved operational efficiency across diverse systems.
Global Health Connector held a Connectathon focusing on Electronic Product Information (ePI) and International Patient Summaries (IPS) as FHIR documents. The event aimed to test the exchange and display of this information in increasingly complex healthcare and life scenarios.
The global healthcare interoperability solutions market is projected to grow from US$3.0 billion in 2021 to US$6.2 billion by 2027. This growth is driven primarily by the need for seamless data exchange due to the adoption of EHRs and remote monitoring, alongside government mandates for standardization and interoperability across various countries.
While interoperability standards like HL7 and FHIR have enabled the exchange of patient data across various systems (EHRs, labs), this connectivity alone is insufficient. Artificial intelligence is necessary to provide 'intelligence' to the data, transforming raw information into real-time, actionable insights.
HAPI FHIR provides an optional 'HFQL Module' allowing the execution of SQL-like queries against a FHIR repository. Intended for analytical purposes, developers can connect directly to the server via a JDBC driver to perform complex data searches.