Search results for "FHIR": 1,661 articles
On October 24th, Biomedica successfully passed two major international interoperability testsβDICOM SWF and FHIR MHDβat the Asia-Pacific IHE Connectathon 2025 held in Tokyo. This demonstrates that Taiwan has reached global standards in the fields of AI medical imaging and smart health information exchange.
Synthetic EHR offers a platform that accelerates healthcare innovation through strategic partnerships and developer ecosystem collaboration, built on privacy-first principles. By utilizing FHIR-native synthetic data, it removes regulatory barriers and achieves seamless integration via an API-first approach.
HL7 has published the ValueSet 'hl7VSpcaTypeV100' to specify types of Pain Control Analgesia (PCA). This resource can be used for classifying PCA in healthcare, supporting standardized data exchange.
Dr. Gustavo Ferrer explains that AI accelerates drug development and pharmaceutical manufacturing by transforming real-world clinical data into actionable insights. Platforms like Moxie-Link, which are FHIR-compliant, integrate care environments directly into research to reduce timelines and costs.
This service provides an automated tool to convert C-CDA, a clinical document format, into structured FHIR bundles. This resolves the difficulties associated with mapping and manual conversion from traditional XML-based data formats, enabling quick access to standards-compliant data necessary for analytics and system integration.
A new study demonstrated that combining CDISC standards with HL7 FHIR enhances data for Alzheimerβs Disease (ADRD) research. This synergy increases the usability of real-world data from electronic health records, accelerating the bridge between clinical care and research.
Google Cloud provides technical 'Reference Patterns' utilizing the Cloud Healthcare API to convert data, such as HL7v2 messages and FHIR resources, into standard models like OMOP. This allows structured organization of data obtained from Electronic Health Records (EHR).
This tutorial guides researchers and data scientists on how to use Google Cloud's Cloud Healthcare API to remove or modify Personally Identifiable Information (PII) and Protected Health Information (PHI) from FHIR clinical data. This process protects patient privacy while preparing the data for research, data sharing, and machine learning.
FUME provides a real-time data interceptor that captures and transforms healthcare data streams. This ensures immediate compliance with FHIR standards, preventing interruptions in critical workflows like clinical alerts or monitoring updates that rely on rapid data exchange.
This service addresses the difficulty developers and clinical informaticists face when dealing with raw JSON payloads. By using the FHIR Viewer, complex FHIR JSON data is converted into a human-readable format, simplifying validation and sharing.
A new process has been established for clinicians and standards experts. This allows stakeholders to propose changes to the International Patient Summary (IPS) suite of standards, ensuring their continued accuracy and global applicability. Proposed changes can include corrections or improvements to existing standards, scope enhancements, or the addition of entirely new standards.
Outburn has introduced 'FUME,' a proprietary real-time data conversion engine. It enables non-intrusive, bidirectional data exchange between legacy formats like HL7 v2 and the modern FHIR standard.
Outburn has implemented a solution using embedded FUME Community Edition to establish seamless, standardized communication between healthcare organizations and insurance providers. This automates the payment authorization process that previously required patients to wait upon arrival, enabling real-time eligibility checks before medical care.
HL7 has released a code system for 'Religion' in the v2+ format. This CodeSystem includes identifiers for various denominations, such as Atheist, Catholic, and Hindu, helping to standardize faith information within medical data.
This course teaches methods for ingesting and processing data in industry standard formatsβFHIR, HL7v2, and DICOMβusing the Cloud Healthcare API. Furthermore, it includes practical training on building prediction models with FHIR data and de-identifying datasets.
This article introduces a method to eliminate complex authorization logic in FHIR-based applications. By utilizing SMART on FHIR V2 scopes combined with Keycloak's composite roles, it is possible to achieve a system where a single API endpoint dynamically provides different data based on the calling user type, all without custom code.
Firely provides FHIR-based products and services to payers, providers, and digital health innovators, helping solve complex challenges related to sharing and utilizing healthcare data. This aims to enhance the overall connectivity and innovation within the healthcare ecosystem.
HL7 released the ValueSet 'hl7VScalendarAlignmentV100' to specify the alignment between repetition cycles and calendar dates. This helps clearly distinguish between expressions like 'the 5th of every month' and simple period specifications like 'every 30 days.'
This study systematically reviewed various architectural patterns to address the fragmentation and interoperability challenges in Health Information Systems (HIS) during digital health transformation. It found that microservices and distributed ledgers are predominant, with FHIR-based contracts stabilizing interfaces.
As EHRs become universal, poor true interoperability remains a major safety hazard. This article explains how the FHIR standard, based on modern web APIs, enables real-time and secure data exchange compared to traditional HL7 v2 or legacy systems, thereby improving hospital workflows and accelerating SaMD adoption.