3,748 FHIR-related articles (latest first)
This documentation provides technical procedures and code samples for patching (updating) FHIR resources using the Google Cloud Healthcare API. Developers can manage resource state by utilizing `application/json-patch+json` Content-Type to modify specific field values.
Providence and Humana are improving data exchange between payers and providers using HL7 FHIR standards and Da Vinci guides. This initiative aims to improve healthcare quality by automating and standardizing processes that were previously manual and inefficient.
This article explains how 'Observation' data, representing clinical findings, is handled in medical information systems. It details the process of treating patient-linked examination results as FHIR resources for searching and detailed retrieval.
This document describes the structure for a specific FHIR resource that must conform to two identical extensions (cdl-ext-is-emergency). This demonstrates data consistency and rigorous definition in healthcare information exchange.
This article discusses the major challenges facing digital transformation in healthcare, providing specific solutions. It examines the future state of medical systems, focusing particularly on technical aspects such as data exchange and system integration.
Researchers introduced FHIR-AgentBench, a new benchmark to evaluate the performance of Large Language Model (LLM) agents designed for HL7 FHIR standard. This benchmark focuses on complex questions arising in real clinical settings, aiming to surpass the limitations of conventional structured data QA.
Waymark Systems offers comprehensive digital health solutions based on FHIR standards, aiming to improve clinical workflows and patient care. The company ensures interoperability while integrating global clinical terminologies like SNOMED CT and LOINC, thereby eliminating data silos and enabling real-time decision support.
El Salvador's new public digital health platform, DoctorSV, combines Google AI and telemedicine. This enables citizens to receive free medical consultations and diagnoses 24/7, aiming to alleviate the burden on traditional hospitals.
The HealthTree Foundation has expanded its platform's integration network by adding Flatiron Health and CareSpace EHR systems. This update extends the reach to over 7,300 connected treatment centers and health systems across the US, enabling patients to aggregate their own health data.
ITC Infotech has launched an FHIR-based interoperability solution for the healthcare industry. This system utilizes MS Azure FHIR API to collate and interoperate clinical data from multiple disparate systems and standards.
OpenMRS has updated its FHIR2 module, evolving it to version 3.0.0. The primary change involves migrating the Data Access Object (DAO) layer from the Hibernate Criteria API to the more standardized JPA Criteria API. Consequently, developers must utilize a new custom context called `OpenmrsFhirCriteriaContext` for query construction.
Cameron Kracke, CISO at Prime Therapeutics, discusses the security challenges in modern healthcare ecosystems. He points out that 'fragmentation' across diverse elements like hospitals, telehealth, and cloud services prevents cohesive visibility.
As the importance of provider-payer data integration grows, Michael Westover highlights significant challenges in data sharing. He argues that relying on vendors for longitudinal data is complex and inefficient, emphasizing that standardized APIs (like FHIR) and strong partnerships are crucial for achieving value-based care.
The National Resource Center for EHR Standards has published a testing ValueSet for Body Measurement. This is part of the FHIR Implementation Guide for ABDM, contributing to the standardization of data structures in healthcare information exchange.
This systematic review systematically evaluates Common Data Models (CDMs) and data standards necessary to integrate diverse data sources and enable federated analysis. The study concludes that OMOP CDM and FHIR scored best across various criteria, emphasizing that achieving seamless interoperability requires enabling transformations between different representations rather than relying on a single global model.
Master Data Management (MDM) is crucial for integrating siloed healthcare data from systems like EHRs and billing platforms. It establishes a trusted single view of patient, provider, and payer data, enabling true interoperability.
CSIRO's Kate Ebrill et al. emphasized the critical importance of interoperability in digital health. They pointed out that global standards like FHIR and SNOMED CT provide the necessary data foundation to build high-quality, 'sovereign AI.'
Google has published a guide detailing how developers can securely and efficiently connect various IDEs to the Cloud Healthcare API using Model Context Protocol (MCP). This enables LLMs to directly search and manipulate FHIR and DICOM data within healthcare datasets.
Google Cloud's Cloud Healthcare API allows for the batch retrieval of large volumes of HL7v2 messages. This capability resolves issues related to network costs and processing load associated with traditional single-message fetching, thereby improving data integration efficiency.
The modern healthcare ecosystem relies on seamless information exchange from diverse data sources. This article explains 'interoperability standards,' detailing how major specifications like HL7 and FHIR provide a common language for medical data.