Search results for "SNOMED": 314 articles
West Coast Informatics appointed Regis Charlot, an expert in healthcare data standardization and semantic interoperability, to its board of directors. This move responds to the growing demand for clinical data infrastructure capable of supporting artificial intelligence applications, highlighting the industry's increasing focus on data structuring.
This article compares FHIR and custom REST APIs regarding their roles in exchanging risk adjustment data within the healthcare sector. It explains that APIs are foundational to system integration, enabling real-time data exchange and moving beyond traditional batch processing or manual workflows.
ardle.orgは、FHIRのコードシステムやバリューセットなどの用語データを共有するための新しいオープン仕様「FTRM」（FHIR Terminology Container）を発表した。これは単一のSQLiteデータベースファイルとして、移植性と利用可能性を確保する。
HL7 Chile has published the structured data definition for the 'Condition' resource within its FHIR-based Chilean Clinical Summary of Patient. This guide details how to map condition information with standard vocabularies like SNOMED CT and LOINC, establishing rules for handling disease information in electronic health records.
A Taiwanese health information organization has published a ValueSet for cancer staging. This set is based on HL7 FHIR R4, aggregating codes from multiple standards like SNOMED CT, covering numerous international cancer staging systems (e.g., Gleason score, FIGO stage).
West Coast Informatics announced the launch of AutomapAI™, a platform that transforms fragmented clinical data into standards-aligned, AI-ready assets. The tool automatically performs semantic normalization on local codes and unstructured text, enabling healthcare organizations to reduce integration costs and establish advanced analytical foundations.
As the European Health Data Space (EHDS) approaches, the article raises a concern that mere system connection or compliance with standards is insufficient. The true challenge lies in transforming data into information that is 'readable,' 'understandable,' and 'actionable.'
The 'FHIR-A-THON' event brings together national and regional healthcare institutions and software companies to validate FHIR profiles within the Belgian eHealth ecosystem. The goal is to prepare for the transition towards FHIR and integrate key services like digital referral prescriptions, aligning with the European Health Data Space (EHDS).
A terminology server is a dedicated FHIR service that hosts code systems and value sets, exposing standardized APIs. This allows applications to resolve, validate, and look up clinical terminology at runtime.
This article details an architecture for utilizing Azure Health Data Services to convert and integrate data from legacy HL7v2 systems and various sources into FHIR R4. This enables complex medical data exchange and analysis.
Digital health platforms must be built not merely as applications that export FHIR messages, but from the ground up with structured, standards-based healthcare exchange in mind. For the UK's NHS environment specifically, understanding base FHIR alongside UK Core and individual guidelines is crucial, emphasizing design based on clinical meaning.
Healthcare organizations face challenges due to inconsistent terminology used for diagnoses and tests. A FHIR Terminology Server addresses this interoperability hurdle by providing standardized vocabularies like SNOMED CT and LOINC through a single API.
The Finnish Health Data Hackathon focused on standardizing FHIR-based care plans and computable guidelines (CPG). Key discussions covered international challenges and technical approaches, such as utilizing CRMI for knowledge sharing and modeling ValueSets for concept definition.
The Ayushman Bharat Digital Mission (ABDM), driven by the Indian government, has established a national digital health infrastructure. It enables secure health data exchange based on patient consent, requiring healthcare facilities to integrate using standards like FHIR R4.
SNOMED CT is the world's largest clinical terminology set, systematically representing diseases, findings, procedures, etc. By combining it with structural standards like FHIR, it supports international interoperability and enables unified semantic representation of clinical data.
This service provides synthetic (dummy) patient data conforming to multiple standards, including FHIR R4 and HL7 v2.4. This enables the testing and integration validation of Electronic Health Record (EHR) systems and interface engines without accessing real patient information.
員榮医療社団法人（員榮醫院）が自主開発した「智能醫囑系統」（APOE）が、衛福部主催の「臺灣50優良 SMART 應用程式」に選出された。本システムはAI技術を活用し、電子病歴作成や診療支援を行うことで、医師が患者との対話により多くの時間を割ける環境を提供することを目的としている。
FHIR統合開発者は、実環境（病院）へのアクセスが困難な「キャッチ・22」に直面する。本記事は、単なる構造検証を超え、臨床的に深いデータを扱うことで、より現実的なテスト戦略を提案している。
The author, working on health data interoperability in Indonesia, implemented an AI agent to automate the creation of complex FHIR templates. This significantly reduces the time required for building massive JSON templates (up to 320 resources), which previously took several days or even a week.
This article explains how to prevent 'hallucinations'—plausible but incorrect outputs—from Large Language Models (LLMs). It introduces a method using FHIR Terminology Services and the Model Context Protocol (MCP) to ground AI output in internationally standardized vocabularies, thereby enhancing the accuracy and reliability of clinical coding.