Search results for "SNOMED": 314 articles
This article addresses the challenges posed by siloed medical data, explaining how the standard FHIR (Fast Healthcare Interoperability Resources) serves as a solution. FHIR coexists with other standards like SNOMED CT and IHE to achieve efficient data exchange through integration with AI.
The HL7 Patient Care WG Steward has published a ValueSet defining Restless Leg Syndrome. This set includes multiple standard codes from SNOMED CT and ICD-10-CM, aiding in the definition and classification of the disorder.
This article addresses the challenges of data modeling in Electronic Health Records (EHRs), explaining that openEHR offers superior flexibility and extensibility. By comparing it with the data exchange standard FHIR, it argues that openEHR is superior for long-term clinical data management and adapting to new concepts.
The Fraunhofer Institute, leading the 'FHIR-Starter' research project, has begun efforts to automatically structure unstructured medical text using LLMs and NLP. This aims to convert non-structured data, such as PDF clinical reports, into standardized formats, thereby improving the efficiency of utilizing healthcare data.
The Fraunhofer-Institut iESE is developing a system that uses LLMs and NLP to automatically extract data from unstructured medical documents (like PDFs) and convert it into standardized formats such as FHIR, LOINC, and SNOMED-CT. This is expected to significantly improve the efficiency of electronic health record data processing.
IHE has published a new resource profile called 'Occupational Data for Health (ODH)' based on the FHIR standard. This provides a data structure to link individual occupational information with health status, enabling the handling of detailed job histories in medical records.
The Fraunhofer research project 'FHIR-Starter' is developing a software service that automatically analyzes and structures unstructured medical texts using LLMs and NLP. This aims to solve key challenges in utilizing medical data within Germany.
The 'FHIR-Starter' project, led by Fraunhofer IESE, aims for the automated structuring of medical data using LLMs and NLP. It seeks to utilize non-structured data scattered across Germany's electronic patient records (ePA) in FHIR format, significantly improving efficiency for clinical care and research.
The Fraunhofer Institute (Fraunhofer IESE) launched the 'FHIR-Starter' project, collaborating with a Berlin hospital and an AI company. The goal is to automatically structure electronic patient record data, which is currently predominantly unstructured.
At HIMSS25, Health Samurai will showcase solutions for various healthcare challenges, including data interoperability and AI utilization. Attendees can learn how a FHIR-based platform contributes to accelerating clinical trials, optimizing workflows, and enabling international medical collaboration through live demos at the booth.
The Fraunhofer IESE is developing 'FHIR-Starter,' a research project that uses AI and Natural Language Processing (NLP) to automatically convert unstructured medical documents, such as PDFs, into standardized data formats. The goal is to improve the efficient use and utilization of electronic health records and clinical notes.
The FHIR-Starter project, led by Fraunhofer IESE, is developing a software service that uses LLMs and NLP to extract medical information from unstructured electronic records (like PDFs) and convert it into standardized data formats. This aims to solve challenges in German healthcare, such as manual data entry and difficulty comparing historical patient findings, ultimately striving for the complete digitalization of the ePA.
Fraunhofer Institute launched the 'FHIR-Starter' project to automatically extract and standardize data from free-text medical documents (like PDFs) using Large Language Models (LLMs) and Natural Language Processing (NLP). This aims to solve key challenges in German healthcare data utilization.
SeeYouDoc is developing a Proof-of-Concept (PoC) for a Referral Tracking System (RTS) using HL7 FHIR, leveraging insights from the Digital Health Interoperability Bootcamp. This guide details the technical steps required to integrate Electronic Medical Records (EMRs) and push referral data into the RTS.
The Fraunhofer Institute (IESE) is developing a project that uses Large Language Models (LLMs) and Natural Language Processing (NLP) to automatically convert unstructured full-text medical documents, such as doctor's notes, into standardized data formats. This aims to solve issues of manual data entry and difficulty in research utilization within the German healthcare system.
The 'FHIR-Starter' project, led by Fraunhofer IESE, aims to automatically structure unstructured medical texts using LLMs and NLP. This facilitates data sharing and research utilization, helping to reduce the burden on physicians and strengthening German research capabilities.
The article discusses the utilization of FHIR Terminology Services to ensure consistent representation of clinical data.
The 'FHIR-Starter' project, led by Fraunhofer IESE, is developing a software service that automatically analyzes unstructured medical documents using LLMs and NLP, converting them into standardized data formats. This aims to promote the utilization of medical data and digitalize electronic health records.
Telstra Health is advancing efforts to integrate and share data across Australian legacy systems using standards like FHIR for connected care. This aims to make a patient's complete history accessible in real-time across multiple healthcare settings, ensuring high-quality continuous care.
A ValueSet defining concepts related to specific immunotherapies has been published. This set includes procedure codes such as biological response modifier therapy and various immunotherapy procedures, aiding standardization in healthcare information exchange.