Search results for "LOINC": 21 articles
In Germany, multiple institutional frameworks (such as ISiK and MIOs), led by bodies like gematik and KBV, are advancing FHIR R4 adoption. This is now compounded by the EU's EHDS regulation, creating a complex interplay between national laws and international interoperability requirements.
The DGBMT (German Society for Biomedical Engineering) recommends that hospitals leverage their purchasing power to establish 'interoperability' as a mandatory criterion when procuring medical technology and IT systems. This change can be achieved by integrating it into the procurement process itself, rather than solely relying on new legislation.
The German Association for Biomedical Engineering (VDE DGBMT) has called for the mandatory inclusion of 'interoperability' as a core procurement criterion when purchasing medical technology. The paper suggests that hospitals can leverage their market power and existing international standards to enforce compliance without needing new legislation.
The Medical Informatics Initiative (MII) conducted a large-scale, distributed analysis using data from various university hospitals to detect risks associated with polypharmacy. Researchers developed an ETL pipeline based on HL7 FHIR standards and employed decentralized statistical methods to integrate and analyze vast clinical datasets from multiple institutions.
Smile Digital Health's FHIR® platform, Smile CDR, enables structured management of clinical data in hospitals. Through nursIT, it integrates standards like HL7 v2 and SNOMED CT to provide a central data platform with high interoperability.
Dr. Viola Henke of DMI points out the financial difficulties and structural challenges facing German hospitals. She states that utilizing new funding mechanisms (KHTF) requires an integrated approach to digitalization, focusing on interoperability and data strategy, rather than mere system implementation.
The PanCareSurPass Project published a JSON representation of clinical records (Observation) using the HL7 FHIR standard, detailing specific diagnoses (C41.9). This serves as a concrete implementation example for data exchange in clinical settings.
Annett Müller of DVMD points out the definition of 'smart data' in modern medical information management and the challenges of its utilization. She emphasizes that obtaining valuable insights requires both data completeness and interoperability.
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.
BIHのシンヴィア・トゥーン教授は、デジタル変革がもたらす可能性と課題について語った。技術的な進歩に加え、法制度や標準規格の「強制力」が必要であり、国際的な成功事例から学ぶべきだと指摘した。
This page presents detailed specifications for coding medical information and data elements related to Digital Health Applications (DiGA). It specifically defines how data should be handled using standard code systems like SNOMED CT and LOINC, and details the mapping process to FHIR.
A lab medicine expert points out the difficulty of integrating test data into electronic patient records (ePA). Although HL7 and FHIR are used in practice, true unified digitalization requires deep semantic standardization using standards like LOINC and SNOMED CT.
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 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 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.
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.