Search results for "LOINC": 168 articles
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.
VSAC (NIH) provides an integrated interface for managing multiple code systems, including UMLS and CDT. The page introduces methods of linking with various data exchange standards, such as FHIR R4, and features detailed code search and definition capabilities.
The author loaded the same dataset (1,000 Synthea patients) into HAPI FHIR and Google Cloud Healthcare API and found that both servers returned different responses across multiple queries. Significant interoperability issues were identified, particularly concerning data ingestion and transaction handling.
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'βplausible but incorrect outputsβfrom 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.
Successful FHIR implementation requires a combination of healthcare domain knowledge, interoperability engineering, and system-level architecture expertise. It is not merely an interface development task but aims to build a governed interoperability layer supporting clinical workflows and regulatory compliance.
The article addresses complex data integration challenges in healthcare, advocating a 'contracts-first' approach. It aims to reduce operational risk and build reliable pipelines by implementing data quality assurance (DQ) and schema evolution management.
This article details how to build advanced, compliant medical AI applications by integrating the Fast Healthcare Interoperability Resources (FHIR) standard with the large language model MedGemma. It provides specific code examples and data formatting procedures for structuring patient information, medical history, and lab results into usable prompts.
AI tools can automate repetitive mapping tasks and detect errors in complex HL7/FHIR integration projects, significantly reducing development time. However, the article stresses that human expertise remains essential for clinical judgment and system-level design.
While data exchange has advanced, true healthcare system interoperability requires consistently interpreting the 'meaning' of data. FHIR plays a crucial role not only by enabling structured data exchange but also by facilitating semantic interoperability through standardized coding and terminology.
Healthcare institutions can eliminate data silos and improve clinical visibility by connecting medical devices with EHRs and analytics platforms. This integration, utilizing standards like HL7 and FHIR, prevents manual entry errors and forms the foundation for AI-driven predictive healthcare systems.
Shen AI is a measurement technology that captures physiological signals from a camera and outputs structured vital signs like heart rate and blood pressure. This SDK provides stable JSON output, enabling integration into existing systems without managing data persistence or clinical workflow orchestration, thus simplifying FHIR mapping.
This page introduces the search and management functionality for code systems, combining large medical knowledge bases like UMLS (Unified Medical Language System) with standards such as FHIR (Fast Healthcare Interoperability Resources). Users can systematically reference various types of codes and terms to acquire necessary information for building Value Sets.
This article addresses the problem of 'Interface Drift' in healthcare data flows, explaining an AI-driven approach for continuous monitoring and self-remediation. This capability detects semantic failures caused by changes in EHRs or interconnected systems, enabling safe and auditable corrections.
Researchers emphasize that systematic data modeling is crucial for efficient, future-proof healthcare information sharing. They recommend that while HL7 FHIR excels in data exchange and interoperability, the appropriate standard must be chosen based on specific goals, such as analysis or organizational structure.
This article explains the versioning process for FHIR Questionnaires, emphasizing its critical role in maintaining data integrity. Since Questionnaires are used to collect structured healthcare data, proper version control is essential for tracking changes and ensuring compatibility.
NCQA PHEMUR has published a ValueSet for the PROMIS-29 Depression Score. This resource includes LOINC codes 71965-8 and 71966-6, enabling standardized data exchange in electronic health records.
This article provides a comprehensive technical guide covering various Fast Healthcare Interoperability Resources (FHIR) resources. It details the structure and operational methods required for handling specific clinical data, such as patient information and medical records.
Developed by HL7, FHIR standardizes fragmented healthcare data using a resource-based structure and RESTful APIs. This enables real-time sharing and analysis of data by leveraging global standards like SNOMED CT and LOINC.
Agentic AI refers to systems that autonomously take action toward a goal, going beyond simple question answering. To realize this advanced capability, standardized data access and mechanisms for action provided by FHIR are essential.