Semantically Interoperable XML Data

Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurç, Tahsin; Saltz, Joel

doi:10.1142/s1793351x13500037

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…An additional ontological layer is needed, which, in our example, is provided by the openEHR archetype models. The data schemas are derived from underlying archetype models, that provide the rich metadata models, common elements and linkage to standard terminologies which are missing in an XML/XSD only approach [50]. Naturally, for true semantic interoperability it is necessary that the clinical models are harmonized across institutional and regional boundaries as well as across all health professions [51].…”

Section: Discussionmentioning

confidence: 99%

Open data models for smart health interconnected applications: the example of openEHR

Demski

Garde²,

Hildebrand

2016

BMC Med Inform Decis Mak

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BackgroundSmart Health is known as a concept that enhances networking, intelligent data processing and combining patient data with other parameters. Open data models can play an important role in creating a framework for providing interoperable data services that support the development of innovative Smart Health applications profiting from data fusion and sharing.MethodsThis article describes a model-driven engineering approach based on standardized clinical information models and explores its application for the development of interoperable electronic health record systems. The following possible model-driven procedures were considered: provision of data schemes for data exchange, automated generation of artefacts for application development and native platforms that directly execute the models. The applicability of the approach in practice was examined using the openEHR framework as an example.ResultsA comprehensive infrastructure for model-driven engineering of electronic health records is presented using the example of the openEHR framework. It is shown that data schema definitions to be used in common practice software development processes can be derived from domain models. The capabilities for automatic creation of implementation artefacts (e.g., data entry forms) are demonstrated. Complementary programming libraries and frameworks that foster the use of open data models are introduced. Several compatible health data platforms are listed. They provide standard based interfaces for interconnecting with further applications.ConclusionOpen data models help build a framework for interoperable data services that support the development of innovative Smart Health applications. Related tools for model-driven application development foster semantic interoperability and interconnected innovative applications.

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Section: Discussionmentioning

confidence: 99%

Open data models for smart health interconnected applications: the example of openEHR

Demski

Garde²,

Hildebrand

2016

BMC Med Inform Decis Mak

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“…Since the DEMS includes a large number of public institutions, private institutions, NGOs and all citizens in its organisational structure, and since it needs to integrate, analyse and disseminate information from many different sources, including citizens, especially during a disaster, it needs syntactic interoperability at a very high level sectorally. Today, some of the barriers to syntactic interoperability have been overcome through the use of SOA technologies and information encoding in XML format (Niedermayr et al, 2013). In addition, much current research is focused on achieving syntactic interoperability by standardising the data exchanged between heterogeneous devices.…”

Section: Determining the Methodology Of Interoperability Modelmentioning

confidence: 99%

Development of Interoperability Principles for Disaster and Emergency Management System of Türkiye

DEMİR ÖZBEK,

YOMRALIOĞLU,

ATEŞ AYDAR

2023

International Journal of Environment and Geoinformatics

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Due to their complex nature, disasters and emergencies require a data-intensive management system in which many actors from different sectors participate and simultaneous processes are managed. Due to this complexity, there are problems in managing and sharing process services and geographic data effectively. The key to solving these problems is the introduction of a complete interoperability model. In this context, interoperability models based on existing international standards are analyzed in this study. FEI and EIF models are taken as basis in terms of disaster and emergency management interoperability requirements. Accordingly, the interoperability reference model was created in legal, organizational, semantic and technical frameworks to meet the interoperability levels. This general framework model has been evaluated in the service and data layers that need to be fully defined in disaster and emergency management phases, and a basic model including interoperability solutions for the service and data model has been created.

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“…From a scientific perspective, digitally acquired measurements are still often documented by hand and transferred again by hand from this documentation into special, digital study systems for research ( 4 ). In addition to the poor data transmission, another major problem can be identified: there is no uniform designation for the measured values for data exchange (semantic interoperability), as the vast majority of manufacturers only use proprietary designations for the parameters, but there are also no interoperability standards for many of these measured values (e.g., SNOMED-CT or LOINC codes) ( 5 , 6 ). Thus, despite a very high standard of technology in medical technology, there is a relevant gap in the automation of data collection and structured storage, which represents a considerable impairment of treatment quality and a clear hurdle for modern IT procedures in health care and research ( 7 , 8 ).…”

Section: Introductionmentioning

confidence: 99%

Smart hospital: achieving interoperability and raw data collection from medical devices in clinical routine

Martens,

Haase,

Mastella

et al. 2024

Front. Digit. Health

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IntroductionToday, modern technology is used to diagnose and treat cardiovascular disease. These medical devices provide exact measures and raw data such as imaging data or biosignals. So far, the Broad Integration of These Health Data into Hospital Information Technology Structures—Especially in Germany—is Lacking, and if data integration takes place, only non-Evaluable Findings are Usually Integrated into the Hospital Information Technology Structures. A Comprehensive Integration of raw Data and Structured Medical Information has not yet Been Established. The aim of this project was to design and implement an interoperable database (cardio-vascular-information-system, CVIS) for the automated integration of al medical device data (parameters and raw data) in cardio-vascular medicine.MethodsThe CVIS serves as a data integration and preparation system at the interface between the various devices and the hospital IT infrastructure. In our project, we were able to establish a database with integration of proprietary device interfaces, which could be integrated into the electronic health record (EHR) with various HL7 and web interfaces.ResultsIn the period between 1.7.2020 and 30.6.2022, the data integrated into this database were evaluated. During this time, 114,858 patients were automatically included in the database and medical data of 50,295 of them were entered. For technical examinations, more than 4.5 million readings (an average of 28.5 per examination) and 684,696 image data and raw signals (28,935 ECG files, 655,761 structured reports, 91,113 x-ray objects, 559,648 ultrasound objects in 54 different examination types, 5,000 endoscopy objects) were integrated into the database. Over 10.2 million bidirectional HL7 messages (approximately 14,000/day) were successfully processed. 98,458 documents were transferred to the central document management system, 55,154 materials (average 7.77 per order) were recorded and stored in the database, 21,196 diagnoses and 50,353 services/OPS were recorded and transferred. On average, 3.3 examinations per patient were recorded; in addition, there are an average of 13 laboratory examinations.DiscussionFully automated data integration from medical devices including the raw data is feasible and already creates a comprehensive database for multimodal modern analysis approaches in a short time. This is the basis for national and international projects by extracting research data using FHIR.

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Semantically Interoperable XML Data

Cited by 7 publications

References 12 publications

Open data models for smart health interconnected applications: the example of openEHR

Open data models for smart health interconnected applications: the example of openEHR

Development of Interoperability Principles for Disaster and Emergency Management System of Türkiye

Smart hospital: achieving interoperability and raw data collection from medical devices in clinical routine

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