Data Interoperability Research Articles

Policy framework for integrating data interoperability at public hospitals in South Africa

The implementation of data interoperability in healthcare relies heavily on policy frameworks. However, many hospitals across South Africa are struggling to integrate data interoperability between systems, due to insufficient policy frameworks. There is a notable awareness that existing policies do not provide clear actionable direction for interoperability implementation in hospitals. This study aims to develop a policy framework for integrating data interoperability in public hospitals in Gauteng Province, South Africa. The study employed a conceptual framework grounded in institutional theory, which provided a lens to understand policies for interoperability. This study employed a convergence mixed method research design. Data were collected through an online questionnaire and semi-structured interviews. The study comprised 144 clinical and administrative personnel and 16 managers. Data were analyzed through descriptive and thematic analysis. The results show evidence of coercive isomorphism that public hospitals lack cohesive policies that facilitate data interoperability. Key barriers to establishing policy framework include inadequate funding, ambiguous guidelines, weak governance, and conflicting interests among stakeholders. The study developed a policy to facilitate the integration of data interoperability in hospitals. This study underscores the critical need for the South African government, legislators, practitioners, and policymakers to consult and involve external stakeholders in the policy-making processes.

The Connected Care for Canadians Act: an important step toward interoperability of health data.

The Connected Care for Canadians Act: an important step toward interoperability of health data.

Indicators for assessing the interoperability of health data in Canada.

Indicators for assessing the interoperability of health data in Canada.

FHIR - Overdue Standard for Radiology Data Warehouses.

In radiology, technological progress has led to an enormous increase in data volumes. To effectively use these data during diagnostics or subsequent clinical evaluations, they have to be aggregated at a central location and be meaningfully retrievable in context. Radiology data warehouses undertake this task: they integrate diverse data sources, enable patient-specific and examination-specific evaluations, and thus offer numerous benefits in patient care, education, and clinical research.The international standard Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is particularly suitable for the implementation of such a data warehouse. FHIR allows for easy and fast data access, supports modern web-based frontends, and offers high interoperability due to the integration of medical ontologies such as SNOMED-CT or RadLex. Furthermore, FHIR has a robust data security concept. Because of these properties, FHIR has been selected by the Medical Informatics Initiative (MII) as the data standard for the core data set and is intended to be promoted as an international standard in the European Health Data Space (EHDS).Implementing the FHIR standard in radiology data warehouses is therefore a logical and sensible step towards data-driven medicine. · A data warehouse is essential for data-driven medicine, clinical care, and research purposes.. · Data warehouses enable efficient integration of AI results and structured report templates.. · Fast Healthcare Interoperability Resources (FHIR) is a suitable standard for a data warehouse.. · FHIR provides an interoperable data standard, supported by proven web technologies.. · FHIR improves semantic consistency and facilitates secure data exchange.. · Arnold P, Pinto dos Santos D, Bamberg F et al. FHIR - Overdue Standard for Radiology Data Warehouses. Fortschr Röntgenstr 2024; DOI 10.1055/a-2462-2351.

Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing

In the era of Industry 4.0, Zero Defect Manufacturing (ZDM) has emerged as a prominent strategy for quality improvement, emphasizing data-driven approaches for defect prediction, prevention, and mitigation. The success of ZDM heavily depends on the availability and quality of data typically collected from diverse and heterogeneous sources during production and quality control, presenting challenges in data interoperability. Addressing this, we introduce a novel approach leveraging Asset Administration Shell (AAS) and Large Language Models (LLMs) for creating interoperable information models that incorporate semantic contextual information to enhance the interoperability of data integration in the quality control process. AAS, initiated by German industry stakeholders, shows a significant advancement in information modeling, blending ontology and digital twin concepts for the virtual representation of assets. In this work, we develop a systematic, use-case-driven methodology for AAS-based information modeling. This methodology guides the design and implementation of AAS models, ensuring model properties are presented in a unified structure and reference external standardized vocabularies to maintain consistency across different systems. To automate this referencing process, we propose a novel LLM-based algorithm to semantically search model properties within a standardized vocabulary repository. This algorithm significantly reduces manual intervention in model development. A case study in the injection molding domain demonstrates the practical application of our approach, showcasing the integration and linking of product quality and machine process data with the help of the developed AAS models. Statistical evaluation of our LLM-based semantic search algorithm confirms its efficacy in enhancing data interoperability. This methodology offers a scalable and adaptable solution for various industrial use cases, promoting widespread data interoperability in the context of Industry 4.0.

Data reporting quality and semantic interoperability increase with community-based data elements (CoDEs). Analysis of the open data commons for spinal cord injury (ODC-SCI).

Data interoperability is crucial for effectively combining data for scientific inquiry. To facilitate interoperability, data standards such as a common definition of variables are often developed. The Open Data Commons for Spinal Cord Injury (odc-sci.org) has established an initial set of community-based data elements (CoDEs)-a minimal set of variables for sharing-to promote data interoperability in SCI research, aligning with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. We sought to understand the use of CoDEs by the SCI community to inform current standards adherence and future standards development. We systematically analyzed 39 public datasets in relation to 17 required CoDEs and found variations between reported data and the structure specified by the CoDEs. Overall, we found that the enforcement of data standards improved reporting rates of CoDEs variables. Notably, different variables were found to require different levels of curation to ensure semantic equivalence among datasets. We also uncovered specific reporting habits of researchers such as formatting and naming patterns. A need for different data standards based on the nature of the study (e.g., human study, derivative study) was realized alongside a detailed list of issues that should be addressed when implementing such standards. Among the various approaches to developing data standards, ODC-SCI adopted a semi-formal approach by creating standards that are easy to adopt by the user. Our data-driven evaluation of actual reporting behavior shows that this flexibility can lead to subsequent problems in harmonization. This study serves as a baseline analysis of reporting behaviors for shaping and facilitating data standards.

CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.

Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science. This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process. We employed the "Best fit" framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework. The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices. The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration. The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment.

Survey of IoT and AI applications: future challenges and opportunities in agriculture

The internet of things (IoT) connects physical objects through sensors, software, and communication technologies, enabling efficient data collection and sharing. This interconnection promotes automation, real-time monitoring, and improved decision-making across various sectors. In agriculture, the integration of IoT with artificial intelligence (AI) is revolutionizing resource management by providing farmers with real-time information on crop health, climate conditions, and soil quality. This paper explores how IoT and AI are transforming traditional agricultural practices to enhance both efficiency and sustainability. Through an in-depth analysis of existing literature and practical applications in the sector, this study identifies significant advancements in crop management, reduction of losses, and resource optimization. Additionally, it highlights persistent challenges such as data security and interoperability. The aim is to address these challenges and propose innovative solutions to optimize agricultural processes. The results indicate that while IoT and AI offer substantial benefits, further advancements and solutions are needed to fully leverage these technologies for sustainable agricultural development.

HBIM: Background, Current Trends, and Future Prospects

Historic building information modeling (HBIM) represents an emerging field that extends traditional building information modeling (BIM) to the preservation, management, and analysis of heritage structures. This paper provides a comprehensive overview of HBIM, tracing its evolution from its origins and early applications to its current state and future prospects. The processes of data collection and modeling are thoroughly examined, addressing levels of detail, digitization methods, and commonly used software and data formats. Attention is also given to existing BIM standards and protocols and their potential application to HBIM. The paper emphasizes the importance of appropriate data selection and management, both for geometrical and non-geometrical (historical and architectural) information. Furthermore, it explores the integration of HBIM with structural analysis tools, a subject of growing interest, particularly in light of its potential for integration with structural health monitoring systems and advanced computational models. The results of this review highlight the increasing role of HBIM in heritage preventive preservation and management, a topic that accounted for 40% of the articles on this subject in 2023. These findings demonstrate that HBIM offers significant potential for managing and preserving heritage buildings, but to fully realize its capabilities, advancements in data interoperability, standardized protocols, and real-time structural analysis are essential to make it a widely effective tool in conservation efforts.

AI-driven health data analytics for early detection of infectious diseases: A conceptual exploration of U.S. public health strategies

This review paper explores the role of AI-driven health data analytics in enhancing the early detection and response to infectious diseases within U.S. public health strategies. It begins by outlining the importance of timely detection in controlling outbreaks and discusses current public health systems and their limitations, including passive surveillance and data fragmentation. The paper highlights innovative AI technologies such as machine learning, natural language processing, and predictive modeling, which can significantly improve disease monitoring and forecasting. By analyzing vast datasets in real time, these technologies facilitate quicker identification of emerging health threats and enable tailored public health responses. Furthermore, the review presents recommendations for integrating AI innovations into existing public health strategies, emphasizing improved data interoperability, investment in infrastructure, stakeholder collaboration, community engagement, and real-world pilot programs. Ultimately, the findings underscore the potential of AI-driven health data analytics to transform infectious disease management, thereby safeguarding public health.

Leveraging Digital Twins as a Common Operating Picture for Disaster Management: Case of Seismic Hazards

Disasters, both natural and man-made, pose significant challenges that necessitate swift and informed decision-making to minimize impacts and enhance recovery efforts. This study explores the integration of digital twins (DTs) with a common operating picture (COP) to improve disaster management practices, particularly during the response phase. Utilizing a comprehensive approach, the research develops a 3D core model of Kocaeli, Turkey, which incorporates real-time data from various sources, including building information models and seismic hazard assessments. The findings reveal that the DT–COP framework significantly enhances situational awareness and resource allocation, allowing for more effective response strategies. However, challenges related to data accuracy, integration, and interoperability persist. The study concludes that leveraging DTs as a COP can transform disaster management practices, ultimately improving response capabilities and community resilience while highlighting the need for ongoing research to address existing challenges.

Integrating Wearable Health Technology into Patient Care: Implications for Chronic Disease Management and Preventive Health

The integration of wearable health technology into patient care represents a transformative approach to managing chronic diseases and promoting preventive health. This article examines the potential of wearable devices, such as fitness trackers, smartwatches, and continuous monitoring sensors, to enhance patient outcomes by providing real-time data on key health indicators, including heart rate, blood pressure, physical activity, and sleep patterns. Through an analysis of current applications and advancements in wearable technology, this study assesses how these devices contribute to early disease detection, improved patient engagement, and proactive management of chronic conditions like diabetes, hypertension, and cardiovascular diseases. Findings indicate that wearable health technology supports more personalized and responsive care models, allowing healthcare providers to track patient progress remotely, adjust treatments as needed, and foster greater adherence to health interventions. Additionally, the study highlights challenges related to data privacy, interoperability, and patient adherence, underscoring the importance of addressing these issues to fully realize the potential of wearable technology in healthcare. By providing insights into the effective integration of wearable devices in clinical settings, this research offers valuable guidance for healthcare providers, policymakers, and technology developers aiming to improve chronic disease management and advance preventive healthcare strategies.

Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in‐laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine‐actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

Enabling circularity in Turkish construction: a case of BIM-based material management utilizing material passports

PurposeThe Turkish construction sector, facing mounting environmental pressures, needs to rapidly adopt circular economy (CE) principles. However, a critical barrier is the lack of accessible and comprehensive material information. This paper investigates how building information modeling (BIM) can facilitate the adoption of material passports (MPs) to overcome this challenge and helps enabling circularity in Turkish construction.Design/methodology/approachA case study approach is adopted, focusing on the New Istanbul Esenler Arena, a major sports facility designed with BIM. An in-depth analysis of the project's BIM model and material data was conducted to develop a practical framework for integrating MPs into BIM workflows. The framework outlines a comprehensive MP data structure, addresses technical integration strategies and highlights the importance of stakeholder collaboration.FindingsThe research demonstrates that integrating MPs with BIM has the potential to significantly enhance material tracking throughout the building lifecycle, leading to more informed decision-making regarding material selection, reuse and recycling. This approach can improve material recovery rates, reduce construction waste and minimize the environmental impact of construction projects. The study also identifies key challenges to MP implementation, such as ensuring data accuracy and interoperability, and proposes strategies to overcome these hurdles.Originality/valueThis paper contributes to the limited but growing body of knowledge on the practical implementation of BIM and MPs for circular construction, particularly in the Turkish context. The proposed framework, informed by a real-world case study, provides valuable guidance for construction professionals, policymakers and researchers seeking to accelerate the transition to a circular economy in the built environment.

The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities.

In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on N = 34 interviews with diverse stakeholders. The BRAIN Initiative-funded archive ecosystem faces interoperability and data stewardship challenges, such as achieving and maintaining interoperability of data and archives and harmonizing research participants' informed consents for tiers of access for human data across multiple archives. Yet, a benefit of this distributed archive ecosystem is the ability of more specialized archives to adapt to the needs of particular research communities. Finally, the multiple archives offer ample raw material for network evolution in response to the needs of neuroscientists over time. Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.

Assessment and the Future Development of the Zagreb 3D City Model

The first 3D model of the city of Zagreb was developed in 2008. Since then, the model has been improved and updated. Today, it is used for various purposes in the city planning and management. It is also publicly available to citizens through the ZG3D Web application. In this paper, an assessment of the existing 3D model is provided by considering different factors (i.e. consistency between models, standardization, data quality, data interoperability, data maintenance/governance, and use cases) that need to be tackled in order for the 3D city model to be further improved and used towards the creation of the digital twin and ultimately to support the smart city concept. The assessment considered some general criteria and also the 3D City Index assessment tool developed by the Urban Analytics Lab, National University of Singapore (NUS). The assessment results show that the existing model is used for various purposes, but to evolve towards a true digital twin, it must be improved.

The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review

This comprehensive review explores the applications and challenges of Digital Twin (DT) technology in smart grids. As power grid systems rapidly evolve to meet the increasing energy demands and the new requirements of renewable source integration, DTs offer promising solutions to enhance the monitoring, control, and optimization of these systems. In this paper, we examine the concept of DTs in the context of smart grids, and their requirements, challenges, and integration with the Internet of Things (IoT) and Artificial Intelligence (AI). We also discuss different applications in asset management, system operation, and disaster response. This paper analyzes current challenges, including data management, interoperability, cost, and ethical considerations. Through case studies from various sectors in Canada, we illustrate the real-world implementation and impact of DTs. Finally, we discuss emerging trends and future directions, highlighting the potential of DTs to revolutionize smart grid networks and contribute to more efficient, reliable, and sustainable power systems.

Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping

ABSTRACT An accurate and timely land-use map of an area is a valuable geospatial dataset. Making one requires assembling heterogeneous geospatial input data. This becomes computationally expensive as the number of inputs grow or the combinational classification logic becomes more convoluted. We present as a solution an analytical framework based on a DGGS. This provides a spatial data structure that: facilitates horizontal scaling; improves geospatial data interoperability; and makes classified geospatial data easier to reproduce. We demonstrate a benchmark of land-use assignment with a DGGS against the vector and raster geospatial data models. Significant performance benefits are apparent over the vector case, while performance is equivalent to raster. Yet a DGGS has other compelling benefits that makes it more convenient to use in the domain of land-use assignment than the raster data model.

Assessing the value of decentralized and interoperable data storage for service providers

Assessing the value of decentralized and interoperable data storage for service providers

Using the German National Medication Plan for Clinical Studies in Practice-Based Research Networks.

The German National Medication Plan (GNMP) can be a valuable and interoperable data source for clinical studies, due to its digital specification and mandatory provisioning for chronically ill patients. Digital transfer of a patients current GNMP from the Patient Data Management System (PDMS) into electronic case report forms would avoid error prone manual data capturing. It is also essential for studies in practice-based research networks (PBRN), where data capturing must have as little impact as possible on everyday practice. The following issues are currently preventing seamless digital integration: There is no standardized interoperable export of the GNMP from PDMS. In the current form, pharmaceutical catalogs are needed to decode the contained pharmaceutical registration numbers. As accessibility to the pharmaceutical catalogs is restricted, there is no generic access to the actual information needed for study data evaluation. In order to conduct studies, feasible workarounds for these issues had to be implemented in the standard operating procedures, tools and participating GP practices. To overcome the GNMP's current lack of digital interoperability, the proposed solution combines semi-automated data export from PDMS at the GP practice and manual database search at the study center with a semi-automated processing pipeline to balance workload between GP practices, study management and evaluation.