Data Exchange Standards Research Articles

The status of health information systems and digital transformation in the European Region

Abstract Although there have been investments in health information systems (HIS) and digitalization, WHO/Europe’s work with Member States (MS) shows that much can be improved still. Typically, there is a strong focus on data collection, but less on analysis and knowledge translation, resulting in available data not being optimally used to support decision making. Standards for data exchange and interoperability have not yet been fully implemented. Efforts are underway to train healthcare professionals, yet only half of MS have developed policies for digital health literacy. Fewer still have implemented a comprehensive digital inclusion plan, leaving disadvantaged populations at risk of falling behind. In line with the traditionally strong focus on HIS strengthening in WHO’s work, the European Programme of Work 2020-2025 (EPW) emphasises the need for MS to improve and modernise their HIS to ensure that decisions are data driven. One of the EPW flagship initiatives is Empowerment through Digital Health, promoting the leveraging and scaling up of digital transformation for better health. The Regional digital health action plan for the WHO European Region 2023-2030 outlines the strategic actions needed to achieve this, while fully respecting the values of equity, solidarity and human rights. Collaboration with MS and international partners is vital in this work. WHO/Europe has established several MS-driven health information networks in the past, most notably the European Health Information Initiative (the EHII). The EHII in 2023 has made a restart in the form of the European Health Information Network (HIN). The HIN will guide the work under the EU-funded action ‘Supporting Member States in the WHO European Region in strengthening health information systems and boosting health data governance’, which will be implemented by WHO/Europe. This strong European Commission and WHO/Europe partnership will be a main catalyst for HIS improvement in the Region the coming years.

Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis.

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

A novel Real-time Control System for next generation gravitational-wave detectors

This contribution presents the INFN Pisa group research and development efforts aimed at defining and implementing a novel Real-time Control System (RCS) for next-generation interferometers, focusing on the initial phase of the Einstein Telescope (ET) gravitational wave detector. The RCS handles complex closed-loop systems, managing data collection from sensing elements, processing and actuator control within well defined time constraints. It integrates multiple spatially distributed control units, facilitating communications among them, with external systems as well as with users. Two distinct hardware approaches are explored: “Katane” and “Zancle”. These systems primarily differ in their core processing elements. The “Katane” system is built on powerful DSP processor boards, based on the Super-Attenuator control system, developed at INFN Pisa for Advanced Virgo detector. It uses the MicroTCA.4 architecture standard, ensuring flexibility, easy maintenance, and fast data exchange among the various custom modules, including several types of Front-End data converter boards, FPGA-based pre-processing boards, DSPs and standard processors. On the other hand, ”Zancle” explores GPU-based processing, aiming to use cost-effective consumer market systems and to leverage substantial computational power of these devices, also incorporating machine learning algorithms.

Space Traffic Management: A Shared Space Object Catalog

This article provides an overview of, and terminology associated with, the topic of a “shared space object catalog,” especially as it relates to and for the purposes of promoting space sustainability and enabling Space Traffic Coordination (STC) and Space Traffic Management (STM). First, the terminology and organizations associated with a shared space object catalog are discussed. Then the requirements for a shared catalog are discussed along with the current state of play. Essential elements for a successful shared catalog are laid out, including but not limited to the configuration, data rules, data integrity and legal and policy issues. Finally, some recommendations and conclusions are drawn. The primary recommendation of this paper is that for a shared catalog to be successful it will be important to develop and utilize open-access space data exchange standards, methodologies, best practices, and potentially even open-source tools at the international level. To that end the Appendix contains information on open access data exchange standards.

Creating a data collection and management platform to support measurement of adolescent HIV care transition processes within low- and middle-income countries: The GRADUATE project.

Few national programs and research cohorts within low- and middle-income countries (LMICs) document transition-related processes and outcomes for adolescents and young adults living with HIV (AYLH) transitioning to adulthood. Between 2017-2020, The Global fRAmework of Data collection Used for Adolescent HIV Transition Evaluation (GRADUATE) project convened a collaborative advisory group to identify key variables and definitions capturing the process, predictors, and outcomes across the transition period. In total, 114 variables identified as essential to measuring AYLH transition-related data were identified and formatted into a GRADUATE Data Exchange Standard (DES), which was added to and harmonized with the existing International epidemiology Databases to Evaluate AIDS (IeDEA) DES. In 2019, the GRADUATE DES was pilot tested at four IeDEA facilities in Malawi, South Africa, and Thailand through a cross-sectional study. Upon comparing the variables to routine medical records, available data were too limited to adequately capture transition-related processes and outcomes. However, additional data collection using GRADUATE tools was feasible and improved completeness. Of the 100 (52% female) AYLH included in the pilot study, 71% had transitioned/transferred to adult care, with 42% transitioning from an adolescent-specific model of care within an integrated family clinic to having their clinic visits scheduled on a different day of the week while 58% transferred from a pediatric facility to one offering adult HIV care. While almost all (94%) had a transition-related discussion with their healthcare providers prior to the transition, we found that 69% (95% CI 49-85%) were somewhat or very satisfied/comfortable with the post-transfer clinic and the staff. Utilization of the GRADUATE DES better characterized AYLH transitioning to adulthood across LMICs, and optimally measured transition preparation activities and outcomes. Utilization of the GRADUATE DES in other settings could facilitate comparisons and identify gaps in the care of transitioning adolescents that need to be addressed.

Establishing data elements and exchange standards to support long COVID healthcare and research.

The Multiple Chronic Conditions (MCCs) Electronic Care (e-Care) Plan project aims to establish care planning data standards for individuals living with MCCs. This article reports on the portion of the project focused on long COVID and presents the process of identifying and modeling data elements using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. Critical data elements for managing long COVID were defined through a consensus-driven approach involving a Technical Expert Panel (TEP). This involved 2 stages: identifying data concepts and establishing electronic exchange standards. The TEP-identified and -approved long COVID data elements were mapped to the HL7 US Core FHIR profiles for syntactic representation, and value sets from standard code systems were developed for semantic representation of the long COVID concepts. Establishing common long COVID data standards through this process, and representing them with the HL7 FHIR standard, facilitates interoperable data collection, benefiting care delivery and patient-centered outcomes research (PCOR) for long COVID. These standards may support initiatives including clinical and pragmatic trials, quality improvement, epidemiologic research, and clinical and social interventions.By building standards-based data collection, this effort accelerates the development of evidence to better understand and deliver effective long COVID interventions and patient and caregiver priorities within the context of MCCs and to advance the delivery of coordinated, person-centered care. The open, collaborative, and consensus-based approach used in this project will enable the sharing of long COVID-related health concerns, interventions, and outcomes for patient-centered care coordination across diverse clinical settings and will facilitate the use of real-world data for long COVID research.

Prospective Directions in the Computer Systems Industry Foundation Classes (IFC) for Shaping Data Exchange in the Sustainability and Resilience of Cities

Sustainability and resilience in addressing construction’s environmental, social, and economic challenges rely on interoperability. A model-centred approach using standardised information structures like industry foundation classes (IFC) is essential for data sharing in architecture, engineering, construction, and facility management. Achieving complete interoperability across domains requires further research. This review paper focuses on IFC schema, highlighting upcoming developments like IFC 5 and “IFC x”, with a core emphasis on modularisation to enhance domain interoperability, improved links between building information modelling (BIM) and geographic information systems (GIS), along with IoT integration into BIM, cloud-based collaboration, and support for other advanced technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI), and digital twins. Through a critical examination of the IFC and an outlook towards its future enhancements, the research has the potential to offer valuable insights into shaping the trajectory of future advancements within the AEC and facility management sectors. The study’s discoveries could aid in establishing standardised data exchange protocols in these industries, promoting uniformity across projects, facilitating smoother communication, and mitigating errors and inefficiencies. Anticipating enhancements in the IFC could catalyse innovation, fostering the adoption of emerging technologies and methodologies. Consequently, this could drive the creation of more sophisticated tools and procedures, ultimately enhancing project outcomes and operational effectiveness.

CancerX Data Sprint: Advancing national oncology data standards through a unique public-private partnership.

e23283 Background: In a concerted effort to enhance the quality, accessibility, and effectiveness of comprehensive real-world cancer datasets, CancerX, a public-private partnership announced by the White House recently conducted a demonstration project, the CancerX Data Sprint. CancerX Data Sprint attempted a novel approach to quickly inform collective efforts of the Center for Medicare and Medicaid Innovation (CMMI), the National Cancer Institute (NCI), the US Food and Drug Administration (FDA), and the Office of the National Coordinator for Health Information Technology (ONC) to advance cancer-related data standards. Specifically, at the intersection of CMMI’s Enhancing Oncology Model (EOM) and the development of ONC’s United States Core Data for Interoperability (USCDI+) – Oncology extension, CancerX leveraged its unique model and broad membership to report novel insights to advance data-driven innovations in oncology research, care, and policy. Methods: From Sept to Dec 2023, the Data Sprint convened 150+ member organizations from industry and government partners to 1) Identify the high-value research questions that could be asked about the standardized real-world data (RWD) generated through EOM 2) Supplement the existing data elements with new ones to extend and enhance the ability to conduct scientific, clinical, and research inquiries with comprehensive datasets. The CancerX community opined via surveys, written feedback, and live workshops to identify key data elements, discuss their clinical value and technical feasibility, and consider their use in real-world settings as part of the comprehensive datasets. Results: Building on the current EOM clinical data elements, we identified and reported 15 new clinical data elements for considerations with high clinical utility and ease of technical abstraction. This comprehensive RWD encompasses a diverse range of patient demographics, clinical details, treatment histories, and outcomes, creating a more accurate and inclusive portrayal of cancer treatment effectiveness and patient journeys. This depth of information is crucial for optimizing the personalization of treatment pathways, conducting impact assessments, and addressing healthcare disparities. Essentially, the new datasets serve as a valuable repository of insights, driving advancements in cancer research, treatment and elevating care quality. Conclusions: CancerX remains committed to supporting federal data initiatives like USCDI+ Cancer standards development and others by providing recommendations for essential data elements implementation to enhance clinical utility, technical feasibility, and exchange of standardized cancer data. By adopting a unique approach and assembling an interdisciplinary group, we can ensure that data standardization efforts prioritize high-utility data elements, ultimately maximizing benefits for our patients.

Adaptive optics telemetry standard

Context. The amount of adaptive optics (AO) telemetry generated by visible/near-infrared ground-based observatories is ever greater, leading to a growing need for a standardised data exchange format to support performance analysis, AO research, and development activities that involve large-scale telemetry mining, processing, and curation. Aims. This paper introduces the Adaptive Optics Telemetry (AOT) data exchange format as a standard for sharing AO telemetry from visible/infrared ground-based observatories. AOT is based on the flexible image transport system (FITS) and aims to provide unambiguous and consistent data access across various systems and configurations, including natural and single- or multiple-laser guide-star AO systems. Methods. We designed AOT with a focus on two key use cases: atmospheric turbulence parameter estimation and point-spread function reconstruction. We prototyped and tested the design using existing AO telemetry datasets from multiple systems: single conjugate with natural and laser guide stars, tomographic systems with multi-channel wavefront sensors, and single- and multi-wavefront correctors in systems featuring either a Shack-Hartmann or Pyramid as the main wavefront sensor. Results. The AOT file structure has been thoroughly defined, with specified data fields, descriptions, data types, units, and expected dimensions. To support this format, we have developed a Python package that enables the data conversion, reading, writing, and exploration of AOT files; it has been made publicly available and is compatible with a general-purpose Python package manager. We have demonstrated the flexibility of the AOT format by packaging data from five different instruments, installed on different telescopes.

TPMS2STEP: Error-Controlled and [formula omitted] Continuity-Preserving Translation of TPMS Models to STEP Files Based on Constrained-PIA

Triply periodic minimal surface (TPMS) is emerging as an important way of designing microstructures. However, there has been limited use of commercial CAD/CAM/CAE software packages for TPMS design and manufacturing. This is mainly because TPMS is consistently described in the functional representation (F-rep) format, while modern CAD/CAM/CAE tools are built upon the boundary representation (B-rep) format. One possible solution to this gap is translating TPMS to STEP, which is the standard data exchange format of CAD/CAM/CAE. Following this direction, this paper proposes a new translation method with error-controlling and C2 continuity-preserving features. It is based on an approximation error-driven TPMS sampling algorithm and a constrained-PIA algorithm. The sampling algorithm controls the deviation between the original and translated models. With it, an error bound of 2ϵ on the deviation can be ensured if two conditions called ϵ-density and ϵ-approximation are satisfied. The constrained-PIA algorithm enforces C2 continuity constraints during TPMS approximation, and meanwhile attaining high efficiency. A theoretical convergence proof of this algorithm is also given. The effectiveness of the translation method has been demonstrated by a series of examples and comparisons. The code will be open-sourced upon publication.

Enhancing Coal Mining Efficiency

Coal is a prominent energy resource for several countries. Of late, exploring the automatic management and control of coal mining has been a significant task. This article presents a framework for a mine-wide integrated automation management and control platform with the goal of advancing coal mining through unified data, models, platforms, and plans. Utilizing cutting-edge technologies, the platform offers resource management, real-time monitoring, remote control, statistical analysis, and intelligent alarm systems. Data access design ensures standardized data collection and exchange, fostering interoperability. A big data storage center manages heterogeneous data sources. The platform interface design emphasizes flexibility and scalability through containerized applications and microservices frameworks, streamlining deployment. The functional design encompasses subsystem configuration access, real-time monitoring, remote access, etc. A detailed evaluation is presented to demonstrate the significance of the proposed platform in terms of functionality, performance, and scalability.

Minimizing Cost Overrun in Rail Projects through 5D-BIM: A Conceptual Governance Framework

Integration of 5D Building Information Modeling (BIM) into large rail projects has the potential to significantly enhance cost management and control. Nevertheless, 5D-BIM implementation has encountered difficulties stemming from technical, functional, and governance-related factors. This paper builds a conceptual framework to support financial decision making, enhances project management, and promotes efficient project delivery. The framework encompasses a set of interrelated elements that include project governance, BIM policies and standards, digital platforms, BIM LOD, cost-estimation classification, and continuous improvement. The proposed framework acknowledges the significance of project governance in guiding and organizing the implementation of 5D-BIM. Additionally, BIM policies and standards ensure the adherence to quality standards for the produced BIM models. Digital platforms serve as the basis for multiple users to generate, access, share, and exchange project information. BIM LOD promotes collaboration and coordination among all project stakeholders. Cost-estimation classification aligns the estimation process with the development of project scope and financial decision making. Continuous improvement plays a vital role in optimizing processes, enhancing efficiency, and achieving higher-quality outcomes. Moreover, it fosters stakeholder satisfaction, improves project performance, and nurtures a conducive environment for innovation and learning. The study analyzes the framework utilization in Victorian rail projects and identifies key implementation challenges. The main technical hurdles encountered were the lack of current horizontal infrastructure standards for data exchange and the lack of compatibility with current cost-management standards. Increased project complexity and the absence of clear project governance strategies and processes also posed organizational challenges. A further validation of the framework in real-world rail projects was recommended to achieve the implementation goals.

Framework for Designing Quality-Centered Assistance Systems for the Smart Factory

Employee assistance systems are essential for companies to manage the growing complexity of information and processes. However, current systems mainly provide process-oriented support through documentation options, leaving a gap in real-time quality assessment. Real-time or in-process quality assessment becomes increasingly attractive for large and complex components where automated inspection is not feasible. To fill this gap, this paper proposes a new modeling framework for quality-centric assistance systems that are tailored to the Smart Factory environment. The framework integrates digital design principles, standard manufacturing processes, and quality parameters, facilitating seamless integration and standardized data exchange. An expert evaluation of the framework yielded positive ratings across various dimensions, underscoring its potential impact. Feedback from experts indicated the need for further refinement, including enhancing credibility through implementation and field tests, providing more detailed procedure models, and addressing challenges in sensor integration. Overall, this framework represents a significant advancement towards process-oriented assistance in various application areas, with implications for optimizing production and quality processes.

Announcements

The Society of Exploration Geophysicists has released an update to the SEG-Y seismic data-exchange standard.

IoT 엣지디바이스를 이용한 경량화 클라우드 컴퓨팅환경 구축 및 검증

This study validated a cloud computing environment using edge devices for Internet of Things (IoT) sensor data collection and communication. Lightweight cloud infrastructure enhanced resource efficiency, minimized latency, and optimized data storage and processing. Technical achievements encompass standardized data exchange, improved device scalability and flexibility through software technologies, real-time data updates via synchronization, and the creation of an API-based user interface for an intuitive dashboard. The study substantiates the efficient establishment and validation of a cloud computing environment using edge devices, providing practical solutions for IoT data management and processing.

Employing word-embedding for schema matching in standard lifecycle management

Today, businesses rely on numerous information systems to achieve their production goals and improve their global competitiveness. Semantically integrating those systems is essential for businesses to achieve both. To do so, businesses must rely on standards, the most important of which are data exchange standards (DES). DES focus on technical and business semantics that are needed to deliver quality and timely products and services. Consequently, the ability for businesses to quickly use and adapt DES to their innovations and processes is crucial.Traditionally, information standards are managed and used 1) in a platform-specific form and 2) usually with standalone and file-based applications. These traditional approaches no longer meet today's business and information agility needs. For example, businesses now must deal with companies and suppliers that use heterogeneous syntaxes for their information. Syntaxes that are optimized for individual but have different objectives. Moreover, file-based standards and the usage specifications derived from the standards cause inconsistencies since there is neither a single standard format for each usage specification nor a single source of truth for all of them.As the number and types of information systems grow, developing, maintaining, reviewing, and approving standards and their derived usage specifications are becoming more difficult and time consuming. Each file-based usage specification is typically based on a different syntax than the standard syntax. As a result, each usage specification must be manually updated as the standard evolves; this can cause significant delays and costs in adopting the new and better standard versions. National Institute of Standards and Technology (NIST) in collaboration with the Open Application Groups Inc. (OAGi) has developed a web-based standard lifecycle management tool called SCORE to address these problems. The objective of this paper is to introduce the SCORE tool and discuss its particular functionality where a word-embedding technique has been employed along with other schema-matching approaches. Together they can assist standard users in updating the usage specification due to the release of new version of a standard leading to faster adaptations of DES to new processes.

Digital child health: opportunities and obstacles. A joint statement of European Academy of Paediatrics and European Confederation of Primary Care Paediatricians.

The advancement of technology and the increasing digitisation of healthcare systems have opened new opportunities to transform the delivery of child health services. The importance of interoperable electronic health data in enhancing healthcare systems and improving child health care is evident. Interoperability ensures seamless data exchange and communication among healthcare entities, providers, institutions, household and systems. Using standardised data formats, coding systems, and terminologies is crucial in achieving interoperability and overcoming the barriers of different systems, formats, and locations. Paediatricians and other child health stakeholders can effectively address data structure, coding, and terminology inconsistencies by promoting interoperability and improving data quality and accuracy of children and youth, according to guidelines of the World Health Organisation. Thus, ensure comprehensive health assessments and screenings for children, including timely follow-up and communication of results. And implement effective vaccination schedules and strategies, ensuring timely administration of vaccines and prompt response to any concerns or adverse events. Developmental milestones can be continuously monitored. This can improve care coordination, enhance decision-making, and optimise health outcomes for children. In conclusion, using interoperable electronic child health data holds great promise in advancing international child healthcare systems and enhancing the child's care and well-being. By promoting standardised data exchange, interoperability enables timely health assessments, accurate vaccination schedules, continuous monitoring of developmental milestones, coordination of care, and collaboration among child healthcare professionals and the individual or their caregiver. Embracing interoperability is essential for creating a person-centric and data-driven healthcare ecosystem where the potential of digitalisation and innovation can be fully realized.

Enhancing Interoperability: Exploring Data Exchange Standards in SaaS Laboratory Management Systems

In the ever-changing environment of health care, smooth information exchange among different systems is significant to ensure efficiency and high-quality patient care. This is also important in laboratory management, where time and accuracy are critical for patients' diagnosis and treatment choices. Nevertheless, interoperability is still a big issue, with SaaS laboratory systems being the primary concern. This study focuses on the importance of interoperability in contemporary healthcare systems, with particular emphasis on laboratory management. It shows the significance of an uninterrupted data flow between SaaS laboratory management systems and other healthcare I.T. systems, such as E.H.R.s and HIEs. Existing data exchange standards and frameworks for interoperability among SaaS laboratory management systems are discussed, including the challenges of achieving interoperability. A quantitative research approach-based questionnaire was deployed to assess the interoperability requirements and processes of the genetic testing laboratories in the survey. Investigations point to different levels of compatibility among SaaS lab management systems in terms of features offered as well as the challenges faced by the latter. Challenges include different data formats, communication protocol standards, and data model incompatibility. The study emphasizes the critical role of interoperability and data exchange in SaaS laboratory management systems and the entire healthcare industry. The methods of overcoming the interoperability problem are investing in education, creating collaborative partnerships, promoting integration frameworks, and establishing incentives for obedience to standardized data exchange format.

Early and fair COVID-19 outcome risk assessment using robust feature selection

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.

Critical Factors Influencing Data Use and Utilization in Health Systems: A Focus on Data and Interoperability Standards for Health Information Exchange (HIE) in Uganda’s Healthcare System

Abstract Limited use and utilization of health data in Uganda's healthcare system is influenced by various factors including; absence of standardized data formats, lack of data governance policies, unskilled data management human resources, limited data use culture, poor data quality, complacency, limited political will, and lack of interoperability. Worse still, the existing data interoperability standards majorly designed for developed world health systems, may not be suitable for Uganda, due to differences in health information maturity levels. This paper examines the critical factors affecting data use and utilization in Uganda, specifically focusing on interoperability and data standards. A cross-sectional design was employed in this study in selected health facilities with electronic systems in Uganda. Purposive sampling was used to select sites and participants based on predetermined criteria. The study included 28 health center IVs, referral hospitals, government entities, and eHealth stakeholders. The findings highlight key factors including limited data collection and management systems, poor data quality, inadequate data analysis capacity, absence of data exchange standards, limited technology access, inadequate funding, and deficiencies in data sharing and dissemination. These factors if addressed through data and interoperability standards, can play a pivotal role in promoting efficient and effective healthcare delivery and outcomes in Uganda.