Interoperability Challenges Research Articles

Lost in interpretation: Making sense of equivocalities and tensions in approaches to interoperability

Interoperability promotes data-driven practices by enabling the aggregation and sharing of data from distributed information systems. It thus offers potential benefits to organizations for delivering more efficient and sustainable services. Despite its potentials, achieving interoperability remains a significant challenge. In this paper, we shed light on the obscure landscape of organizational challenges that lead to low interoperability. Based on a 3-year longitudinal single-case study of a public agency’s attempts to enhance interoperability, we uncover how three major categories of equivocality – scope, needs, and priorities – hinder approaches to interoperability at the data, software, and IT levels. We also reveal that entangled organizational tensions explain why such equivocalities emerge. By analyzing interoperability challenges from the combined concepts of equivocality and tensions, this paper offers a unique perspective on their intricate nature, making sense of the obscure landscape of challenges in various approaches to interoperability.

NATO’s Artificial Intelligence Strategy and Interoperability Challenges: The Case of Turkey

ABSTRACT The rapid advancement of artificial intelligence (AI) has significantly changed military applications, creating new competitive advantages and shifting the global balance of power. This article examines NATO’s AI strategy and the associated interoperability challenges, with a particular focus on Turkey. NATO’s AI strategy seeks to enhance interoperability among its member states by fostering the integration of AI technologies into military capabilities. However, achieving this goal is complicated by the varying levels of AI technological advancement, divergent national AI-military strategies and differing geopolitical considerations among member countries. Using Turkey as a case study, this paper explores how the rapid development of AI-based military drones contributes to Turkey’s strategic autonomy and enhances regime resilience while also highlighting certain interoperability considerations within NATO. The analysis underlines the need for a cohesive approach to AI integration that addresses these disparities to maintain NATO’s collective defence capabilities.

Strategies for unlocking EMR Interoperability: A Narrative Review

Electronic Medical Records (EMR) is intended to solve many issues in patient care pathway besides the administrative roles which equally makes the hospital paperless, but they still face major interoperability issues. When there is no systematic connection between different healthcare providers, medical information pertaining to patients becomes fragmented, incomplete, and outdated. This study was undertaken to investigate the strategies used to overcome interoperability challenges by EMR implementors. A narrative review was conducted, and various keywords were used to search in EBSCO, Google Scholar, and pertinent healthcare informatics articles for papers published between 2012 and 2024. Drawing from a range of studies, the findings indicate that Machine learning and artificial intelligence, Standardization of Data Exchange Protocols, Blockchain technology, EHR-Agnostic Platforms, Training and Organizational Support, Government and Policy Interventions, social factors are the strategies that successful EMR implementers have employed to achieve EMR interoperability.

Enhancing Security and Traceability in Aerospace Supply Chains through Block Chain Technology

This study explores the impact of blockchain technology on aerospace supply chains, focusing on mitigating risks such as supply chain disruptions, counterfeit parts, and regulatory compliance challenges. A mixed-methods approach was employed, combining quantitative and qualitative analysis using data sourced from publicly available databases, including the Federal Aviation Administration (FAA), the Aerospace Industries Association (AIA), the International Air Transport Association (IATA), and reports from blockchain technology providers. Descriptive analysis identified supply chain disruptions (M = 3.74, SD = 1.17) as the most significant risk, followed by counterfeit parts (M = 3.34, SD = 1.20). Structural Equation Modeling (SEM) demonstrated that part authentication and provenance tracking had the strongest impact on improving traceability (β = 0.40, p < 0.001) and transparency (β = 0.38, p < 0.001). A Cost-Benefit Analysis (CBA) revealed a positive return on investment, with cost-benefit ratios up to 2.31. Blockchain adoption was also found to reduce CO₂ emissions by up to 25%, while improving operational efficiency through shorter procurement lead times and lower administrative costs. However, regulatory misalignment and interoperability challenges remain barriers to full adoption. The study concludes that blockchain is a transformative tool for enhancing traceability, security, and sustainability in aerospace supply chains.

Federating cross-domain BIM-based knowledge graph

Despite decades of the digitization of the construction industry, interoperability challenges prevail. One of which is the lack of sufficient integration between domain-specific BIM models. Therefore, the paper proposes a novel methodology for federating cross-domain BIM-based knowledge graphs. The approach leverages the integration of multiple models into a single knowledge graph, aiming to bridge the existing gaps in data exchange and enhance the semantic richness of building information models. Through a detailed exploration of existing data exchange mechanisms, popular ontologies, and schema limitations, this research addresses the interoperability dilemma. A practical demonstration, encapsulated in the “LBD-Online-Merge” demo application, validates the methodology’s efficacy in real-world scenarios, illustrating its potential to revolutionize building operations management and data preparation for the operational phase of a building’s lifecycle. This study significantly contributes to the academic and practical discourse by providing a robust framework for achieving a more integrated, intelligent building information management ecosystem, setting a new benchmark for future research and applications in constructing semantically rich digital twins of the built environment.

Sociotechnical Factors Affecting Interoperability of Digital Humanities

ABSTRACTIn the past two decades, the digitization of humanities resources has rapidly expanded, giving rise to the field of Digital Humanities (DH). This evolution has fostered a growing need for effective interoperability—the ability to access and integrate data across diverse digital collections, tools, and platforms. Despite significant technological advancements and the development of standards aimed at facilitating interoperability, challenges persist. These challenges are not only technical but are also influenced by sociocultural factors, which include organizational and cultural elements. This paper aims to bridge the gap in the literature concerning these sociotechnical factors affecting interoperability within DH projects and collections.This paper presents the analysis of the real‐world experiences of DH researchers attempting to connect material from different sources. Each participant described a specific incident where combining software or content was particularly problematic. This data was analyzed via thematic analysis. The findings reveal a complex landscape of interoperability challenges, underpinned by a range of sociotechnical factors. These factors include, but are not limited to, discrepancies in metadata standards, differences in organizational cultures, and varying levels of technical expertise among practitioners. By highlighting these factors, the paper contributes valuable insights into the multifaceted nature of interoperability issues in DH, offering evidence‐based plans to address these challenges.

Assessing semantic interoperability in environmental sciences: variety of approaches and semantic artefacts

The integration and reuse of digital research products can be only ensured through the adoption of machine-actionable (meta)data standards enriched with semantic artefacts. This study compiles 540 semantic artefacts in environmental sciences to: i. examine their coverage in scientific domains and topics; ii. assess key aspects of their FAIRness; and iii. evaluate management and governance concerns. The analyses showed that the majority of semantic artefacts concern the terrestrial biosphere domain, and that a small portion of the total failed to meet the FAIR principles. For example, 5.5% of semantic artefacts were not available in semantic catalogues, 8% were not built with standard model languages and formats, 24.6% were published without usage licences and 22.4% without version information or with divergent versions across catalogues in which they were available. This investigation discusses common semantic practices, outlines existing gaps and suggests potential solutions to address semantic interoperability challenges in some of the resources originally designed to guarantee it.

Efficient Data Exchange between WebAssembly Modules

In the past two decades, there has been a noticeable decoupling of machines and operating systems. In this context, WebAssembly has emerged as a promising alternative to traditional virtual machines. Originally designed for execution in web browsers, it has expanded to executing code in restricted and secure environments, and it stands out for its rapid startup, small footprint, and portability. However, WebAssembly presents significant challenges in data transfer and the management of interactions with the module. Its specification requires each module to have its own memory, resulting in a “share-nothing” architecture. This restriction, combined with the limitations of importing and exporting functions that only handle numerical values, and the absence of an application binary interface (ABI) for sharing more complex data structures, leads to efficiency problems; these are exacerbated by the variety of programming languages that can be compiled and executed in the same environment. To address this inefficiency, the Karmem was designed and developed. It includes a new interface description language (IDL) aimed at facilitating the definition of data, functions, and relationships between modules. Additionally, a proprietary protocol—an optimized ABI for efficient data reading and minimal decoding cost—was created. A code generator capable of producing code for various programming languages was also conceived, ensuring harmonious interaction with the ABI and the foreign function interface. Finally, the compact runtime of Karmem, built atop a WebAssembly runtime, enables communication between modules and shared memory. Results of the experiments conducted show that Karmem represents an innovation in data communication for WASM in multiple environments and demonstrates the ability to overcome challenges of efficiency and interoperability.

EHR Interoperability Challenges Leveraging HL7 FHIR for Seamless Data Exchange in Healthcare

Electronic health records, often known as EHRs, have revolutionised the healthcare industry by making patient information more easily accessible and more efficient on the whole. There is still a huge obstacle to overcome in order to achieve seamless data transmission across different electronic health record systems. variances in data formats, terminologies, and standards often give rise to interoperability concerns. These variances impede the smooth flow of information across various systems and have the potential to cause problems. The Fast Healthcare Interoperability Resources (FHIR) standard, which is based on Health Level Seven (HL7), has emerged as a potentially useful option to overcome these difficulties. Through the provision of a uniform and standardized method for the sharing of healthcare data, FHIR is intended to facilitate the interchange of data in a manner that is both more efficient and trustworthy. The issues that are involved with electronic health record (EHR) interoperability are investigated in this study, as is the manner in which HL7 FHIR may ease the interchange of data in healthcare settings in a smooth manner. It starts out by providing an overview of the basic problems that exist in electronic health record (EHR) interoperability. These problems include data fragmentation, inconsistent data formats, and the absence of standardized protocols. HL7 FHIR is then presented in the article, along with a description of its fundamental principles. These concepts include the emphasis placed on interoperability, the simplicity of implementation, and the support for contemporary web technologies.

Best practices for business process automation description – a case study

ABSTRACT Organizations in competitive, regulated environments must enhance business processes for efficiency, quality, and compliance while minimizing risks and costs. Process automation solutions play a vital role in achieving these goals, though the variety of tool descriptions creates challenges for compatibility and interoperability. This hinders innovation and competitiveness. The adoption of standard specifications or widely accepted best practices for automation descriptions offers a solution. This research aims to identify a set of best practices to guide process-oriented organizations in evaluating their current automation practices, ensuring alignment and fostering improvements in business process automation.

Pregnant People-Infant Linkage for Surveillance

Objectives and ApproachAs maternal mortality and morbidity continue to progress, better data is needed to comprehensively understand the problem, especially in underserved populations who are differentially impacted. Surveillance in safety-net populations is complicated by care fragmentation and interoperability challenges between primary care and hospital settings. . AllianceChicago (AC), a health center-controlled network, supports a common data infrastructure across a set of Federally Qualified Health Centers (FQHCs) in Chicago that is further linked to a distributed query infrastructure connecting Chicago’s major health systems known as CAPriCORN. Leveraging electronic health record (EHR) data collected by the AC and CAPriCORN consortium, the objective of this surveillance effort was to connect infant data with maternal data while protecting privacy. We developed a deterministic matching algorithm that utilized multiple approaches and technologies for Pregnant People-Infant (PPI) pair identification. The linkage process involved a five-step method in which each step is recorded within the linkage database and the confidence interval is calculated. ResultsPreliminary analyses are ongoing. Through each step of the linkage process, the confidence interval increased with the increase of matching nodes, highlighting its accumulative accuracy. The result demonstrates the feasibility of linking maternal and baby healthcare characteristics using a range of clinical and demographic variables captured in pseudonymized hospital and ambulatory data. Conclusions and ImplicationsOur approach improves the capacity of EHR data for surveillance while protecting patient privacy and preserving data confidentiality. Triangulating outcomes recorded in different settings can help improve data quality for research and surveillance.

Blockchain's Transformative Potential in Healthcare

In Blockchain in Healthcare Today, the journal explores blockchain's transformative potential, its current applications, and the path forward for its integration into the healthcare ecosystem. The healthcare industry is currently facing significant challenges and opportunities after COVID-19. As we navigate the complexities of increasing healthcare costs and technological updates for better patient outcomes, innovative technologies are emerging as pivotal tools for healthcare transformation. Healthcare digital platforms have witnessed revolutionizing the dynamics of healthcare systems using disruptive technologies. However, while these technologies have garnered extensive attention for their transformative potential, there remains a critical gap in our understanding of the impact of digital technology on the healthcare industry. Population health management has critical challenges in data protection, sharing, and interoperability, where personalized medicines and wearable devices are highlighted as a concern. Patients and medical personnel need a safe and simple way to record, transmit, or access information through networks without fearing for their safety. Using blockchain technology can help address these problems (Haleem et al., 2021).

Re-ISSUES—Renewable Energy-Linked Interoperable Smart and Sustainable Urban Environmental Systems

Smart cities will be smart if they improve their citizens’ quality of life; to do so, it is essential to listen to citizens and collaborate with service and technological companies. For that, digitalization seems essential. Environmental management systems are complex and expensive. If their lifecycle costs are reduced, these systems would be more sustainable. This can be achieved through citizen collaboration (CS), the use of low-cost Internet of Things (IoT) devices, and collaboration with local renewable energy businesses. All this leads to a real interoperability challenge. Systems engineering offers a valid framework for managing information and knowledge for environmental systems. It offers a range of guides for processes that can improve the quality of the related information and the reusability of knowledge throughout the lifecycles of these systems. After quantifying the opportunity and the cost for a motivational case of atmospheric neighborhood odor impact and introducing trends and opportunities in energy management, the authors propose a model for renewable energy-linked interoperable smart and sustainable urban environmental systems (re-ISSUES). The model’s ontology is used to discover research trends and potential for improvements to the model itself, enabling semantic interoperability and knowledge reuse.

How Interoperability Can Enable Artificial Intelligence in Clinical Applications.

This paper explores the critical role of Interoperability (IOP) in the integration of Artificial Intelligence (AI) for clinical applications. As AI gains prominence in medical analytics, its application in clinical practice faces challenges due to the lack of standardization in the medical sector. IOP, the ability of systems to exchange information seamlessly, emerges as a fundamental solution. Our paper discusses the indispensable nature of IOP throughout the Data Life Cycle, demonstrating how interoperable data can facilitate AI applications. The benefits of IOP encompass streamlined data entry for healthcare professionals, efficient data processing, enabling the sharing of data and algorithms for replication, and potentially increasing the significance of results obtained by medical data analytics via AI. Despite the challenges of IOP, its successful implementation promises substantial benefits for integrating AI into clinical practice, which could ultimately enhance patient outcomes and healthcare quality.

Standardizing X-Ray and Angiography Procedure Coding in Estonia: Validating TermX Through the Implementation of SNOMED CT.

The existence of multiple code systems and standards has highlighted the necessity for innovative solutions to bridge these discrepancies. This research investigates the utilisation of TermX to tackle the challenges of interoperability in radiology procedures, with a specific emphasis on angiography and X-ray modalities. The study produced a revised RadLex data model and mapping guide, designed to classify radiology services using TermX. In total, 380 concepts were required to comprehensively describe all 622 procedures examined. Our study demonstrates the effectiveness of TermX in simplifying the process of mapping between code systems, thus enabling more efficient analysis, and reporting of data.

ONCO-FAIR Project: Improving Data Interoperability in Oncology Chemotherapy Treatments for Data Reuse.

The ONCO-FAIR project's initial experimentation aims to enhance data interoperability in oncology chemotherapy treatments, adhering to the FAIR principles. This study focuses on integrating the HL7 FHIR standard to address interoperability challenges within chemotherapy data exchange. Collaborating with healthcare institutions in Rennes, the research team assessed the limitations of current standards such as PN13, mCODE, and OSIRIS, leading to the customization of twelve FHIR resources complemented by two chemotherapy-specific extensions. The methodological approach follows the Integrating the Healthcare Enterprise (IHE) framework, organizing the process into four key stages to ensure the effectiveness and relevance of health data reuse for research. This framework facilitated the identification of chemotherapy-specific needs, the evaluation of existing standards, and data modeling through a FHIR implementation guide. The article underscores the importance of upstream interoperability for aligning chemotherapy software with clinical data warehouse infrastructure, showcasing the proposed solution's capability to overcome interoperability barriers and promote data reuse in line with FAIR principles. Furthermore, it discusses future directions, including extending this approach to other oncology data categories and enhancing downstream interoperability with health data sharing platforms.

UNICOM Test Lab: Implementation of ISO's IDMP Standards Across the EU.

The UNICOM Test Lab enhances patient safety and healthcare efficiency by implementing ISO's IDMP standards across the EU. Funded by the EU's HORIZON Programme, this initiative unites diverse stakeholders to enable the seamless exchange of medicinal product information. This paper details the interoperability challenges and methodologies employed, highlighting the UNICOM Test Lab's role in validating and testing tools for IDMP standards implementation, aiming for semantic interoperability and a safe pharmaceutical supply chain within the EU.

A distributed and cooperative signature-based intrusion detection system framework for multi-channel man-in-the-middle attacks against protected Wi-Fi networks

A Multi-Channel Man-in-the-Middle (MC-MitM) attack is an advanced form of MitM attack, characterized by its ability to manipulate encrypted wireless communications between the Access Point (AP) and clients within a WiFi network. MC-MitM attacks can target any Wi-Fi client, regardless of the authentication method used with the AP. Notable examples of such attacks include Key Reinstallation Attacks and FragAttacks, which have impacted millions of WiFi systems worldwide, especially those involving Internet of Things devices. Current defense mechanisms are inadequate against these attacks due to interoperability challenges and the need for modifications to devices or protocols within the targeted Wi-Fi networks. This paper introduces a distributed and cooperative signature-based wireless intrusion detection mechanism designed for online passive monitoring to detect malicious traffic patterns during MC-MitM attacks in any environment, from apartments and houses to large areas like hotels, offices or industrial sites. We implemented the proposed framework on Raspberry Pis and evaluated it in real-world settings. Our evaluation demonstrates that this framework can effectively identify MC-MitM attacks with an average accuracy of 98% when deployed across different locations within our experimental testbed.

Assessing Cross-Border Telemedicine Data Exchange in the European Union: A Call to Action.

Background: Telemedicine offers potential benefits for health care delivery. However, evidence of cross-border telemedicine data exchange within the European Union (EU) remains limited. The objective of this communication provides a brief outline of the regulatory framework, initiatives, and challenges associated with cross-border telemedicine data exchange in the EU, setting the stage for a comprehensive evidence assessment. Methods: We explore the current regulatory landscape (European Health Data Space), existing initiatives (the European Electronic Health Record Exchange Format), and interoperability challenges (e.g., legal, technical, semantic) facing EU cross-border telemedicine data exchange. Results: There is a need for thorough evidence assessment of cross-border telemedicine and related data movements. Conclusion: Understanding the current landscape of cross-border telemedicine is crucial. This article highlights the need for evidence assessment through a formal review to inform future research and policy initiatives in this domain.

Ubiquitous computation in internet of vehicles for human-centric transport systems

The Internet of Vehicles (IoV) has the potential to bring about a revolutionary transformation in transportation through its influence on human behavior and interactions between users and vehicles. However, interoperability challenges between retailer organizations and manufacturers present a barrier to decision-making processes and impact the human-centric nature of the IoV. Ethical dilemmas arise as a result of the IoV’s inability to prevent accidents, particularly in critical situations. This study aims to enhance the IoV’s effectiveness by carefully selecting and improving essential attributes from various data sources, including sensors, GPS, 5G or 6G communication networks, and real-time data provisioning. To achieve the aim of the proposed study, a Multi-criterion Decision-making (MCDM) approach is proposed, which allows for the analysis and selection of optimal choices while taking into account various quantitative and qualitative factors. Despite the challenges posed by complex models and ambiguous data, MCDM remains an indispensable technique for aligning transportation systems with current expectations. The CRITIC and TOPSIS MCDM-enabled methodologies are employed to analyze IoV architecture, prioritizing significant elements that impact system performance and identifying optimal solutions by considering complications from worst-case scenarios. The study will assist engineers, scientists, and organizations to develop smart IoV systems that will cater to human needs by improving mobility and inspiration among users.