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The increasing digitization of healthcare has led to vast amounts of clinical data, much of which remains underutilized for research. While Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) improves interoperability in clinical care, it's primarily designed for real-time data exchange to support diagnosis and treatment, rather than for secondary use of health data. As a result, transforming FHIR data into standardized models such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) remains a challenge. This study employs TermX, an open-source terminology and data interoperability platform designed to enhance health data interoperability and support knowledge management. This allowed us to create bidirectional transformation rules between FHIR and OMOP CDM. Using the Design Science methodology, we developed and validated a set of standardized transformation rules that support bidirectional mapping of vital signs data between FHIR and OMOP CDM. In these transformations we used synthetical FHIR JSON data, focusing on five main resources— Observation, Patient, Encounter, Organization , and Practitioner . The focus of this work is primarily on methodological mapping rather than processing real-world datasets; the evaluation concentrates on mapping coverage, i.e., the proportion of FHIR elements that can be reliably transformed into OMOP CDM structures and vice versa. The resulting rules achieved 74% mapping coverage from FHIR to OMOP CDM tables, with unmapped elements primarily related to structural discrepancies. Mapping from OMOP CDM to FHIR reached approximately 23% coverage, capturing mostly values that were previously mapped from FHIR to OMOP CDM. These percentages reflect variations in the standards' structure and granularity. The application of TermX shows the feasibility of reusable, standards-based transformations that support the secondary use of real-world clinical data for medical research and analysis. By addressing key technical and semantic interoperability challenges, this work contributes to advancing digital health interoperability and supports the objectives of the European Health Data Space.

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

Following the proposal by Tsafnat et al (2024) to converge on three open health data standards, this viewpoint offers a critical reflection on their proposed alignment of openEHR, Fast Health Interoperability Resources (FHIR), and Observational Medical Outcomes Partnership (OMOP) as default data standards for clinical care and administration, data exchange, and longitudinal analysis, respectively. We argue that open standards are a necessary but not sufficient condition to achieve health data interoperability. The ecosystem of open-source software needs to be considered when choosing an appropriate standard for a given context. We discuss two specific contexts, namely standardization of (1) health data for federated learning, and (2) health data sharing in low- and middle-income countries. Specific design principles, practical considerations, and implementation choices for these two contexts are described, based on ongoing work in both areas. In the case of federated learning, we observe convergence toward OMOP and FHIR, where the two standards can effectively be used side-by-side given the availability of mediators between the two. In the case of health information exchanges in low and middle-income countries, we see a strong convergence toward FHIR as the primary standard. We propose practical guidelines for context-specific adaptation of open health data standards.

Traditionally, the one-to-one interaction between heterogeneous software has become the most commonly used method for multi-disciplinary collaboration in building projects, resulting in numerous data interfaces, different data formats, and inefficient collaboration. As the prevalence of Building Information Modeling (BIM) increases in building projects, it is expected that the exchange of Industry Foundation Classes (IFC)-based data can smoothly take place between heterogeneous BIM software. However, interoperability issues frequently occur during bidirectional data exchanges using IFC. Hence, a data interoperability experiment, including architectural, structural and MEP models from a practical project, was conducted to analyze these issues in the process of data import and re-export between heterogeneous software. According to the results, the fundamental causes of interoperability issues can be concluded as follows: (a) software tools cannot well interpret several objects belonging to other disciplines due to the difference in domain knowledge; (b) software tools have diverse methods to represent the same geometry, properties and relations, leading to inconsistent model data. Furthermore, this paper presents a suggested method for improving the existing bidirectional data sharing and exchange: BIM software tools export models using IFC format, and these IFC models are imported into a common IFC-based BIM platform for data interoperability.

Over recent years, we have witnessed the development of mobile and wearable technologies to collect data from human vital signs and activities. Nowadays, wrist wearables including sensors (e.g., heart rate, accelerometer, pedometer) that provide valuable data are common in market. We are working on the analytic exploitation of this kind of data towards the support of learners and teachers in educational contexts. More precisely, sleep and stress indicators are defined to assist teachers and learners on the regulation of their activities. During this development, we have identified interoperability challenges related to the collection and processing of data from wearable devices. Different vendors adopt specific approaches about the way data can be collected from wearables into third-party systems. This hinders such developments as the one that we are carrying out. This paper contributes to identifying key interoperability issues in this kind of scenario and proposes guidelines to solve them. Taking into account these topics, this work is situated in the context of the standardization activities being carried out in the Internet of Things and Machine to Machine domains.

Recent natural language processing breakthroughs, particularly with the emergence of large language models (LLMs), have demonstrated remarkable capabilities on general knowledge benchmarks. However, there is limited data on the performance and understanding of these models in relation to the Fast Healthcare Interoperability Resources (FHIR) standard. The complexity and specialized nature of FHIR present challenges for LLMs, which are typically trained on broad datasets and may have a limited understanding of the nuances required for domain-specific tasks. Improving health data interoperability can greatly benefit the use of clinical data and interaction with electronic health records. This study presents the Fast Healthcare Interoperability Resources (FHIR) Workbench, a comprehensive suite of datasets designed to evaluate the ability of LLMs to understand and apply the FHIR standard. In total, 4 evaluation datasets were created to assess the FHIR knowledge and capabilities of LLMs. These tasks include multiple-choice questions on general FHIR concepts and the FHIR Representational State Transfer (REST) application programming interface, as well as correctly identifying the resource type and generating FHIR resources from unstructured clinical patient notes. In addition, we evaluate open-source LLMs, such as Qwen 2.5 Coder and DeepSeek-V3, and commercial LLMs, including GPT-4o and Gemini 2, on these tasks in a zero-shot setting. To provide context for interpreting LLM performance, a subset of the datasets was human-evaluated by recruiting 6 participants with varying levels of FHIR expertise. Our evaluation across multiple FHIR tasks revealed nuanced performance metrics. Commercial models demonstrated exceptional capabilities, with GPT-4o achieving a 0.9990 F1-score on the FHIR-ResourceID task, 0.9400 on the FHIR-QA task, and 0.9267 on the FHIR-RESTQA task. Open-source models also demonstrated strong performance, with DeepSeek-v3 achieving 0.9400 on FHIR-QA, 0.9400 on FHIR-RESTQA, and 0.9142 on FHIR-ResourceID. Qwen 2.5 Coder-7B-Instruct demonstrated high accuracy, scoring 0.9533 on FHIR-QA and 0.8920 on FHIR-ResourceID. However, all models struggled with the Note2FHIR task, with performance ranging from 0.0382 (OLMo) to a maximum of 0.3633 (GPT-4.5-preview), highlighting the significant challenge of converting unstructured clinical text into FHIR-compliant resources. Human participants achieved accuracy scores ranging from 0.50 to 1.0 across the first 3 tasks. This study highlights the competitive performance of both open-source models, such as Qwen and DeepSeek, and commercial models, such as GPT-4o and Gemini, in FHIR-related tasks. While open-source models are advancing rapidly, commercial models still have an advantage for specific, complex tasks. The FHIR Workbench offers a valuable platform for evaluating the capabilities of these models and promoting improvements in health data interoperability.

This study delves into the dynamics of pragmatic interoperability, focusing on the case of a digital ecosystem in India —the eKrishi platform—which combines of industry 4.0 technologies with human-centric principles. Through qualitative analysis, we unveil the motivations shaping system and business-level interoperability alignment. We found that three categories of sustainability metrics—socio-economic, socio-ecological, and eco-efficiency— are driven by diverse pragmatic views. Furthermore, we found that system-level alignment is driven by actors’ defensive strategy for compliance and standardization, while business level interoperability is underpinned by actors’ offensive strategy for social and economic innovation. The study introduces a 2 × 2 alignment framework—corporate citizenship, regulatory stewardship, corporate stewardship, and value chain stewardship—offering nuanced insights. By aligning systems and business motives for pragmatic interoperability, we contribute towards theory building on interoperability and provide practical implications for guiding stakeholder alignment in Industry 4.0 initiatives.

The widespread use of electronic health records (EHRs) and healthcare information systems (HISs) has led to isolated data silos across healthcare providers, and current interoperability standards like FHIR cannot address some scenarios. For instance, it cannot retrieve patients’ health records if they are stored by multiple healthcare providers with diverse interoperability standards or the same standard but different implementation guides. FHIR and similar standards prioritize institutional interoperability rather than patient-centered interoperability. We explored the challenges in transforming fragmented data silos into patient-centered data interoperability. This research comprehensively reviewed 56 notable studies to analyze the challenges and approaches in patient-centered interoperability through qualitative and quantitative analyses. We classified the challenges into four domains and categorized common features of the propositions to patient-centered interoperability into six categories: EMR integration, EHR usage, FHIR adaptation, blockchain application, semantic interoperability, and personal data retrieval. Our results indicated that “using blockchain” (48%) and “personal data retrieval” (41%) emerged as the most cited features. The Jaccard similarity analysis revealed a strong synergy between blockchain and personal data retrieval (0.47) and recommends their integration as a robust approach to achieving patient-centered interoperability. Conversely, gaps exist between semantic interoperability and personal data retrieval (0.06) and between FHIR adaptation and personal data retrieval (0.08), depicting research opportunities to develop unique contributions for both combinations. Our data-driven insights provide a roadmap for future research and innovation.

In recent years, Internet-of-Things (IoT)-based applications have been used in various domains such as health, industry and agriculture. Considerable amounts of data in diverse formats are collected from wireless sensor networks (WSNs) integrated into IoT devices. Semantic interoperability of data gathered from IoT devices is generally being carried out using existing sensor ontologies. However, crop-specific trait ontologies—which include site-specific parameters concerning hazelnut as a particular agricultural product—can be used to make links between domain-specific variables and sensor measurement values as well. This research seeks to address how to use crop-specific trait ontologies for linking site-specific parameters to sensor measurement values. A data-integration approach for semantic and syntactic interoperability is proposed to achieve this objective. An open-data platform is developed and its usability is evaluated to justify the viability of the proposed approach. Furthermore, this research shows how to use web services and APIs to carry out the syntactic interoperability of sensor data in agriculture domain.

Water utilities collect, store and manage vast data sets using many information systems (IS). For infrastructure asset management (IAM) planning those data need to be processed and transformed into information. However, information management efficiency often falls short of desired results. This happens particularly in municipalities where management is structured according to local government models. Along with the existing IS at the utilities' disposal, engineers and managers take their decisions based on information that is often incomplete, inaccurate or out-of-date. One of the main challenges faced by asset managers is integrating the several, often conflicting, sources of information available on the infrastructure, its condition and performance, and the various predictive analyses that can assist in prioritizing projects or interventions. This paper presents an overview of the IS used by Portuguese water utilities and discusses how data from different IS can be integrated in order to support IAM.