Celia Alvarez-Romero

Health data spaces are emerging as essential infrastructures for secure and interoperable data sharing across decentralized health systems. This work presents the initial deployment of FEDERA Salud, Spain’s first federated health data repository based on SIMPL, developed at the University Hospital Virgen del Rocio (HUVR) - Institute of Biomedicine of Seville (IBIS). The system enables secure, interoperable data exchange while preserving data sovereignty through a Kubernetes-based architecture and aligned with FAIR principles. It incorporates identity and access management, digital contracts and an app-to-data model to support privacy-preserving computation. Additionally, a metadata catalogue compliant with healthDCAT-AP enhances dataset discoverability and semantic interoperability, and governance adheres to GDPR and national regulations for secondary use of health data. Initial results indicate that SIMPL’s Minimum Viable Product (MVP) provides a scalable and trustworthy foundation for Spain’s contribution to the European Health Data Space.

Building federated infrastructure for European ICU data requires establishing standards for semantic interoperability across heterogeneous clinical settings. Even with standardized terminologies like LOINC and SNOMED CT, a single clinical concept may correspond to hundreds of alternative codes, precluding interoperability without explicit harmonization guidance. We developed the INDICATE Minimal Data Dictionary through an iterative, consensus-based process involving clinical experts and data scientists. The dictionary defines standardized OHDSI concept sets across nine clinical domains and supports six concrete use cases. Implementation is supported by a web-based application that provides expert-curated guidance for terminology selection and mapping. This approach reduces terminology ambiguity while remaining adaptable to local ICU practices, enabling scalable data harmonization across 15 European data providers from 12 countries.

The INDICATE project, funded by the European Union under the Digital Europe Programme, develops a federated framework for the secure reuse of intensive care unit (ICU) data within the EHDS. This work presents the design of a privacy-preserving ETL dataflow that standardizes and validates ICU data locally. The process enables harmonization of ICU data into the OMOP Common Data Model, based on HL7 FHIR as well, ensuring semantic interoperability, data quality, and regulatory compliance. Only aggregated study results are uploaded to the INDICATE Portal, ensuring that no patient-level data leave the hospital domain.

This work aims to identify the Key Research Areas for building and deploying the semantic interoperability framework for the Intensive Medicine Data Space in Europe. A set of European experts defined four research areas and associated challenges: i) to characterize the value of Common Data Models, vocabularies and standardized data elements for creating the foundation for the standardization of Intensive Care Unit (ICU) data sets, ii) to derive the Common Data Model ensuring seamless data interoperability supporting the querying and access to high-quality, multi-modal data located on federated nodes according to the FAIR principles, iii) to define and support the data standardization process enabling efficient secondary use of ICU data set during the execution of key decision support use cases through the Intensive Medicine Data Space in Europe and beyond, and iv) to define requirements for anonymization and pseudonymization for the good balance between data protection and innovation.

Fair Digital Objects (FDOs) are designed to manage complex scientific and technological data through open standards and metadata enhancing transparency and traceability. A conceptual framework has been developed to be reliably describe, locate and use any digital entity. This paper aimed to compile and analyze the literature related to FDOs applied in biomedical research, presenting a real case: the OHDO (Open Health research supports Digital Objects life-cycle) tool. Using the PRISMA-ScR methodology, a comprehensive review was conducted to identify gaps and highlight the potential of FDOs, demonstrating OHDO’s ability to leverage existing FAIR frameworks. The review showed that while FDOs improve data management and reproducibility in biomedical research, they face challenges such as technical barriers and the need for better interoperability.

Clinical practice guidelines (CPGs) are a formalization of specific clinical knowledge that states the best evidence-based clinical practices for treating pathologies. However, CPGs are limited because they are usually expressed as text. This gives rise to a certain level of ambiguity, subjective interpretation of the actions to be performed, and variability in clinical practice by different health professionals facing the same circumstances. The inherent complexity of CPGs is also a challenge for software engineers designing, developing, and maintaining software systems and clinical decision support system to manage and digitize them. This challenge stems from the need to evolve CPGs and design software systems capable of allowing their evolution. This paper proposes a model-driven, human-centric and tool-supported framework (called IDE ₄ ICDS) for improving digitisation of CPG in practical environments. This framework is designed from a human-centric perspective to be used by mixed teams of clinicians and software engineers. It was also validated with the type 2 diabetes mellitus CPG in the Andalusian Public Health System (Spain) involving 89 patients and obtaining a kappa-based analysis. The recommendations were acceptable (0.61–0.80) with a total kappa index of 0.701, leading to the conclusion that the proposal provided appropriate recommendations for each patient.

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation.

Background: Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease. Objective: This study aimed to predict alterations in the clinical condition of patients with complex chronic diseases by predicting the Barthel Index (BI), to assess their clinical and functional status using an artificial intelligence model and data collected through an internet of things mobility device. Methods: A 2-phase pilot prospective single-center observational study was designed. During both phases, patients were recruited, and a wearable activity tracker was allocated to gather physical activity data. Patients were categorized into class A (BI≤20; total dependence), class B (2060; moderate or mild dependence, or independent). Data preprocessing and machine learning techniques were used to analyze mobility data. A decision tree was used to achieve a robust and interpretable model. To assess the quality of the predictions, several metrics including the mean absolute error, median absolute error, and root mean squared error were considered. Statistical analysis was performed using SPSS and Python for the machine learning modeling. Results: Overall, 90 patients with complex chronic diseases were included: 50 during phase 1 (class A: n=10; class B: n=20; and class C: n=20) and 40 during phase 2 (class B: n=20 and class C: n=20). Most patients (n=85, 94%) had a caregiver. The mean value of the BI was 58.31 (SD 24.5). Concerning mobility aids, 60% (n=52) of patients required no aids, whereas the others required walkers (n=18, 20%), wheelchairs (n=15, 17%), canes (n=4, 7%), and crutches (n=1, 1%). Regarding clinical complexity, 85% (n=76) met patient with polypathology criteria with a mean of 2.7 (SD 1.25) categories, 69% (n=61) met the frailty criteria, and 21% (n=19) met the patients with complex chronic diseases criteria. The most characteristic symptoms were dyspnea (n=73, 82%), chronic pain (n=63, 70%), asthenia (n=62, 68%), and anxiety (n=41, 46%). Polypharmacy was presented in 87% (n=78) of patients. The most important variables for predicting the BI were identified as the maximum step count during evening and morning periods and the absence of a mobility device. The model exhibited consistency in the median prediction error with a median absolute error close to 5 in the training, validation, and production-like test sets. The model accuracy for identifying the BI class was 91%, 88%, and 90% in the training, validation, and test sets, respectively. Conclusions: Using commercially available mobility recording devices makes it possible to identify different mobility patterns and relate them to functional capacity in patients with polypathology according to the BI without using clinical parameters. Trial Registration: