Ethical framework for FACILITATE: a foundation for the return of clinical trial data to participants.

This paper discusses the importance of return of clinical trial data to patients in the context of the FACILITATE project that aims to develop a participant-centric approach for the systematic return of individual clinical trial data. It reflects on the need for an ethical framework to support the return of clinical trial data. The discussion revolves around the developing FACILITATE ethical framework, specifically focusing on the ethical principles that form the foundation of the framework and guidance on how to implement those principles into practice.

Improving uptake of population health management through scalable analysis of linked electronic health data.

Population Health Management - often abbreviated to PHM - is a relatively new approach for healthcare planning, requiring the application of analytical techniques to linked patient level data. Despite expectations for greater uptake of PHM, there is a deficit of available solutions to help health services embed it into routine use. This paper concerns the development, application and use of an interactive tool which can be linked to a healthcare system's data warehouse and employed to readily perform key PHM tasks such as population segmentation, risk stratification, and deriving various performance metrics and descriptive summaries. Developed through open-source code in a large healthcare system in South West England, and used by others around the country, this paper demonstrates the importance of a scalable, purpose-built solution for improving the uptake of PHM in health services.

Will older adults be represented in patient-reported data? Opportunities and realities.

Policy makers and health professionals are grappling with the high costs of and demand for health care, questions of sustainability and value, and changing population demographics-in particular, ageing populations. Digital solutions, including the adoption of patient-reported measures, are considered critical in achieving person-centred and value-based health care. However, the utility of patient-reported measures and the data they produce may be subject to ageist beliefs, prejudices and attitudes, rendering these data ineffective at promoting improved patient experiences and outcomes for older adults. This article explores the ethical considerations raised in relation to patient-reported measures and the digital agency of older patients.

Patient Input into the Electronic Health Record: Co-Designing Solutions with Patients and Healthcare Professionals.

Patient-generated health data (PGHD) is the person's health-related data collected outside the clinical environment. Integrating this data into the electronic health record (EHR) supports better patient-provider communication and shared decision-making, empowering patients to actively manage their health conditions. In this study, we investigated the essential features needed for patients and healthcare providers to effectively integrate PGHD functionality into the EHR system. Through our collaborative design approach involving healthcare professionals (HCPs) and patients, we developed a prototype and suggestion, using Estonia as a model, which is the ideal approach for collecting and integrating PGHD into the EHR.

Education Challenges Among Early Career Researchers in Medical Informatics.

There is limited knowledge about early career researchers' challenges when studying the interdisciplinary field of Medical Informatics (MI). We conducted a qualitative content analysis through semi-structured interviews with early career researchers in MI, including individuals pursuing Master's, PhD, and postdoctoral research programmes, across two higher education institutions in the UK. We identified five challenges, including understanding biological jargon, interpreting biological data, interdisciplinary communication, understanding mathematical/statistical concepts, and programming difficulties. These insights and suggested actions to address those challenges can help to improve MI education.

The Ouest Data Hub: An Interregional Health Data Sharing Ecosystem for Research.

The Ouest Data Hub (ODH) a project lead by GCS HUGO which is a cooperation group of University Hospitals in the French Grand Ouest region represents a groundbreaking initiative in this territory, advancing health data sharing and reuse to support research driven by real-world health data. Central to its structure are the Clinical Data Warehouses (CDWs) and Clinical Data Centers (CDCs), essential for analytics and as the linchpin of the ODH's status as an interregional Learning Health System. Aimed at fostering innovation and research, the ODH's collaborative and multi-institutional model effectively utilizes both local and shared resources. Yet, the path is not without challenges, especially in data quality and interoperability, where ongoing harmonization and standard adherence are critical. In 2023, this facilitated access to extensive health data from over 9.3 million patient records, demonstrating the ODH's capacity for both monocentric and multicentric research across various clinical fields, in close collaboration with physicians. The integration of healthcare professionals is crucial, ensuring data's clinical relevance and guiding accurate interpretations. Future expansions of the ODH to new hospitals and data types promise to enhance its model further, already inspiring similar frameworks across France. This scalable model for health data ecosystems showcases the ODH's potential as a foundation for national and supranational data sharing efforts.

Health Data Re-Identification: Assessing Adversaries and Potential Harms.

Sharing biomedical data for research can help to improve disease understanding and support the development of preventive, diagnostic, and therapeutic methods. However, it is vital to balance the amount of data shared and the sharing mechanism chosen with the privacy protection provided. This requires a detailed understanding of potential adversaries who might attempt to re-identify data and the consequences of their actions. The aim of this paper is to present a comprehensive list of potential types of adversaries, motivations, and harms to targeted individuals. A group of 13 researchers performed a three-step process in a one-day workshop, involving the identification of adversaries, the categorization by motivation, and the deduction of potential harms. The group collected 28 suggestions and categorized them into six types, each associated with several of six distinct harms. The findings align with previous efforts in structuring threat actors and outcomes and we believe that they provide a robust foundation for evaluating re-identification risks and developing protection measures in health data sharing scenarios.

Distributed Ledger-Based System to Support Health Data Integrity and Transparency.

This article addresses critical health data integrity by proposing an HF (Hyperledger Fabric)-based architecture with integration into the global health data architecture based on distributed content-addressable storage networks.

Synergies Among Health Data Projects with Cancer Use Cases Based on Health Standards.

We are creating a synergy among European Health Data Space projects (e.g., IDERHA, EUCAIM, ASCAPE, iHELP, Bigpicture, and HealthData@EU pilot project) via health standards usage thanks to the HSBOOSTER EU Project since they are involved or using standards, and/or designing health ontologies. We compare health-standardized models/ontologies/terminologies such as HL7 FHIR, DICOM, OMOP, ISO TC 215 Health Informatics, W3C DCAT, etc. used in those projects.

Childhood Cancer Survivorship Passport Challenges in the European Health Data Space.

Innovation in cancer therapy has increased childhood cancer survival rates. However, survivors are still at risk of developing late effects. In the digital transformation of the health sector, the Survivorship Passport (SurPass) can support long-term follow-up care plans. Gaps in seamless connectivity among hospital departments, primary care, combined with the time of health professionals required to collect and fill-in health data in SurPass, are barriers to its adoption in daily clinical practice. The PanCareSurPass (PCSP) project was motivated to address these gaps by a new version of SurPass (v2.0) that supports semi-automatic assembly from organizational Electronic Health Record (EHR) systems of the treatment summary data using HL7 FHIR, to create SurPass, and to link it to regional or national digital health infrastructures in six European countries. In this paper we present the methodology used to develop the SurPass technical implementation strategy with special focus on the European Health Data Space (EHDS). The recently provisionally approved EHDS regulation instruments a digital health data ecosystem with opportunities for cost-effective SurPass implementation across Europe. Moving forward, a European HL7 FHIR SurPass Implementation Guide along with synthetic data sets, and validation tools can enrich the European Electronic Health Record Exchange Format (EEHRxF) with use cases on health & wellness of childhood cancer survivors.

Review of Key Elements in Developing a Common Data Model for Rare Diseases: Identifying Common Success Factors.

This paper explores key success factors for the development and implementation of a Common Data Model (CDM) for Rare Diseases (RDs) focusing on the European context. Several challenges hinder RD care and research in diagnosis, treatment, and research, including data fragmentation, lack of standardisation, and Interoperability (IOP) issues within healthcare information systems. We identify key issues and recommendations for an RD-CDM, drawing on international guidelines and existing infrastructure, to address organisational, consensus, interoperability, usage, and secondary use challenges. Based on these, we analyse the importance of balancing the scope and IOP of a CDM to cater to the unique requirements of RDs while ensuring effective data exchange and usage across systems. In conclusion, a well-designed RD-CDM can bridge gaps in RD care and research, enhance patient care and facilitate international collaborations.

HCE2RNFC: An Efficient Methodology for Reusing the EHR in the Spanish National Hip Fracture Registry.

Hip fracture is a condition associated with ageing and frailty, with an associated prevalence of 7 per 10000 population in Spain. Evidence suggests that factors in the healthcare process can influence clinical outcomes, so the creation of a national registry is an opportunity to monitor and improve this process. In this regards, Electronic Health Record (EHR) can provide a large amount of data, that can be used to populate the Spanish National Hip Fracture Registry (RNFC, by its acronym in Spanish). However, this reuse of the EHR requires a prior effort in modelling and standardization to build the extraction, transformation, and loading (ETL) processes in a flexible, transparent, and scalable manner. In this work, a robust EHR reuse methodology is implemented to obtain EHR-derived data for the RNFC. The main result of this work was the design and implementation of an EHR data reuse methodology, which was able to load 1279 hip fracture cases and almost 68% of the required concepts from the RNFC.

Privacy-Preserving Linkage of Distributed Pseudonymised Datasets in a Virtual European Rare Disease Platform.

Secondary use of data for research purposes is especially important in rare diseases (RD), since, per definition, data are sparse. The European Joint Programme on Rare Diseases (EJP RD) aims at developing an RD infrastructure which supports the secondary use of data. Significant amounts of RD data are a) distributed and b) available only in pseudonymised format. Privacy-Preserving Record Linkage (PPRL) concerns the linking of such distributed datasets without disclosing the participant's identities. We present a concept for linking a PPRL Service to the EJP RD Virtual Platform (VP). Level 1 (resource discovery) connection is provided by running an FDP within the PPRL Service. On Level 2 (data discoverability), the PPRL Service can represent both, an individual and a catalog endpoint. Our solution can count patients in PPRL-supporting resources, count duplicates only once, and count only patients registered to multiple resources. Currently, we are preparing the deployment within the EJP RD VP.

Nordic Citizens' Willingness to Share Digital Health Data.

The quality of the digital healthcare systems relies on citizens' willingness to share their digital health data. This makes citizens' use, perceptions, and attitudes towards digital healthcare systems pivotal. The study presented here examines Nordic citizens' willingness to share digital health data with healthcare providers and for research purposes. A cross-sectional study design was applied to obtain answers from citizens in Denmark, Finland, Iceland, Norway, and Sweden. The results are based on answers from 5078 citizens across the five countries. Results based on descriptive statistics indicate that the majority of Nordic citizens are willing to share health data that has clinical relevance with healthcare providers and for research purposes. The odds ratio analysis reveals that citizens' odds of sharing health data decreases with age and increases with the level of education. Conclusively, this study shows that most Nordic citizens are willing to share their health data, influenced by age and level of education. Awareness of and efforts to support citizens who are unable or unwilling to actively use and engage with the digital healthcare system is recommended.

Usability Evaluation of a Web-Based Remote Patient Monitoring Application: An Ongoing Study.

This study aims to discover problems and user experiences in a new released version of Sleepiz web application using heuristic evaluation and eye-tracking retrospective think-aloud performed by domain experts and end users. The web application is designed to support healthcare professionals in decision-making and monitoring of elderly people diagnosed with chronic respiratory diseases. Identification of usability problems and user experiences might contribute to improve the platform and will be reported to the developers.

Univie: An App for Managing Digital Health Data of Refugees.

The refugee crisis has reached historic proportions, with more than 82 million people on the run. Access to healthcare is often difficult for them due to a lack of medical records and language barriers. This paper examines a digital medical documentation system for refugees that captures, stores, and translates records. International data protection standards are considered. The contribution consists of designing a system that manages and translates medical data across borders and integrates a prediction model for epidemics in refugee camps.

Synthesizing Clinical Data Warehouse Content for Enhanced External Collaboration: A Preliminary Proposal.

This paper introduces a novel approach aimed at enhancing the accessibility of clinical data warehouses (CDWs) for external users, particularly researchers and biomedical companies interested in developing and testing their solutions. The primary focus is on proposing a clinical data catalogue designed to elucidate the contents of CDWs, facilitating biomedical project launch and completion. The catalogue is designed to address three fundamental inquiries that external users may have regarding CDWs: "What data is available, how much data is present, and how was it generated?" Additionally, the paper showcases a prototype of the catalogue through a visualization example, utilizing data from the CDW of Rennes University Hospital.

Improving Healthcare Quality with a LHS: From Patient-Generated Health Data to Evidence-Based Recommendations.

One approach to enriching the Learning Health System (LHS) is leveraging vital signs and data from wearable technologies. Blood oxygen, heart rate, respiration rates, and other data collected by wearables (like sleep and exercise patterns) can be used to monitor and predict health conditions. This data is already being collected and could be used to improve healthcare in several ways. Our approach will be health data interoperability with HL7 FHIR (for data exchange between different systems), openEHR (to store researchable data separated from software but connected to ontologies, external terminologies and code sets) and maintain the semantics of data. OpenEHR is a standard that has an important role in modelling processes and clinical decisions. The six pillars of Lifestyle Medicine can be a first attempt to change how patients see their daily decisions, affecting the mid to long-term evolution of their health. Our objective is to develop the first stage of the LHS based on a co-produced personal health recording (CoPHR) built on top of a local LLM that interoperates health data through HL7 FHIR, openEHR, OHDSI and terminologies that can ingest external evidence and produces clinical and personal decision support and, when combined with many other patients, can produce or confirm evidence.

An In-Depth Exploration of Mapping openEHR and PGHD: A Case Study on Fitbit-Generated Data.

In recent years, the adoption of wearable gadgets such as Fitbit has revolutionized the way individuals track and monitor their personal activity data. These devices provide valuable in-sights into an individual's physical activity levels, sleep patterns, and overall health metrics. Integrating this data into healthcare informatics systems can offer significant benefits in terms of personalized healthcare delivery and improved patient outcomes. This paper explores the synergistic integration of Fitbit-generated personal activity data using the openEHR Reference Model in healthcare informatics as a practical case study in patient-generated health data (PGHD) integration based on health informatics standards as a framework for the representation and exchange of Electronic Health Records (EHRs). The synergistic integration of Fitbit-generated personal activity data through openEHR and FHIR standards models also covers the way for advanced analytics and population health management. By linking and analyzing data from various sources, including sensors and wearable devices, healthcare organizations can identify trends, patterns, and insights that can guide population health strategies, preventive care initiatives, and personalized treatment plans, in addition to aiding physicians in follow-up care.

Childhood Maltreatment and Perinatal Complications: A Scoping Review of Official Health Data.

Child maltreatment (CM) poses significant risks to victims, resulting in enduring physical, psychological, and developmental consequences. Adult survivors of CM seem especially vulnerable to perinatal complications. However, existing research on perinatal outcomes presents mixed results and relies heavily on self-reported data, which may not align with official medical data. Hence, a systematic review using official health data may provide clarity on this association; it may orient future research and the provision of perinatal services. This scoping review aimed to synthesize and evaluate the quality of the literature that utilizes official health data to explore associations between CM and perinatal complications. Following Arksey and O'Malley's model, searches across four databases (PsycINFO, MEDLINE, Scopus, and ProQuest Dissertations/Thesis) produced 8,870 articles. After screening, 23 articles met the inclusion criteria (e.g., recorded perinatal complications using official health data, and peer-reviewed studies or dissertation). Evidence indicates CM survivors have less prenatal care visits, more fetal loss and preterm births, lower gestational age, and increases in emergency cesarean sections. Adults had more cervical insufficiency, lower episiotomies and sphincter ruptures, and overall pregnancy and postpartum complications while adolescents had lower Apgar scores. No associations were observed on other outcomes (e.g., vaginal bleeding, group B streptococcus, and fetal distress). Mixed findings emerged for other perinatal and maternal health concerns such as birth weight and blood pressure. CM survivors may face an increased risk of experiencing perinatal complications. Findings point to the relevance of leveraging health data for CM research and adopting trauma-informed practices in perinatal services.