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Tensor decomposition has recently been gaining attention in the machine learning community for the analysis of individual traces, such as Electronic Health Records. However, this task becomes significantly more difficult when the data follows complex temporal patterns. This paper introduces the notion of a temporal phenotype as an arrangement of features over time and it proposes SWoTTeD (Sliding Window for Temporal Tensor Decomposition), a novel method to discover hidden temporal patterns. SWoTTeD integrates several constraints and regularizations to enhance the interpretability of the extracted phenotypes. We validate our proposal using both synthetic and real-world datasets, and we present an original usecase using data from the Greater Paris University Hospital. The results show that SWoTTeD achieves at least as accurate reconstruction as recent state-of-the-art tensor decomposition models, and extracts temporal phenotypes that are meaningful for clinicians. [ABSTRACT FROM AUTHOR]

Objective: Both Indigenous and non-Indigenous governments and organizations have increasingly called for improved Indigenous health data in order to improve health equity among Indigenous peoples. This scoping review identifies best practices, potential consequences and barriers for advancing Indigenous health data and Indigenous data sovereignty globally. Methods: A scoping review was conducted to capture the breadth and nature of the academic and grey literature. We searched academic databases for academic records published between 2000 and 2021. We used Google to conduct a review of the grey literature. We applied Harfield's Aboriginal and Torres Strait Islander Quality Appraisal Tool (QAT) to all original research articles included in the review to assess the quality of health information from an Indigenous perspective. Results: In total, 77 academic articles and 49 grey literature records were included. Much of the academic literature was published in the last 12 years, demonstrating a more recent interest in Indigenous health data. Overall, we identified two ways for Indigenous health data to be retrieved. The first approach is health care organizations asking clients to voluntarily self-identify as Indigenous. The other approach is through data linkage. Both approaches to improving Indigenous health data require awareness of the intergenerational consequences of settler colonialism along with a general mistrust in health care systems among Indigenous peoples. This context also presents special considerations for health care systems that wish to engage with Indigenous communities around the intention, purpose, and uses of the identification of Indigenous status in administrative databases and in health care settings. Partnerships with local Indigenous nations should be developed prior to the systematic collection of Indigenous identifiers in health administrative data. The QAT revealed that many research articles do not include adequate information to describe how Indigenous communities and stakeholders have been involved in this research. Conclusion: There is consensus within the academic literature that improving Indigenous health should be of high priority for health care systems globally. To address data disparities, governments and health organizations are encouraged to work in collaboration with local Indigenous nations and stakeholders at every step from conceptualization, data collection, analysis, to ownership. This finding highlights the need for future research to provide transparent explanation of how meaningful Indigenous collaboration is achieved in their research. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative analysis of data governance mechanisms concerning the secondary use of health data in Taiwan and the European Union (EU). Both regions have adopted distinctive approaches and regulations for utilizing health data beyond primary care, encompassing areas such as medical research and healthcare system enhancement. Through an examination of these models, this study seeks to elucidate the strategies, frameworks, and legal structures employed by Taiwan and the EU to strike a delicate balance between the imperative of data-driven healthcare innovation and the safeguarding of individual privacy rights. This paper examines and compares several key aspects of the secondary use of health data in Taiwan and the EU. These aspects include data governance frameworks, legal and regulatory frameworks, data access and sharing mechanisms, and privacy and security considerations. This comparative exploration offers invaluable insights into the evolving global landscape of health data governance. It provides a deeper understanding of the strategies implemented by these regions to harness the potential of health data while upholding the ethical and legal considerations surrounding its secondary use. The findings aim to inform best practices for responsible and effective health data utilization, particularly in the context of medical AI applications. [ABSTRACT FROM AUTHOR]

We report on a qualitative Group Survey study involving four healthcare professionals (HCPs) and eight people living with HIV who were recipients of care in the United Kingdom (UK). The survey aimed to bring participants' perspectives into dialogue and establish consensus about how communication between HCPs delivering HIV care and their patients could be improved in the context of the routine care consultation. Responses from both parties were anonymously collated, thematically analysed, and shared back with participants in two subsequent survey rounds to support consensus-building on matters of concern and identify thematic insights. In this paper, we report three themes for informing future designs of tools and services to support communication between patients and HCPs: Patient-clinician relationship for trusted sharing; Self-reporting psychosocial information to support Whole-person care; and Perceived barriers for online trusted sharing with HCPs. Our findings highlight key areas of concern and further investigation is needed to understand how self-reported information may be meaningfully captured, interpreted and processed by HCPs in ways that are trusted by patients who voice privacy and security concerns. [ABSTRACT FROM AUTHOR]

Purpose: The use of wearable devices to monitor aspects of personal health is increasing. The Fitbit is an example of a popular device used for this purpose. It is unknown whether users' privacy (i.e. sensitive data collected from wearable devices) would be leaked via unauthorized access. So, this investigation will answer the following questions; are the data transmissions protected against unauthorised access or modification? what data are transmitted between the device and the server? how much data can be collected by unauthorized access? Method: This paper describes an investigation into data access in the Fitbit Blaze and, specifically, whether this is possible without connecting to the Fitbit server. A Man-In-The-Middle (MITM) attack was used in this investigation. Result: In this experiment, the firmware image, transferred when the device connects to the Fitbit server, is first captured and analysed to obtain data. This was done to attempt to identify the encryption method and obtain the unique device MAC address. Secondly, some fitness data, namely, the authentication key, the cryptographic key and the Nonce, were extracted from the Fitbit application. We attempted to connect the Fitbit Blaze device and the Fitbit application directly without connecting via the Fitbit server. We also attempted direct access to the Fitbit Blaze using a charger cable. In addition, Fitbit Java files were extracted from the Fitbit application. Conclusion: Finally, the outcomes of this investigation are compared with investigations into other Fitbit devices in the previous research. [ABSTRACT FROM AUTHOR]

With the development of cross-border telemedicine, international cooperation in clinical trials of drugs and vaccines, cross-border academic discussions and clinical research, cross-border flow of health and medical data has become an irreversible trend. Rules for cross-border flow of health data require doing a balance among the objectives and policies of maintaining national health security, protecting citizens' privacy, and promoting the development of health industry. This paper uses literature research, comparative analysis and other methods to study the legislative regulation and regulatory mechanism of cross-border health data in Australia. The study's findings reveal that Australia prioritizes health data security, enforcing stringent oversight on the cross-border movement of personally identifiable health data through dedicated agencies and comprehensive legislation. China should build a scientific, complete and strict legal and policy system for health data protection and cross-border flow, and organically combine and implement laws and regulations with policy guidelines; improve the terms of standard contract for health data exit and play the function of the standard contract for regulation; actively use the existing hierarchical classification of health data to build a flexible and diverse health data exit regulation model; and strengthen exchanges and cooperation with countries along the "Belt and Road", especially ASEAN member countries, in the field of cross-border health data flow.

The creation of the European Health Data Space is a milestone. It establishes something new and makes it possible to have faster and more efficient health treatment, but also to untap the potential of unused data for policy-making and development of new technologies. However, for the EHDS to be functional, it is imperative that a balance it struck between the aim of having useful data to achieve important public objectives on one side and the goal of enabling patients to have control over their health data. The original Draft Report of the European Parliament came closest to achieving these objectives and should have represented the blueprint for the final compromise. Having a harmonised opt-out for secondary use is crucial for striking the right balance between the mentioned objectives. The end result is far from perfect regarding primary use, but represents a good balance in the area of secondary use concerning the opt-out. [ABSTRACT FROM AUTHOR]

Abstract Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English, this manuscript focuses on predicting disease categories automatically from symptoms documented in the Afaan Oromo language, employing various classification algorithms. This study encompasses machine learning techniques such as support vector machines, random forests, logistic regression, and Naïve Bayes, as well as deep learning approaches including LSTM, GRU, and Bi-LSTM. Due to the unavailability of a standard corpus, we prepared three data sets with different numbers of patient symptoms arranged into 10 categories. The two feature representations, TF-IDF and word embedding, were employed. The performance of the proposed methodology has been evaluated using accuracy, recall, precision, and F1 score. The experimental results show that, among machine learning models, the SVM model using TF-IDF had the highest accuracy and F1 score of 94.7%, while the LSTM model using word2vec embedding showed an accuracy rate of 95.7% and F1 score of 96.0% from deep learning models. To enhance the optimal performance of each model, several hyper-parameter tuning settings were used. This study shows that the LSTM model verifies to be the best of all the other models over the entire dataset.

Abstract Background Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. Methods In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. Results In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and “other” geographical scope were ranked at the bottom of the list with the lowest proportion. Responders’ willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. Conclusions The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.

Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patientcentric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions. [ABSTRACT FROM AUTHOR]

The growth of wearable technology has enabled the collection of even more personalized information on individuals. New health-related devices marketed to consumers collect health information that might not fall under the traditional category of Protected Health Information, and thus, HIPAA protections do not fully apply. Meaning, commercial wearable health devices do not fall under FDA oversight, and data not paired with a doctor–patient relationship do not fall under HIPAA privacy protection; thus, much of the gathered health-related metrics are left without regulation and open to be sold to data brokers. As such, these data can be leveraged by health insurance, law enforcement, and employers, to name a few. This manuscript explores the loopholes in current regulations and suggests a framework that categorizes wearable data and addresses challenges in data transfer. Furthermore, taking a user perspective, the suggested framework offers solutions that aim to guide users and policymakers in navigating privacy issues in wearable technology. [ABSTRACT FROM AUTHOR]

L'intelligence artificielle (IA) se développe à une vitesse impressionnante dans le domaine de la santé. L'IA ouvre la voie à de nouvelles pratiques d'aide au diagnostic, de médecine préventive, de suivi individualisé du patient, d'appui au choix d'une thérapie, d'aide à l'interprétation de résultats d'examens complémentaires... Loin d'être une solution miracle, la délégation de tâches à l'IA soulève de nombreuses questions éthiques portant notamment sur la supervision des algorithmes, l'égalité d'accès à l'innovation et l'utilisation des données des patients. Il est encore difficile de prédire si l'IA restera un « filet de sécurité » pour le médecin ou si elle le supplantera. La France et l'Europe ont posé le principe de « garantie humaine » à l'interprétation des résultats diagnostiques et thérapeutiques, impliquant que la machine ne peut agir seule et consacrant la responsabilité du professionnel de santé. Cet article vise à comprendre à quel point l'IA est complexe à encadrer au plan juridique au risque d'en négliger le secret, que ce soit au plan individuel ou collectif. Artificial intelligence (AI) is developing at an impressive speed in the field of health. AI paves the way for new practices in diagnostic assistance, preventive medicine, individualized patient follow-up, support in choosing a therapy, assistance in interpreting the results of additional examinations, etc. Far from being a magic bullet, delegating tasks to AI raises many ethical questions relating in particular to the supervision of algorithms, equal access to innovation and the use of patient data. It is still difficult to predict whether AI will remain a "safety net" for the doctor or if it will supplant him. France and Europe have laid down the principle of "human guarantee" in the interpretation of diagnostic and therapeutic results, implying that the machine cannot act alone and enshrining the responsibility of the health professional. This article aims to understand how complex AI is to regulate legally at the risk of neglecting its secrecy, whether at the individual or collective level. [ABSTRACT FROM AUTHOR]

In 2019, the International Consortium for Personalised Medicine (ICPerMed) developed a vision on how the use of personalized medicine (PM) approaches will promote "next-generation" medicine in 2030 more firmly centered on the individual's personal characteristics, leading to improved health outcomes within sustainable healthcare systems through research, development, innovation, and implementation for the benefit of patients, citizens, and society. Nevertheless, there are significant hurdles that healthcare professionals, researchers, policy makers, and patients must overcome to implement PM. The ICPerMed aims to provide recommendations to increase stakeholders' awareness on actionable measures to be implemented for the realization of PM. Starting with best practice examples of PM together with consultation of experts and stakeholders, a careful analysis that underlined hurdles, opportunities, recommendations, and information, aiming at developing knowledge on the requirements for PM implementation in healthcare practices, has been provided. A pragmatic roadmap has been defined for PM integration into healthcare systems, suggesting actions to overcome existing barriers and harness the potential of PM for improved health outcomes. In fact, to facilitate the adoption of PM by diverse stakeholders, it is mandatory to have a comprehensive set of resources tailored to stakeholder needs in critical areas of PM. These include engagement strategies, collaboration frameworks, infrastructure development, education and training programs, ethical considerations, resource allocation guidelines, regulatory compliance, and data management and privacy. [ABSTRACT FROM AUTHOR]

The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes. [ABSTRACT FROM AUTHOR]

Telemedicine is considered to be the result of the combination of rapid developments in the two specialized fields of information technology and medicine, and observing the favorable and rapid effects of this technology has encouraged the managers of health systems in many countries, including Iran, to expand it. However, despite the advantages that this technology can have for a country like Iran, this technology did not have the expected expansion in Iran, and for this reason, the current research sought to formulate a model for the expansion of telemedicine in the field of treatment. Previous researches, with much more limited areas, have focused more on Davis's technology acceptance model and the theory of logical action. In this research, which was conducted with a user-centred approach, using the opinions of experts and the fuzzy Delphi method (in the first part) and the validation of the resulting model by operational experts (in the second part), finally, a model for expanding this technology with 4 main factors effective and 14 components of those factors were obtained and the validity of the resulting model was also confirmed by several different criteria, including the goodness of fit criterion. The results showed that the component of resistance to use among cultural and social factors has the most significant impact on the development of this technology, and this resistance was observed mostly on the side of doctors and treatment service providers. After that, the amount and type of allocation of financial resources and government policies are in the next ranks. The least significant impact was attributed to the perceived risk. The results showed that the components of attitude and the expected performance had no significant effect on the development of telemedicine and the most important factors affecting the development of this technology are in the area of authority of the government and policymakers of the health system. [ABSTRACT FROM AUTHOR]

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector‐borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large‐scale analyses from geopolitical frameworks to more ecologically meaningful regions. Plain Language Summary: In the United States (US), geopolitical subdivisions like states and counties are the governing regions that collect public health data and implement health interventions like risk messaging, mask mandates, or spraying chemicals for mosquito control. These subdivisions were mostly decided by politics and other human activity, not similarities in ecosystems and climate. This can make it difficult to understand how climate affects illnesses like mosquito‐borne disease since mosquitoes will live within specific ecosystems and aren't confined to geopolitical boundaries as health data are. We explored this idea by analyzing the relationships between temperature, precipitation, and the mosquito‐borne pathogen called West Nile virus (WNV) from 2005 to 2019. We grouped our data using two different regional boundaries: (a) climate regions from the National Oceanic and Atmospheric Administration (NOAA), primarily based on state boundaries, and (b) ecoregions from the Environmental Protection Agency (EPA), based on similar ecosystems. We found that these groupings resulted in some contradictory results. The results using EPA ecoregions more often agreed with biological hypotheses, so it may be a better approach to dividing the US for similar studies. We encourage other scientists to consider this conceptual shift from analyzing data by geopolitical boundaries to more ecologically meaningful regions. Key Points: While health data is collected within geopolitical boundaries, environmental disease dynamics are influenced by ecosystem characteristicsFor West Nile virus, correlations between cases and climate were different depending on geopolitical or ecosystem regional groupings of dataWe propose a conceptual shift from analyzing climate and health data at geopolitical boundaries to more ecologically meaningful regions [ABSTRACT FROM AUTHOR]

Automated disease diagnosis and prediction, powered by AI, play a crucial role in enabling medical professionals to deliver effective care to patients. While such predictive tools have been extensively explored in resource-rich languages like English, this manuscript focuses on predicting disease categories automatically from symptoms documented in the Afaan Oromo language, employing various classification algorithms. This study encompasses machine learning techniques such as support vector machines, random forests, logistic regression, and Naïve Bayes, as well as deep learning approaches including LSTM, GRU, and Bi-LSTM. Due to the unavailability of a standard corpus, we prepared three data sets with different numbers of patient symptoms arranged into 10 categories. The two feature representations, TF-IDF and word embedding, were employed. The performance of the proposed methodology has been evaluated using accuracy, recall, precision, and F1 score. The experimental results show that, among machine learning models, the SVM model using TF-IDF had the highest accuracy and F1 score of 94.7%, while the LSTM model using word2vec embedding showed an accuracy rate of 95.7% and F1 score of 96.0% from deep learning models. To enhance the optimal performance of each model, several hyper-parameter tuning settings were used. This study shows that the LSTM model verifies to be the best of all the other models over the entire dataset. [ABSTRACT FROM AUTHOR]

The notion of transparency regularly comes up in discussions about the secondary uses of health data. Few studies, however, examine the impact of the presence or absence of transparency on members of the public. This literature review responds to this gap in the scientific literature. It is the result of a secondary analysis of 124 texts from an exploratory review on transparency, following the PRISMAS-ScR guidelines. The results help to identify negative or positive impacts and associate these with certain communication components relating to secondary uses of health data. They also make it possible to identify the components associated with communication deemed transparent or opaque by stakeholders. Transparency, and more specifically continuity of communication, is strongly associated with increased trust and social acceptability, whereas members of the public generally perceive a lack of transparency negatively. This literature review also deepens our understanding of the potential negative impacts of transparent communication. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data. [ABSTRACT FROM AUTHOR]

Background: Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. Methods: In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. Results: In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. Conclusions: The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data. [ABSTRACT FROM AUTHOR]

Digitalization in healthcare and the increasing availability of data demand data literacy competences of nurses and other healthcare professionals including technical, ethical and communication skills. The international Spring School 2023 "Information in Healthcare - From Date to Knowledge" aimed at these competences covering interoperability, data protection and security, data analytics and ethical issues. These topics were embedded in the overall case of data-driven quality improvement for diabetes patients in a region. The curriculum includes an online preparation-phase and a five-days attendance week, incorporating problem-based and group work approaches. According to the student's evaluation, the awareness of the importance of the topics was raised and theoretical as well as practical application skills were improved. The Spring School enhanced data literacy competences, critical thinking, problem-solving, interprofessional und intercultural skills among healthcare professionals. Such course offering can contribute to meeting the increasing challenges of digitalization in healthcare. [ABSTRACT FROM AUTHOR]

The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workers. [ABSTRACT FROM AUTHOR]

The rapid growth of available wearable technologies, particularly in the healthcare sector, has ushered in a new era of digital health. This progress has introduced new possibilities for the monitoring of vital signs, including breathing rate (BR). BR is a crucial health parameter used to assess an individual's overall well-being; specifically, it can be considered a precise indicator of pathological conditions and stressors. The aim of this study is to conduct a metrological characterization of one of the most widely used BR sensors for the measurement of BR (i.e., Zephyr BioHarness 3.0), comparing it to a BR gold standard monitor (i.e., a spirometer), in determined test conditions, including both natural and defined BR patterns. Additionally, the Monte Carlo method (MCM) was applied to evaluate the propagation of the measurement uncertainty in the identification of the peaks of respiratory signal, used to calculate BR. Results reveal that the Zephyr BioHarness 3.0 sensor provides highly accurate BR measurements, with a precision of ±1 bpm. Bland-Altman analysis indicates a 95 % confidence interval of [-2; 3] bpm, demonstrating a close agreement with the reference instrument. The Pearson's correlation coefficient of 0.95 further validates the linear correlation between the reference/test measurements. The simulation based on MCM provides an expanded uncertainty of ±2 bpm (coverage factor k = 2) in BR estimation due to the uncertainty in the peaks identification (compatible with the accuracy provided by the manufacturer). These results highlight the Zephyr BioHarness 3.0 sensor reliability for BR monitoring while emphasizing the significance of accounting for different disturbing factors, e.g., uncertainty in peak detection during data analysis, also to provide a rigorous level of confidence in the measurement results, hence being able to properly interpret them in biomedical applications.

Abstract Background Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects’ metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts. Methods Three synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3). Results Three pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric. Conclusion The initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall.

• LGBTQIA+ populations are relevant components of our society and are increasing numerically, but they unfortunately face health and health care inequities. • Each gender identity and sexual orientation has its own cluster of risk factors and healthcare needs. • Health workforce should be a point of strength in the fight against any type of discrimination. • Modernizing and expanding gender, sex and sexual orientation information practices may inform the development and equitable implementation of prevention and treatment guidelines for the growing LGBTQIA+ population: successfully collecting, recording, storing and using accurate GSSO data is key to ensuring that any health inequality for all SGM is overcome. [ABSTRACT FROM AUTHOR]

The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis. [ABSTRACT FROM AUTHOR]

In the era of rapid technological advancement, wearables have emerged as a promising tool for enhancing health and well-being. The convergence of health and technology drives an unprecedented change in the approach to health and well-being management. This paper aims to provide a comprehensive understanding of the potential role of wearables in actualising health and well-being, thereby paving the way for a healthier and more sustainable future. Using the Affordance Theory lens, this paper delves into the transformative potential of wearables in health and well-being management, thereby promoting Sustainable Development Goal 3 to ensure healthy lives and well-being for all at all ages. The thematic analysis of online reviews on wearable devices captured through web scraping was carried out to explore the potential of these devices in the management of health and well-being. The paper explored how wearables, often integrated into everyday life, can monitor vital signs, track fitness metrics, and even provide therapeutic benefits for health and well-being. The findings reveal that wearables can empower individuals to take charge of their health by leveraging real-time data and personalised feedback, promoting a proactive and preventive approach to health management and resource-effective healthcare. Furthermore, the paper highlights how wearables can contribute to long-term health outcomes for the present generation without exerting excessive strain on the resources for future generations. [ABSTRACT FROM AUTHOR]

With the advent of ICT-based healthcare applications, various formats of health data are generated every day in huge volume. Such data, consisting of unstructured, semi-structured and structured data, has every characteristic of Big data. NoSQL databases are generally preferred for storing such type of health data with the objective of improving query performance. However, for efficient retrieval and processing of Big Health Data and for resource optimization, suitable data models and design of the NoSQL databases are important requirements. Unlike relational databases, no standard methods or tools exist for NoSQL database design. In this work, we adopt an ontology-based schema design approach. We propose that an ontology, which captures the domain knowledge, be used for developing a health data model. An ontology for primary healthcare is described in this paper. We also propose an algorithm for designing the schema of a NoSQL database, keeping in mind the characteristics of the target NoSQL store, using a related ontology, a sample query set, some statistical information of the queries, and performance requirements of the query set. The ontology proposed by us for primary healthcare domain and the above mentioned algorithm along with a set of queries are used for generating a schema targeting MongoDB datastore. The performance of the proposed design is compared with a relational model developed for the same primary healthcare data and the effectiveness of our proposed approach is demonstrated. The entire experiment has been carried out on MongoDB cloud platform. [ABSTRACT FROM AUTHOR]

Secondary use of clinical health data implies a prior integration of mostly heterogenous and multidimensional data sets. A clinical data warehouse addresses the technological and organizational framework conditions required for this, by making any data available for analysis. However, users of a data warehouse often do not have a comprehensive overview of all available data and only know about their own data in their own systems - a situation which is also referred to as 'data siloed state'. This problem can be addressed and ultimately solved by implementation of a data catalog. Its core function is a search engine, which allows for searching the metadata collected from different data sources and thereby accessing all data there is. With this in mind, we conducted an explorative online market survey followed by vendor comparison as a pre-requisite for system selection of a data catalog. Assessment of vendor performance was based on seven predetermined and weighted selection criteria. Although three vendors achieved the highest score, results were lying closely together. Detailed investigations and test installations are needed for further narrowing down the selection process. [ABSTRACT FROM AUTHOR]

Copyright of Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects' metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts. Methods: Three synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3). Results: Three pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric. Conclusion: The initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall. [ABSTRACT FROM AUTHOR]

The advent of the Internet of Things (IoT) has opened new horizons in the healthcare domain, enabling massive collection of medical data from connected devices. However, this rapid expansion of IoT devices has brought forth major challenges in terms of health data storage and processing, demanding innovative solutions to ensure efficient and secure management. For a considerable period, cloud computing has been the predominant paradigm for storing and analyzing data gathered from a plethora of connected medical objects. Nevertheless, with the rise of big data and considering the sensitivity and criticality of health data, this paradigm has started to face limitations, particularly in terms of latency, throughput, bandwidth, dependence on the internet, and cost, which escalates with the consumption of its resources, even when such processing can be performed at the client's end. To address these limitations, various other paradigms have emerged, notably edge computing and fog computing. As a result, research has witnessed a proliferation of hybrid solutions based on these three computing paradigms. This work aims to provide a holistic overview of a substantial number of these proposals, with the objective of offering a one-stop resource for researchers and professionals in the field. In summary, this scientific review contributes to enlightening researchers and practitioners about the emerging IoT Edge-Fog-Cloud architectures for healthcare applications. It underscores the significance of these integrated approaches in enhancing health data management. [ABSTRACT FROM AUTHOR]

Population-based biobanking is an essential element of medical research that has grown substantially over the last two decades, and many countries are currently pursuing large national biobanking initiatives. The rise of individual biobanks is paralleled by various networking activities in the field at both the national and international level, such as BBMRI-ERIC in the EU. A significant contribution to population-based biobanking comes from large cohort studies and national repositories, including the United Kingdom Biobank (UKBB), the CONSTANCES project in France, the German National Cohort (NAKO), LifeLines in the Netherlands, FinnGen in Finland, and the All of Us project in the U.S. At the same time, hospital-based biobanking has also gained importance in medical research. We describe some of the scientific questions that can be addressed particularly well by the use of population-based biobanks, including the discovery and calibration of biomarkers and the identification of molecular correlates of health parameters and disease states. Despite the tremendous progress made so far, some major challenges to population-based biobanking still remain, including the need to develop strategies for the long-term sustainability of biobanks, the handling of incidental findings, and the linkage of sample-related and sample-derived data to other relevant resources. [ABSTRACT FROM AUTHOR]

Bexar Data Dive, an online data platform, was created to increase accessibility and use of health and social determinants of health data, such as education, economic barriers to healthcare, and hospitalization rates, to decrease racial/ethnic health disparities throughout Bexar County. A model of user-centered design helped us incorporate community input into the platform. We conducted four interviews and five focus groups to gather information on how people use data -- specifically beginner and intermediate-level data users from various educational, governmental, and nonprofit organizations. Then, we launched a community survey to assess specific data needs. Lastly, once the alpha version of Bexar Data Dive was ready, we conducted user testing sessions to measure usability, identify bugs, and gather final feedback before launch. Our findings included many recommendations for incorporating user-centered design in health data management. Participants wanted a health data tool that was easy to use, had the indicators they commonly need, and would provide visualizations for presentations, grants, and other projects. [ABSTRACT FROM AUTHOR]

Copyright of TraHs - Trayectorias Humanas Trascontinentales is the property of Universite de Limoges and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sharing of health data is an essential component in the provision of healthcare, in medical research, and disease surveillance. Health data sharing is subject to regulatory frameworks that vary across jurisdictions. In Africa, numerous factors complicate the regulation of health data sharing, including technological, motivational, economic, and political barriers, as well as ethical and legal challenges. This comparative study examines the regulation of health data sharing in Africa by comparing and contrasting the legal and policy frameworks of five African countries. The study identifies gaps and inconsistencies in the current regulatory regimes and provides recommendations for improving the regulation of health data sharing in Africa. [ABSTRACT FROM AUTHOR]