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Data standards play a key role in the governance of commercial actors on the market for health data. This commentary explores expectations for standard-setting in the European Health Data Space (EHDS), a pivotal legal initiative for facilitation of cross-border health data exchange. While EHDS aims to establish shared standards for interoperability and innovation in the European Union (EU) and affiliated countries, the analysis reveals some of the inherent complexities. We draw on insights from policy analysis and Science and Technology Studies (STS) that underscore how standards are performative and have unpredictable and uneven implications. To exemplify this, the commentary delves into the intricate issues surrounding the harmonization of diagnostic codes and pharmaceutical prescriptions. In conclusion, it asserts that while standard setting has unpredictable effects, it still involves prioritizing some purposes and benefits over others. We raise questions about the distribution of costs and benefits among stakeholders in the evolving landscape of health data governance and call for further empirical investigation. [ABSTRACT FROM AUTHOR]

Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell's C of 0.74 (0.73–0.74) (calibration slope 1.00 (0.97–1.02) and 0.67 (0.66–0.69) (calibration slope 0.92 (0.84–1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants. [ABSTRACT FROM AUTHOR]

This paper concerns the modeling of smart health sensors for data monitoring, where the data are eventually noisy, with correlated noise. In this work, we applied Clifford-based wavelets/multiwavelets for correlated noise in multi-sensors health data monitoring by estimating the set of sensor nodes minimizing an eventual error computed by the signal values at the selected nodes mostly caused by the correlated noise. Instead of directly minimizing the estimation error, we focused on evaluating a multi-level scheme based on multiwavelets for the estimation of the error between the parameter vector and its sub-vector of those nodes. Numerical simulations are provided with a comparison to some recent existing works. This model showed a performance and a fast time execution compared to those existing works. This model exceeds these models by the non-necessity to assume a priory structure of the data. Wavelets are capable to detect, localize, and eliminate the noise, even correlated, efficiently via the independent uncorrelated multiwavelets’ components. [ABSTRACT FROM AUTHOR]

Introduction: The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices. Methods: The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis. Results: The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project. Discussion: Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) poses significant health risks due to its asymptomatic nature in early stages and its association with increased cardiovascular and kidney events. Early detection and management are critical for improving outcomes. Objective: This study aimed to develop and validate a prediction model for hospitalization for ischemic heart disease (IHD) or cerebrovascular disease (CVD) and major kidney events in Japanese individuals with mild CKD using readily available health check and prescription data. Methods: A retrospective cohort study was conducted using data from approximately 850,000 individuals in the PREVENT Inc. database, collected between April 2013 and April 2023. Cox proportional hazard regression models were utilized to derive and validate risk scores for hospitalization for IHD/CVD and major kidney events, incorporating traditional risk factors and CKD-specific variables. Model performance was assessed using the concordance index (c-index) and 5-fold cross-validation. Results: A total of 40,351 individuals were included. Key predictors included age, sex, diabetes, hypertension, and lipid levels for hospitalization for IHD/CVD and major kidney events. Age significantly increased the risk score for both hospitalization for IHD/CVD and major kidney events. The baseline 5-year survival rates are 0.99 for hospitalization for IHD/CVD and major kidney events are 0.99. The developed risk models demonstrated predictive ability, with mean c-indexes of 0.75 for hospitalization for IHD/CVD and 0.69 for major kidney events. Conclusions: This prediction model offers a practical tool for early identification of Japanese individuals with mild CKD at risk for hospitalization for IHD/CVD and major kidney events, facilitating timely interventions to improve patient outcomes and reduce healthcare costs. The models stratified patients into risk categories, enabling identification of those at higher risk for adverse events. Further clinical validation is required. [ABSTRACT FROM AUTHOR]

Large data-intensive health research programmes benefit from collaboration amongst researchers who may be located in different institutions and international contexts. However, complexities in navigating privacy frameworks and data protection laws across various jurisdictions pose significant challenges to researchers seeking to share or transfer data outside of institutional boundaries. Research on the awareness of data protection and privacy laws amongst stakeholders is limited. Our qualitative study, drawn from a larger project in Singapore, revealed insights into stakeholders' perceptions of the role of law in cross-national health data research. Stakeholders in our study demonstrated a range of perceptions regarding the role of data protection law in governing the collection and transfer of health data for research. The main criticisms included inadequate legal protection to data and lack of uniformed data protection standards. Despite these criticisms, participants recognised the importance of data protection law in supporting cross-border data transfers and proposed measures to improve perceived limitations of existing laws. These measures include strengthening existing legal framework, establishing contractual agreements and imposing severe punishments for data misuse. [ABSTRACT FROM AUTHOR]

Global health emergencies often lead to a proliferation of health-related research and resultant data, which is shared across borders to help control the outbreak of disease and support decision-making regarding public health interventions. However, efforts to share data can be hindered by diverse international ethical and legal frameworks. The frameworks aim to govern coordinated processing, sharing and transfer of health data across borders thus placing burdens on researchers who are willing or obligated to share data. In this paper, we investigate ethical values and legal principles that can be applied to ensure a more streamlined approach in the governance of cross-border health data sharing during global health emergencies. A scoping review approach was used to better understand emerging evidence about issues related to the ethical and legal governance of cross-border health data sharing. We first highlight the challenges of sharing health data across borders. We then discuss how the challenges might be overcome through the application of ethical values and legal principles that have been identified in literature. This is followed by a proposal for possible ways of streamlining the governance of health data sharing from ethical and legal perspectives in a bid to better prepare for future global health emergencies and promote the responsible sharing of health data. [ABSTRACT FROM AUTHOR]

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]

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]

Abstract Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell’s C of 0.74 (0.73–0.74) (calibration slope 1.00 (0.97–1.02) and 0.67 (0.66–0.69) (calibration slope 0.92 (0.84–1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants.

Abstract Background Chronic kidney disease (CKD) poses significant health risks due to its asymptomatic nature in early stages and its association with increased cardiovascular and kidney events. Early detection and management are critical for improving outcomes. Objective This study aimed to develop and validate a prediction model for hospitalization for ischemic heart disease (IHD) or cerebrovascular disease (CVD) and major kidney events in Japanese individuals with mild CKD using readily available health check and prescription data. Methods A retrospective cohort study was conducted using data from approximately 850,000 individuals in the PREVENT Inc. database, collected between April 2013 and April 2023. Cox proportional hazard regression models were utilized to derive and validate risk scores for hospitalization for IHD/CVD and major kidney events, incorporating traditional risk factors and CKD-specific variables. Model performance was assessed using the concordance index (c-index) and 5-fold cross-validation. Results A total of 40,351 individuals were included. Key predictors included age, sex, diabetes, hypertension, and lipid levels for hospitalization for IHD/CVD and major kidney events. Age significantly increased the risk score for both hospitalization for IHD/CVD and major kidney events. The baseline 5-year survival rates are 0.99 for hospitalization for IHD/CVD and major kidney events are 0.99. The developed risk models demonstrated predictive ability, with mean c-indexes of 0.75 for hospitalization for IHD/CVD and 0.69 for major kidney events. Conclusions This prediction model offers a practical tool for early identification of Japanese individuals with mild CKD at risk for hospitalization for IHD/CVD and major kidney events, facilitating timely interventions to improve patient outcomes and reduce healthcare costs. The models stratified patients into risk categories, enabling identification of those at higher risk for adverse events. Further clinical validation is required.

Artificial intelligence is actively taking over sphere after sphere and particular successes and achievements can be seen in the medical sector and medical technology. However, the introduction of AI raises a number of practical and ethical issues. One of the main ones is the issue of handling personal data, as access to a large number of patients’ health data plays a key role in the development and use of AI in medicine. The most convenient solution for their use is to anonymise them beforehand. However, with anonymisation, there is a risk of re-identification and the potential for loss of data informativeness may be lost. In the framework of this article the experience in the sphere of legal regulation of personal health data handling when using artificial intelligence systems in medicine is considered in the example of the USA, EU and Singapore. Each country is endeavoring to strike a balance between the protection of personal data privacy and the advancement of technological innovations. The analysis suggests that the emphasis on artificial intelligence development necessitates specific premises in the domain of personal data protection. Conversely, stringent standards for the protection of personal data could potentially exert a restrictive influence.

Abstract Methodological developments in different sectors like health, biomedical and biological areas are the recent burning issue in the statistical literature. The approach of implementing declining hazard function which is obtained by compounding truncated Poisson distribution and a lifetime distribution is a special concern in a few studies. In this paper we proposed a newly introduced distribution called inverse Lomax-Uniform Poisson distribution mostly applied in the health, biomedical, biological, and related sectors. Some basic statistical properties of the distribution are discussed. The capability of the model is checked by comparing it with six potential models by using a practical real data set. Based on the comparison techniques, the newly proposed model out performs all its counterparts. The simulation study is also conducted. Furthermore, the joint modelling of repeatedly measured and time-to-vent processes is discussed in detail with the real data set in the health sector.

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.

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]

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]

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]

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]

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]

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]

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]

The SINFONIA project’s main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental effects of radiation exposure on patients, workers, caretakers, and comforters, the public, and the environment during the management of patients suspected or diagnosed with lymphoma, brain tumors, and breast cancers. The project plan defines a series of key objectives to be achieved on the way to the main objective. One of these objectives is to develop and operate a repository to collect, pool, and share data from imaging and non-imaging examinations and radiation therapy sessions, histological results, and demographic information related to individual patients with lymphoma, brain tumors, and breast cancers. This paper presents the final version of that repository, a cloud-based platform for imaging and non-imaging data. It results from the implementation and integration of several software tools and programming frameworks under an evolutive architecture according to the project partners’ needs and the constraints of the General Data Protection Regulation. It provides, among other services, data uploading and downloading, data sharing, file decompression, data searching, DICOM previsualization, and an infrastructure for submitting and running Artificial Intelligence models.

The Kanta Patient Data Repository (PDR) contains healthcare data from the population of Finland for more than a decade. The repository is a continuously expanding real world dataset produced by many information systems and healthcare service providers. Kanta data has been available for secondary uses such as scientific research since 2019. The data can be requested from the Finnish authority Findata. However, before a request has been accepted, it is difficult to assess if the accumulated data allows answering a specific research question. Publicly available descriptions of data structures in the Kanta PDR do not tell how much they are used in practice. This publication enables future data use cases by providing a view on the overall availability of types of structured health data in the Kanta PDR based on a sample of 96 200 medical histories of over 18-year-old patients. We conclude that the Kanta PDR is a promising source of real world data for development and evaluation of medical risk calculators within the Finnish population. The wide coverage of the Finnish population and timeliness of the data are its strengths as a source of research data also outside of Finnish context. However, the limitations on data availability in variable level need to be considered on a case-by-case basis. Main challenges in the use of data in the Kanta PDR are multiple code systems for laboratory results, short durations of recorded data for specific data types, and missing or very rarely used structured format e.g., in cases of tobacco and alcohol use.

IntroductionThe incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices.MethodsThe objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis.ResultsThe performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project.DiscussionDeveloped in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios.

[Purpose] The National Health Data Network (RNDS) is a national platform for the integration and interoperability of health information. Since 2019 the network has been implemented in Brazil, in the context of the development of initiatives for the digital transformation in health and to be used by public and private health entities. As it is an environment in which health information circulates, it involves processing of personal data that is subject to the General Data Protection Law (LGPD). Notably, it involves the processing of sensitive health data. This article analyzes the context of the emergence of the RNDS and expose its challenges for the protection of personal data, including the analysis of LGPD rules and how some of these rules are related to the implementation of the network. [Methodology/approach/design] The research was carried out in three fundamental phases. The first consisted of reviewing specialized bibliography and documents published by public entities regarding digital transformation in health and the implementation of the RNDS. The second aimed at identifying the legal provisions relating to the protection of personal data applicable to the RNDS. Finally, the third phase consisted of identifying the measures adopted to RNDS be compliant with these legal provisions and its challenges for compliance with LGPD. [Findings] As a result, the article demonstrates that the RNDS, despite representing progress for national digital transformation in health, faces challenges to comply with the LGPD, with regards to the principles for data processing, such as the purpose and transparency principles, information security and prevention, compatibility of the consent-based approach of the RNDS with the requirements for valid consent of the LGPD and the legal prohibition of sharing health data with the objective of obtaining economic advantage.

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.

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]