Digital Health Data Research Articles

EHDS and Free Movement of Patients: What EU Intervention is Needed?

The European Commission issued a proposal for a Regulation on the European Health and Data Space to improve patient mobility. This EHDS Regulation lays down rules for the exchange of digital health data for primary use (treatment) and for secondary use (i.e. research, policymaking). This proposal has far-reaching implications for national healthcare systems. The question arises whether, with this draft, the Commission in fact proposes to overstep the powers of the European Union, as the delivery and organization of healthcare belongs to the competences of the Member States. Furthermore, the (lack of) exchange of digital health data is not the only hinderance for the free movement of patients. The current set-up of the Patient Directive and the Social Security Coordination Regulation also discourages patients from seeking treatment abroad. Therefore, a more integrated approach in the form of a new Patient Regulation is, in our view, needed.

The Anonymous Data Warehouse: A Hands-On Framework for Anonymizing Data From Digital Health Applications.

The digital health space is growing rapidly, and so is the interest in sharing anonymized health data. However, data anonymization techniques have yet to see much coverage in the medical literature. The purpose of this article is, therefore, toprovide a practical framework for anonymization with a focus on the unique properties of data from digital health applications.Literature trends, as well as common anonymization techniques, were synthesized into a framework that considers the opportunities and challenges of digital health data. A rationale for each design decision is provided, and the advantages and disadvantages are discussed. We propose a framework based on storing data separately, anonymizing the data where the identified data is located, only exporting selected data, minimizing static attributes, ensuring k-anonymity of users and their static attributes, and preventing defined metrics from acting as quasi-identifiers by using aggregation, rounding, and capping. Data anonymization requires a pragmatic approach that preserves the utility of the data while minimizing reidentification risk. The proposed framework should be modified according to the characteristics of the respective data set.

The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.

This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles. We included peer-reviewed journal articles written in English, with no time constraints on the search. A Google Scholar search, conducted up to 19 September 2023, yielded 27 journal articles. Through a topic-modeling approach, the identified topics encompassed optimizing patient healthcare through data-driven medicine, predictive modeling with data and algorithms, predicting diseases with deep learning of biomedical data, and machine learning in medicine. This review delves into specific applications of explainable artificial intelligence, emphasizing its role in fostering transparency, accountability, and trust within the healthcare domain. Our review highlights the necessity for further development and validation of explanation methods to advance precision healthcare delivery.

Assessment of ownership of smart devices and the acceptability of digital health data sharing

Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18–25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.

Farming activity and risk of treated thyroid disorders: Insights from the TRACTOR project, a nationwide cohort study

BackgroundEpidemiological data regarding thyroid diseases are lacking, in particular for occupationally exposed populations. ObjectivesTo compare the risk of hypothyroidism and hyperthyroidism between farming activities within the complete population of French farm managers (FMs). MethodsDigital health data from retrospective administrative databases, including insurance claims and electronic health/medical records, was employed. This cohort data spanned the entirety of French farm managers (FMs) who had undertaken work at least once from 2002 to 2016. Survival analysis with the time to initial medication reimbursement as timescale was used to examine the association (hazard ratio, HR) between 26 specific farming activities and both treated hypothyroidism and hyperthyroidism. A distinct model was developed for each farming activity, comparing FMs who had never engaged in the specific farming activity between 2002 and 2016 with those who had. All analyses were adjusted for potential confounders (e.g., age), and sensitivity analyses were conducted. ResultsAmong 1088561 FMs (mean age 46.6 [SD 14.1]; 31% females), there were 31834 hypothyroidism cases (75% females) and 620 hyperthyroidism cases (67% females), respectively. The highest risks were observed for cattle activities for both hyperthyroidism (HR ranging from 1.75 to 2.42) and hypothyroidism (HR ranging from 1.41 to 1.44). For hypothyroidism, higher risks were also observed for several animal farming activities (pig, poultry, and rabbit), as well as fruit arboriculture (HR = 1.22 [1.14–1.31]). The lowest risks were observed for activities involving horses. Sex differences in the risk of hypothyroidism were observed for eight activities, with the risk being higher for males (HR = 1.09 [1.01–1.20]) than females in viticulture (HR = 0.97 [0.93–1.00]). The risk of hyperthyroidism was two times higher for male dairy farmers than females. DiscussionOur findings offer a comprehensive overview of thyroid disease risks within the FM community. Thyroid ailments might not stem from a single cause but likely arise from the combined effects of various causal agents and triggering factors (agricultural exposome). Further investigation into distinct farming activities—especially those involving cattle—is essential to pinpoint potential risk factors that could enhance thyroid disease monitoring in agriculture.

Physical and mental health in adolescence: novel insights from a transdiagnostic examination of FitBit data in the ABCD study

Adolescence is among the most vulnerable period for the emergence of serious mental illnesses. Addressing this vulnerability has generated interest in identifying markers of risk for symptoms and opportunities for early intervention. Physical fitness has been linked to psychopathology and may be a useful risk marker and target for early intervention. New wearable technology has made assessing fitness behavior more practical while avoiding recall and self-report bias. Still, questions remain regarding the clinical utility of physical fitness metrics for mental health, both transdiagnostically and along specific symptom dimensions. The current study includes 5007 adolescents (ages 10–13) who participated in the Adolescent Brain Cognitive Development (ABCD) study and additional sub-study that collected fitness data from wearable technology and clinical symptom measures. Physical fitness metrics included resting heart rate (RHR- an index of cardiovascular health), time spent sedentary (associated with increased inflammation and cardiovascular disease), and time spent in moderate physical activity (associated with increased neurogenesis, neuroplasticity, and healthy neurodevelopment). Self-report clinical symptoms included measures of psychosis-like experiences (PLE), internalizing symptoms, and externalizing symptoms. Increased RHR- lower cardiovascular fitness- related only to greater internalizing symptoms (t = 3.63). More sedentary behavior related to elevated PLE severity (t = 5.49). More moderate activity related to lower PLE (t = −2.69) and internalizing (t = −6.29) symptom severity. Wearable technology fitness metrics linked physical health to specific mental health dimensions, which emphasizes the utility of detailed digital health data as a marker for risk and the need for precision in targeting physical health behaviors to benefit symptoms of psychopathology.

Typical everyday movements cause specific patterns in heart rate.

Physical inactivity and sedentary behaviour are important risk factors for cardiovascular disease. Knowledge about the impact of everyday movements on cardiac autonomic regulation is sparse. This study aims to provide evidence that typical everyday movements show a clear impact on heart rate regulation. 40 healthy participants performed two everyday movements: (1) calmly kneeling down ("tie one's shoes") and standing up again and (2) raising the arms to the horizontal ("expressive yawning"). Both movements elicited reproducible pattern in the sequence of heart periods. Local minima and local maxima appeared in the transient period of approx. 30s. The regulatory response for ergometer cycling, which was used as control, did not show a pattern formation. Calmly performed everyday movements are able to elicit rich cardiac regulatory responses including specific patterns in heart rate. These newly described patterns have multiple implications for clinical and rehabilitative medicine, basic research, digital health data processing, and public health. If carried out regularly these regulatory responses may help to mitigate the burden of physical inactivity and enrich cardiovascular regulation.

Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries.

With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level. This study investigated public preferences for digital health data sharing. A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets. A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions. This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Survey of Public Attitudes toward the Secondary Use of Public Healthcare Data in Korea.

Public healthcare data have become crucial to the advancement of medicine, and recent changes in legal structure on privacy protection have expanded access to these data with pseudonymization. Recent debates on public healthcare data use by private insurance companies have shown large discrepancies in perceptions among the general public, healthcare professionals, private companies, and lawmakers. This study examined public attitudes toward the secondary use of public data, focusing on differences between public and private entities. An online survey was conducted from January 11 to 24, 2022, involving a random sample of adults between 19 and 65 of age in 17 provinces, guided by the August 2021 census. The final survey analysis included 1,370 participants. Most participants were aware of health data collection (72.5%) and recent changes in legal structures (61.4%) but were reluctant to share their pseudonymized raw data (51.8%). Overall, they were favorable toward data use by public agencies but disfavored use by private entities, notably marketing and private insurance companies. Concerns were frequently noted regarding commercial use of data and data breaches. Among the respondents, 50.9% were negative about the use of public healthcare data by private insurance companies, 22.9% favored this use, and 1.9% were "very positive." This survey revealed a low understanding among key stakeholders regarding digital health data use, which is hindering the realization of the full potential of public healthcare data. This survey provides a basis for future policy developments and advocacy for the secondary use of health data.

On the path to an equitable and sustainable digital future for European health systems

Equitable, patient-centred approaches are at the centre of WHO/Europe's regional digital health action plan 2023-2030 which was approved by Member States of the European Region at the 72nd session of the WHO Regional Committee. The Action Plan specifically identifies the need for WHO to “identify and examine the main impacts that gender and equity present in the adoption and scaling of digital health solutions in the Region.” The session will be organised as a round table discussion to examine to inequalities surrounding access to, and the use of data and digital technologies in health care and public health. Panellists will firstly discuss the factors which give rise to inequalities in access and use of data and digital technologies in health and then examine strategies for how the digital transformation of health systems going forward can address these barriers. They will be asked to reflect on the future of digital health systems in Europe and the ethical, policy, legal and technical actions required to ensure trust, safety and equitable access to health services and data digitally. Finally, in line with the theme of the conference, panellists will be asked to extend the discussion to reflect upon the use of digital technologies in the context of sustainability and how the public health community can drive thinking around the agenda for the green digitalization of health. Moderator: Natasha Azzopardi Muscat, Director Division of Country Health Policies and Systems, WHO Regional Office for Europe Speakers/Panellists: Alisha Davies, Head of Research and Development, Health and Care Research Wales, United Kingdom Anna Odone, President EUPHA Digital health section David Novillo Ortiz, Regional Adviser, Data and Digital Health, WHO Regional Office for Europe

6.F. Workshop: Digital public health in Europe - What is the new normal?

Abstract Digital public health has influenced how countries use health data for research and practice, how we deliver healthcare, and how health professionals monitor infectious diseases. The COVID-19 pandemic accelerated the international adoption of digital health interventions for public health aims. While some European countries were already advanced in digital health and digital public health before the pandemic, other nations took the opportunity to catch up. Internationally, governments introduced contact-tracing apps, telemedical services, digitally supported health promotion programs, nationally regulated medical apps, and education programs for citizens and experts to improve digital (health) literacy. The planned European Health Data Space will lift the level of digital public health in Europe once more. However, the maturity of digital public health among European countries varies drastically. While some are global leaders in digitalizing their health systems, others barely implemented the necessary infrastructure. But there is also a third perspective: Some countries that led Europe through the pandemic from a digital public health perspective started withdrawing from digitalizing their healthcare and health promotion and returning to “normal”. It is time to examine what this new “normal” post-pandemic means and how countries evolved. We will learn from the experience of 4 different European countries (Romania, Ireland, Slovenia, and Germany) how COVID-19 changed their health system and if digital public health is still applied now that the pandemic is officially over. The workshop's ultimate goal is to highlight and discuss which digital transformations in European health systems were permanent and whether or not Europe will continue to take a leading role in digital public health in the future. Each representative will have 10 minutes to contextualize their country's digital public health strategy before the pandemic (the old normal), their strategy to address the crisis, and how the country includes digital public health in its health system after the pandemic (the new normal). We will use the remaining 20 minutes of the workshop for a discussion between the speakers and the audience. Key messages • The COVID-19 crisis pushed Europe's digital public health transformation so that countries were enabled to use digital health data for improved healthcare and health promotion on a national level. • In order for the transformation to be sustainable, countries need to fundamentally rethink their health systems and make way for better digital innovation.

Digital Health Technology for Real-World Clinical Outcome Measurement Using Patient-Generated Data: Systematic Scoping Review.

Digital health technologies (DHTs) play an ever-expanding role in health care management and delivery. Beyond their use as interventions, DHTs also serve as a vehicle for real-world data collection to characterize patients, their care journeys, and their responses to other clinical interventions. There is a need to comprehensively map the evidence-across all conditions and technology types-on DHT measurement of patient outcomes in the real world. We aimed to investigate the use of DHTs to measure real-world clinical outcomes using patient-generated data. We conducted this systematic scoping review in accordance with the Joanna Briggs Institute methodology. Detailed eligibility criteria documented in a preregistered protocol informed a search strategy for the following databases: MEDLINE (Ovid), CINAHL, Cochrane (CENTRAL), Embase, PsycINFO, ClinicalTrials.gov, and the EU Clinical Trials Register. We considered studies published between 2000 and 2022 wherein digital health data were collected, passively or actively, from patients with any specified health condition outside of clinical visits. Categories for key concepts, such as DHT type and analytical applications, were established where needed. Following screening and full-text review, data were extracted and analyzed using predefined fields, and findings were reported in accordance with established guidelines. The search strategy identified 11,015 publications, with 7308 records after duplicates and reviews were removed. After screening and full-text review, 510 studies were included for extraction. These studies encompassed 169 different conditions in over 20 therapeutic areas and 44 countries. The DHTs used for mental health and addictions research (111/510, 21.8%) were the most prevalent. The most common type of DHT, mobile apps, was observed in approximately half of the studies (250/510, 49%). Most studies used only 1 DHT (346/510, 67.8%); however, the majority of technologies used were able to collect more than 1 type of data, with the most common being physiological data (189/510, 37.1%), clinical symptoms data (188/510, 36.9%), and behavioral data (171/510, 33.5%). Overall, there has been real growth in the depth and breadth of evidence, number of DHT types, and use of artificial intelligence and advanced analytics over time. This scoping review offers a comprehensive view of the variety of types of technology, data, collection methods, analytical approaches, and therapeutic applications within this growing body of evidence. To unlock the full potential of DHT for measuring health outcomes and capturing digital biomarkers, there is a need for more rigorous research that goes beyond technology validation to demonstrate whether robust real-world data can be reliably captured from patients in their daily life and whether its capture improves patient outcomes. This study provides a valuable repository of DHT studies to inform subsequent research by health care providers, policy makers, and the life sciences industry. Open Science Framework 5TMKY; https://osf.io/5tmky/.

Developing inclusive digital health diagnostic for schistosomiasis: a need for guidance via target product profiles

IntroductionThe INSPIRED project aims to develop inclusive Digital Optical Diagnostic Devices (DODDs) for schistosomiasis, to support disease management by enabling rapid diagnostic results, to improve efficient data management to guide decision-making and to provide healthcare workers with critical health information to facilitate follow-up action. Due to the non-availability of Target Product Profiles (TPPs) for guiding the development of digital diagnostics for schistosomiasis, we explored existing diagnostic TPPs.MethodsUsing a curated open access database (Notion database), we studied a selection of TPPs for diagnosing infectious diseases, focusing on specifications related to digital health products for Neglected Tropical Diseases (NTDs).ResultsEighteen TPPs originating from 12 documents, covering 13 specific diseases, were selected and their characteristics were labeled and entered into the database. Further exploration of the database revealed several gaps, including a lack of stakeholder input, sustainability, and TPP availability. Other significant gaps related to digital health platform interconnectivity and data stewardship specifically in relation to digital diagnostics, including DODDs.DiscussionThese findings reflect two possible scenarios: (1) there is currently no need for digital diagnostic devices for schistosomiasis and, by extension for other NTDs; or (2) those needs are not yet covered by TPPs. Therefore, we recommend that digital health diagnostics are included in the use cases for schistosomiasis control and elimination, at least in the ideal/desirable scenario, as this will guide research and incentivize investment in digital health diagnostics for schistosomiasis.

Applying Digital Health in Cancer and Palliative Care in Europe: Policy Recommendations from an International Expert Workshop (MyPal Project).

Background: Digital health interventions are becoming increasingly important for adults, children, and young people with cancer and palliative care needs, but there is little research to guide policy and practice. Objectives: To identify recommendations for policy development of digital health interventions in cancer and palliative care. Design: Expert elicitation workshop. Setting: European clinical (cancer and palliative care, adult and pediatric), policy, technical, and research experts attended a one-day workshop in London, England, in October 2022, along with MyPal research consortium members. Methods: As part of the European Commission-funded MyPal project, we elicited experts' views on global, national, and institutional policies within structured facilitated groups, and conducted qualitative analysis on these discussions. Results/Implementation: Thirty-two experts from eight countries attended. Key policy drivers and levers in digital health were highlighted. Global level: global technology regulation, definitions, access to information technology, standardizing citizens' rights and data safety, digital infrastructure and implementation guidance, and incorporation of technology into existing health systems. National level: country-specific policy, compatibility of health apps, access to digital infrastructure including vulnerable groups and settings, development of guidelines, and promoting digital literacy. Institutional level: undertaking a needs assessment of service users and clinicians, identifying best practice guidelines, providing education and training for clinicians on digital health and safe digital data sharing, implementing plans to minimize barriers to accessing digital health care, minimizing bureaucracy, and providing technical support. Conclusions: Developers and regulators of digital health interventions may find the identified recommendations useful in guiding policy making and future research initiatives. MyPal child study Clinical Trial Registration NCT04381221; MyPal adult study Clinical Trial Registration NCT04370457.

ZotCare: a flexible, personalizable, and affordable mhealth service provider.

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare's service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution.

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review.

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing.

How Participatory Health Informatics Catalyzes One Digital Health.

To identify links between Participatory Health Informatics (PHI) and the One Digital Health framework (ODH) and to show how PHI could be used as a catalyst or contributor to ODH. We have analyzed the addressed topics within the ODH framework in previous IMIA Yearbook contributions from our working group during the last 10 years. We have matched main themes with the ODH's framework three perspectives (individual health and wellbeing, population and society, and ecosystem). PHI catalysts ODH individual health and wellbeing perspective by providing a more comprehensive view on human health, attitudes, and relations between human health and animal health. Integration of specific behavior change techniques or gamification strategies in digital solutions are effective to change behaviors which address the P5 paradigm. PHI supports the population and society perspective through the engagement of the various stakeholders in healthcare. At the same time, PHI might increase a risk for health inequities due to technologies inaccessible to all equally and challenges associated with this. PHI is a catalyst for the ecosystem perspective by contributing data into the digital health data ecosystem allowing for analysis of interrelations between the various data which in turn might provide links among all components of the healthcare ecosystem. Our results suggest that PHI can and will involve topics relating to ODH. As the ODH concept crystalizes and becomes increasingly influential, its themes will permeate and become embedded in PHI even more. We look forward to these developments and co-evolution of the two frameworks.

Digital health care and data work: Who are the data professionals?

This article reports on a study that investigated data professionals in health care. The topic is interesting and relevant because of the ongoing trend towards digitisation of the healthcare domain and efforts for it to become data driven, which entail a wide variety of work with data. Despite an interest in data science and more broadly in data work, we know surprisingly little about the people who work with data in healthcare. Therefore, we investigated data work at a large national healthcare data organisation in Denmark. An explorative mixed method approach combining a non-probability technique for design of an open survey with a target population of 300+ and 11 semi-structured interviews, was applied. We report findings relevant to educational background, work identity, work tasks, and how staff acquired competences and knowledge, as well as what these attributes comprised. We found recurring themes of healthcare knowledge, data analytical skills, and information technology, reflected in education, competences and knowledge. However, there was considerable variation within and beyond those themes, and indeed most competences were learned "on the job" rather than as part of formal education. Becoming a professional working with data in health care can be the result of different career paths. The most recurring work identity was that of "data analyst"; however, a wide variety of responses indicated that a stable data worker identity has not yet developed. The findings present implications for educational policy makers and healthcare managers.

Exploring Regulatory Interplay in the Health Internet of Everything: Digital Health Technologies, Data Protection and Cybersecurity.

Against the backdrop of interconnected devices, people, and processes in the Internet of Everything (IoE) an examination of the MDR (Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EE), data protection and cybersecurity rules are carried out with respect to medical devices' privacy and security. The legal analysis therefore investigates how and to what extent the MDR could cope with the copious challenges of cybersecurity and data protection in the IoE of medical devices. The article sheds light on how the MDR sets a minimal standard of requirements for medical device safety and security. In this respect, an unexpected and complicated regulatory interplay is discussed providing for a well-coordinated approach for digital health technologies. Finally, the article discusses several key points of the proposed EU AI Regulation considering digital health technologies.

RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease

Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning. The average accuracy and F1-weighted score for the extraction ofrepomics features using RadioLOGIC are 0.934 and 0.934, respectively, and 0.906 and 0.903 for the prediction of breast imaging-reporting and data system scores. The areas under the receiver operating characteristic curve forthe prediction of pathological outcome without and with transfer learning are0.912 and 0.945, respectively. RadioLOGIC outperforms cohort models in the capability to extract features and also reveals promise for checking clinical diagnoses directly from electronic health records.