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Objective: The International Medical Informatics Association (IMIA) Open Source Working Group (OSWG) initiated a group discussion to discuss current privacy and security issues in the open data movement in the healthcare domain from the perspective of the OSWG membership.
Methods: Working group members independently reviewed the recent academic and...
Context in source publication
Context 1
... open data movement has been productive for a positive cycle of creation for both datasets and tools [7]. Even though the spectrum of datasets for open data is different from person identifiable data sets (Figure 1), there is frequent scepticism about open health data initiatives. Questions that arise here are not only those about mixing open data with private medical information, there are also questions about providing commercial organisations with live patient data that could be used in a business space, for business advantage. ...
Citations
... Due to the sensitive nature of healthcare data, ID-based properties simplify certificate management, time-bound properties limit access periods, and deniability enhances privacy by ensuring message authenticity. As WBAN users grow, more services are provided by multiple providers, increasing the demand for intelligent systems [21]. Current architectures may not meet service demands, highlighting the need for a more secure and functional authentication protocol. ...
The advent of 6G technology is expected to bring a paradigm shift in the field of wireless communication. With its faster data transfer rates and lower latency, 6G could be an ideal solution for the challenges faced by Wireless Body Area Networks (WBANs) in terms of efficient data bandwidth and edge computing. Smart healthcare systems with 6G-based WBANs might provide more efficient and higher-quality healthcare services. However, 6G-based WBAN healthcare systems might face potential security and safety challenges from cybersecurity threats. This paper will propose an ID-based deniable authentication protocol with key agreement and time-bound properties for 6G-based WBAN healthcare environments by considering user privacy, secure communications, authentication, authorization, and scalability of 6G-based WBANs. As compared with previously proposed protocols, the proposed protocol will achieve the following security requirements: mutual authentication, key agreement for secure communication, deniability, time-bound access privilege control, and identity-based public key management for scalable wearable devices and 6G-based WBAN Service Providers. We proved the claimed security requirements of the proposed protocol by using AVISPA simulation and discussed its computational complexities. As compared with previous works, the proposed protocol can gain better contributions in terms of security requirements and performance evaluations for 6G-based WBAN healthcare environments.
... Although intended to ensure privacy and security, challenges in data access have frequently not kept pace with innovations in computing and safe data sharing. [29][30][31] Movements toward open data and democratization of information promise acceleration of the knowledge translation cycle. 29,32 We hope the success of our proof-of-concept access to the HDPBC will form a foundation for future, near real-time analyses of outcome data to influence policymaking and clinical practice in BC, a vital component of a Learning Health System. ...
Background:
British Columbia's 8-1-1 telephone service connects callers with nurses for health care advice. As of Nov. 16, 2020, callers advised by a registered nurse to obtain in-person medical care can be subsequently referred to virtual physicians. We sought to determine health system use and outcomes of 8-1-1 callers urgently triaged by a nurse and subsequently assessed by a virtual physician.
Methods:
We identified callers referred to a virtual physician between Nov. 16, 2020, and Apr. 30, 2021. After assessment, virtual physicians assigned callers to 1 of 5 triage dispositions (i.e., go to emergency department [ED] now, see primary care provider within 24 hours, schedule an appointment with a health care provider, try home treatment, other). We linked relevant administrative databases to ascertain subsequent health care use and outcomes.
Results:
We identified 5937 encounters with virtual physicians involving 5886 8-1-1 callers. Virtual physicians advised 1546 callers (26.0%) to go to the ED immediately, of whom 971 (62.8%) had 1 or more ED visits within 24 hours. Virtual physicians advised 556 (9.4%) callers to seek primary care within 24 hours, of whom 132 (23.7%) had primary care billings within 24 hours. Virtual physicians advised 1773 (29.9%) callers to schedule an appointment with a health care provider, of whom 812 (45.8%) had primary care billings within 7 days. Virtual physicians advised 1834 (30.9%) callers to try a home treatment, of whom 892 (48.6%) had no health system encounters over the next 7 days. Eight (0.1%) callers died within 7 days of assessment with a virtual physician, 5 of whom were advised to go to the ED immediately. Fifty-four (2.9%) callers with a "try home treatment" disposition were admitted to hospital within 7 days of a virtual physician assessment, and no callers who were advised home treatment died.
Interpretation:
This Canadian study evaluated health service use and outcomes arising from the addition of virtual physicians to a provincial health information telephone service. Our findings suggest that supplementation of this service with an assessment from a virtual physician safely reduces the overall proportion of callers advised to seek urgent in-person visits.
... The stream of theoretical developments in data economy research embraces theories from different disciplines ( Table 5). The integrated theories are trust, security, privacy (Meijer et al., 2014;Kobayashi et al., 2018) (Yıldırım et al., 2021) and attitudes (Tenopir et al., 2020;Baždari c et al., 2021). Figure 2 displays the prominent theories used in data economy research. ...
Purpose
The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.
Design/methodology/approach
The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.
Findings
This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.
Research limitations/implications
Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.
Practical implications
The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.
Originality/value
This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.
... As operações de abertura e compartilhamento de dados abrangem todo o ciclo da pesquisa, com implicações para o depósito, a curadoria, o acesso e o reúso dos dados. Essas práticas podem esbarrar em diversos tipos de proteção jurídica, tais como: direitos autorais, segredos industriais, patentes e outros, no universo da propriedade intelectual; direitos à privacidade e à confidencialidade de dados pessoais, no âmbito das garantias individuais e coletivas; direitos relacionados à segurança nacional; e direitos protegidos por contratos (CARROL, 2015;GUIBAULT;MARGONI, 2015;KOBAYASHI;KANE;PATON, 2018;GUANAES et al., 2018;BRASIL, 2018). ...
... As operações de abertura e compartilhamento de dados abrangem todo o ciclo da pesquisa, com implicações para o depósito, a curadoria, o acesso e o reúso dos dados. Essas práticas podem esbarrar em diversos tipos de proteção jurídica, tais como: direitos autorais, segredos industriais, patentes e outros, no universo da propriedade intelectual; direitos à privacidade e à confidencialidade de dados pessoais, no âmbito das garantias individuais e coletivas; direitos relacionados à segurança nacional; e direitos protegidos por contratos (CARROL, 2015;GUIBAULT;MARGONI, 2015;KOBAYASHI;KANE;PATON, 2018;GUANAES et al., 2018;BRASIL, 2018). ...
... As operações de abertura e compartilhamento de dados abrangem todo o ciclo da pesquisa, com implicações para o depósito, a curadoria, o acesso e o reúso dos dados. Essas práticas podem esbarrar em diversos tipos de proteção jurídica, tais como: direitos autorais, segredos industriais, patentes e outros, no universo da propriedade intelectual; direitos à privacidade e à confidencialidade de dados pessoais, no âmbito das garantias individuais e coletivas; direitos relacionados à segurança nacional; e direitos protegidos por contratos (CARROL, 2015;GUIBAULT;MARGONI, 2015;KOBAYASHI;KANE;PATON, 2018;GUANAES et al., 2018;BRASIL, 2018). ...
Este artigo analisa questões do direito autoral relacionadas a dados de pesquisa subjacentes a artigos de revistas científicas. A análise é feita tendo como pano de fundo a abertura e o compartilhamento de dados de pesquisa, operação que também comporta práticas colaborativas em nível internacional. Tais práticas podem esbarrar em diversos tipos de proteção jurídica, de diferentes legislações nacionais, de cultura e interpretações distintas, que acabam gerando áreas de diferença que podem inibir o reúso de dados subjacentes. Desta forma, efetuamos um estudo do direito autoral como possível proteção de dados de pesquisa, sob a perspectiva da comunicação científica. Para tanto, foram realizadas pesquisas bibliográfica e documental. A bibliográfica incluiu livros e artigos nas áreas jurídica, biomédica e biológica, por meio de buscas no repositório PubMed Central da Biblioteca Nacional de Medicina do National Institutes of Health dos Estados Unidos e em outras bases bibliográficas que abrigam periódicos dessas áreas, como o Portal de Periódicos da Capes, Springer Link, entre outras. A pesquisa documental consistiu em consultas a leis sobre direitos autorais, direitos sui generis sobre bases de dados e proteção a dados pessoais da União Europeia; lei de direitos autorais dos Estados Unidos; leis brasileiras que regulam o direito autoral e a proteção a dados pessoais; e a Constituição brasileira. Conclui-se que o direito autoral, naturalizado e, ao mesmo tempo, pouco pesquisado como elemento formador da área científica, revela-se provavelmente inadequado para regular relações jurídicas no universo científico cuja matéria-prima é a produção de conhecimento.
... Una política de OD podría situarse en algún punto de un espectro delimitado en un extremo por el acceso completo a los datos de forma gratuita, o como máximo al costo de reproducción, como en el caso del Archivo General de la Nación; en el otro extremo se ubicaría un acceso restringido a la información, que permita la identificación de los individuos participantes en un estudio (Gitter, 2010) o el argumento potencialmente más peligroso de restricción de uso por motivos de seguridad (Kobayashi et al., 2018;Ramachandran et al., 2021) . Esto podría representar un obstáculo real para la apertura de algunos tipos de datos que podrían redundar en gran beneficio para la sociedad (Huston et al., 2019) . ...
Uruguay, al igual que más de 190 países miembros, ha suscrito la Recomendación de Ciencia Abierta de Unesco que se ha aprobado en noviembre de 2021. La ciencia abierta es un ecosistema de procesos interconectados construido sobre distintos movimientos: acceso abierto, datos abiertos, código abierto e investigación abierta reproducible, entre otros, cuyo objetivo es hacer las investigaciones científicas, datos y divulgación accesibles e inclusivos para todos los niveles de la sociedad. La implementación de políticas de ciencia abierta requiere equilibrar cuidadosamente sus costos y beneficios. Las experiencias de algunos países parecen ser exitosas, aunque la factibilidad de algunos aspectos plantea dudas en la comunidad científica. Los países del Sur Global tienen una oportunidad para posicionarse y beneficiarse de esta transición, pero deben estar un paso adelante y ser parte de su construcción. En este trabajo se revisan los principales conceptos para la implementación de un sistema de ciencia abierta y se realizan algunas consideraciones sobre el sistema
... One of the main issues in the classification of neurological disorders using deep learning is data scarcity 57 . Not only because labeling is expensive but also because privacy reasons and institutional policies make acquiring and sharing large sets of labeled imaging data even more challenging 58 . To show the impact of data size on model performance, we created 10 small subsets from the OASIS dataset (OASIS-34 datasets). ...
In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson’s Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.
... Public or open datasets must respond to three main criteria: online availability, the absence of costs, and reusability (26). Public data may represent a solution, considering that they create value in multiple heterogeneous areas (healthcare, city security, savings, etc.); therefore, numerous worldwide countries have implemented governmental open data sites (27) to increase findability and accessibility. ...
Rare diseases (RDs) are complicated health conditions that are difficult to be managed at several levels. The scarcity of available data chiefly determines an intricate scenario even for experts and specialized clinicians, which in turn leads to the so called “diagnostic odyssey” for the patient. This situation calls for innovative solutions to support the decision process via quantitative and automated tools. Machine learning brings to the stage a wealth of powerful inference methods; however, matching the health conditions with advanced statistical techniques raises methodological, technological, and even ethical issues. In this contribution, we critically point to the specificities of the dialog of rare diseases with machine learning techniques concentrating on the key steps and challenges that may hamper or create actionable knowledge and value for the patient together with some on-field methodological suggestions and considerations.
... One of the main issues in the classi cation of neurological disorders using deep learning is data scarcity 36 . Not only because labeling is expensive, but also because privacy reasons and institutional policies make acquiring large sets of labeled imaging data even more challenging 37 . To show the impact of data size on model performance, we created 10 small subsets from the OASIS dataset (OASIS-34 datasets). ...
In recent years, 2D convolutional neural networks (CNNs) have been extensively used for the diagnosis of neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models for the classification of patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson's Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.
... For years, the open-data movement in research has expressed concern that the inability to share data safely hampers research progress [44,12,49,37,1,30,17,31,3,4,29,43]. The core issue at play here is a textbook example of the copy problem: once an individual or institution shares a copy of their data, it becomes extremely difficult to control what the recipient(s) might do with it. ...
Many socially valuable activities depend on sensitive information, such as medical research, public health policies, political coordination, and personalized digital services. This is often posed as an inherent privacy trade-off: we can benefit from data analysis or retain data privacy, but not both. Across several disciplines, a vast amount of effort has been directed toward overcoming this trade-off to enable productive uses of information without also enabling undesired misuse, a goal we term `structured transparency'. In this paper, we provide an overview of the frontier of research seeking to develop structured transparency. We offer a general theoretical framework and vocabulary, including characterizing the fundamental components -- input privacy, output privacy, input verification, output verification, and flow governance -- and fundamental problems of copying, bundling, and recursive oversight. We argue that these barriers are less fundamental than they often appear. Recent progress in developing `privacy-enhancing technologies' (PETs), such as secure computation and federated learning, may substantially reduce lingering use-misuse trade-offs in a number of domains. We conclude with several illustrations of structured transparency -- in open research, energy management, and credit scoring systems -- and a discussion of the risks of misuse of these tools.
... The reuse of health data is guided by a variety of controls from both legal and ethical perspectives, such as privacy rights, data protection regulations, and duties of confidentiality [12]. However, legal measures pose challenges for the effective reuse of clinical data [13,14]. ...
Background:
Learning from routine healthcare data is important for the improvement of the quality of care. Providing feedback on clinicians' performance in comparison to their peers has been shown to be more efficient for quality improvements. However, the current methods for providing feedback do not fully address the privacy concerns of stakeholders.
Methods:
The paper proposes a distributed architecture for providing feedback to clinicians on their clinical performances while protecting their privacy. The indicators for the clinical performance of a clinician are computed within a healthcare institution based on pseudonymized data extracted from the electronic health record (EHR) system. Group-level indicators of clinicians across healthcare institutions are computed using privacy-preserving distributed data-mining techniques. A clinician receives feedback reports that compare his or her personal indicators with the aggregated indicators of the individual's peers. Indicators aggregated across different geographical levels are the basis for monitoring changes in the quality of care. The architecture feasibility was practically evaluated in three general practitioner (GP) offices in Norway that consist of about 20,245 patients. The architecture was applied for providing feedback reports to 21 GPs on their antibiotic prescriptions for selected respiratory tract infections (RTIs). Each GP received one feedback report that covered antibiotic prescriptions between 2015 and 2018, stratified yearly. We assessed the privacy protection and computation time of the architecture.
Results:
Our evaluation indicates that the proposed architecture is feasible for practical use and protects the privacy of the patients, clinicians, and healthcare institutions. The architecture also maintains the physical access control of healthcare institutions over the patient data. We sent a single feedback report to each of the 21 GPs. A total of 14,396 cases were diagnosed with the selected RTIs during the study period across the institutions. Of these cases, 2924 (20.3%) were treated with antibiotics, where 40.8% (1194) of the antibiotic prescriptions were narrow-spectrum antibiotics.
Conclusions:
It is feasible to provide feedback to clinicians on their clinical performance in comparison to peers across healthcare institutions while protecting privacy. The architecture also enables monitoring changes in the quality of care following interventions.
