Data Sharing and the Idea of Ownership

With the advent of rapid development of wearable technology and mobile computing, huge amount of personal health-related data is being generated and accumulated on continuous basis at every moment. These personal datasets contain valuable information and they belong to and asset of the individual users, hence should be owned and controlled by themselves. Currently most of such datasets are stored and controlled by different service providers and this centralised data storage brings challenges of data security and hinders the data sharing. These personal health data are valuable resources for healthcare research and commercial projects. In this research work, we propose a conceptual design for sharing personal continuous-dynamic health data using blockchain technology supplemented by cloud storage to share the health-related information in a secure and transparent manner. Besides, we also introduce a data quality inspection module based on machine learning techniques to have control over data quality. The primary goal of the proposed system is to enable users to own, control and share their personal health data securely, in a General Data Protection Regulation (GDPR) compliant way to get benefit from their personal datasets. It also provides an efficient way for researchers and commercial data consumers to collect high quality personal health data for research and commercial purposes.

BackgroundPersonal health data is crucial for effective medical care, personalized treatment, and health monitoring. It enables accurate diagnosis, efficient treatment plans, and informed healthcare decisions. Personal health data should be protected to ensure patient privacy, prevent misuse or unauthorized access, and maintain trust in healthcare systems, thereby safeguarding individuals’ sensitive information from potential harm or exploitation. Therefore, this study aimed to investigate whether perceived risk and perceived benefits have mediating roles in the relationships among individuals’ personal health information disclosure behaviour, perceived control, and privacy concerns.MethodThe population of the study consisted of individuals living in the provinces of Izmir, Konya and Adana. The sample of the study consisted of individuals who were reached through a convenience sampling method. The scales for privacy concerns, perceived control, perceived risk, perceived benefits and information disclosure behaviour were used in the study. Cronbach’s alpha and the AVE were calculated, and a confirmatory factor analysis was performed. A path analysis was performed using the structural equation model to test the hypotheses.ResultsThe analysis revealed a significant negative relationship between individuals’ personal health data disclosure behaviour and their privacy concerns. However, perceived risk and perceived benefit did not mediate this relationship. Additionally, a significant positive relationship was found between individuals’ behaviour of disclosing their perceived control and personal health data, with perceived risk and benefits playing a mediating role in this relationship.ConclusionThe study concluded that as individuals’ concerns about sharing personal health data increase, they are less likely to share these data. It was also found that perceived risk and perceived benefit mediate this relationship. Additionally, higher perceived risk intensifies privacy concerns, further discouraging data sharing, while perceived benefits can mitigate these concerns, promoting greater willingness to disclose health information.

Hassas kişisel veriler kategorisi içinde yer alan kişisel sağlık verileri, “kimliği belirli ya da belirlenebilir gerçek kişiye ilişkin her türlü sağlık bilgisi” olarak tanımlanabilir. Bu tanımın kapsamına kişinin “geçmişi, şimdiki anı ve geleceğine ilişkin fiziksel ve ruhsal sağlığı hakkında her türlü bilgi ile kişiye sunulan sağlık hizmetiyle ilgili bilgiler” dahildir. Bu çalışmada öncelikle, ulusal ve Türkiye’nin taraf olduğu uluslararası düzenlemeler ışığında, kişisel sağlık verileri kavramı ve bu verilerin işlenmesine ilişkin kurallar ve usuller ele alınacaktır. Türk hukukunda kişisel sağlık verilerinin işlenebileceği hallere ayrıca değinilecektir. Bu bağlamda, veri koruma hukukunda, hassas verilerin işlenmesinin diğer verilere kıyasla daha katı bir denetime tabi tutulması anlamına gelen hassaslık ilkesi ve veri toplama ve işlemenin bağlı olduğu amaç/amaçları gerçekleştirmek için zorunlu olan miktarla sınırlı olacak şekilde veri toplanması gerekliliğini ifade eden minimumluk ilkesine uygunluk değerlendirmesi yapılacaktır. Ardından, idarenin kişisel sağlık verilerinin korunmasına ilişkin yükümlülükleri açıklanacaktır. İdarenin kişisel sağlık verilerine ilişkin aydınlatma, veri güvenliğini sağlama, düzenleme ve denetleme yapma yükümlülüklerinin anlamı ve kapsamı ortaya konulacaktır. Avrupa İnsan Hakları Mahkemesi ve Danıştay kararları ışığında, idarenin söz konusu yükümlülüklerini yerine getirmemesi durumunun nasıl ortaya çıkabileceği ve ortaya çıkış şekillerine göre sorumluluğunun türünün ve kapsamının ne olacağı ihtimallere göre değerlendirilecektir.

The aim of this paper is to encourage critical investigations of information governance related to personal health data. Presently, most societies have regulations that balance the protection of privacy of personal health data with the need to disclose personal data for public good. This balance seems to be challenged by recent technological and policy developments, and this paper seeks to illuminate some of these developments and the associated dilemmas that emerge. Firstly, the challenges of handling novel data types from new sources (such as smartphones, devices and sensors) is still poorly understood and regulated. Secondly, not only are medical and societal gains predicted from giving access to personal health data, but also expectations of economic value creation circulate. Thirdly, the emerging data-intensive scientific practices depend on data, both as raw material and for building the scientific toolbox (models, methodologies and repositories). Being observant on these developments can facilitate interventions into the ongoing “ethics-in-the-making” of these crucial processes.

With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language. This knowledge can increase their behavioral awareness and allow them to take action to meet their health goals. It can also bridge the gap between the vast collection of personal health data and the summary generation required to describe an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal(Weber and Achananuparp 2016) and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.

As people deal with cardiovascular disease (CVD), they are to self-monitor routinely and be aware of complications and the corresponding course of action. Engaging in these self-care behaviors is conducive to gaining knowledge of health status. Even so, knowledge of the self may be insufficient in making sense of chronic conditions. In constructing a new normal after health-related life disruptions, people often turn to peers (others facing similar health issues) and share personal health information with each other. Although health information-sharing behavior is well-documented, it remains underexplored what attitudes individuals with chronic conditions, such as CVD, have toward disclosing personal health data to peers and exploring those of others with similar conditions. We surveyed 39 people who reported being diagnosed with CVD to understand how they conceptualize sharing personal health data with their peers. By analyzing qualitative survey data thematically, we found that respondents expressed themselves as uncertain about the benefits of interacting with peers in such a manner. At the same time, they recognized an opportunity to learn new ideas to enhance CVD self-care in mutual data sharing. We also report participants’ analytical orientation toward this sort of data sharing herein and elaborate on what sharing a range of personal health data could mean. In light of the existing literature, this study unpacks the notion of sharing in a different population/pathology and with more nuance, particularly by distinguishing between disclosing one’s data and exploring others’.

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests (p < 0.05) to compare the testing data set performance of each machine learning model to that of the Breast Cancer Risk Prediction Tool (BCRAT), an implementation of the Gail model. None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.

IntroductionResearch data combined with administrative data provides a robust resource capable of answering unique research questions. However, in cases where personal health data are encrypted, due to ethics requirements or institutional restrictions, traditional methods of deterministic and probabilistic record linkages are not feasible. Instead, privacy-preserving record linkages must be used to protect patients’ personal data during data linkage.ObjectivesTo determine the feasibility and validity of a deterministic privacy preserving data linkage protocol using homomorphically encrypted data.MethodsFeasibility was measured by the number of records that successfully matched via direct identifiers. Validity was measured by the number of records that matched with multiple indirect identifiers. The threshold for feasibility and validity were both set at 95%. The datasets shared a single, direct identifier (health card number) and multiple indirect identifiers (sex and date of birth). Direct identifiers were encrypted in both datasets and then transferred to a third-party server capable of linking the encrypted identifiers without decrypting individual records. Once linked, the study team used indirect identifiers to verify the accuracy of the linkage in the final dataset.ResultsWith a combination of manual and automated data transfer in a sample of 8,128 individuals, the privacy-preserving data linkage took 36 days to match to a population sample of over 3.2 million records. 99.9% of the records were successfully matched with direct identifiers, and 99.8% successfully matched with multiple indirect identifiers. We deemed the linkage both feasible and valid.ConclusionsAs combining administrative and research data becomes increasingly common, it is imperative to understand options for linking data when direct linkage is not feasible. The current linkage process ensured the privacy and security of patient data and improved data quality. While the initial implementations required significant computational and human resources, increased automation keeps the requirements within feasible bounds.

More than half of the natural vegetation in Mexico is managed collectively within common property systems. The appropriation and continuity of government programs related to the conservation of land that is communally used is proposed to depend on the level of organization of the communities and the interaction between the local and governmental institutions, as well as the benefits derived from conservation projects. Patterns of what drives the conservation of common natural resources were analyzed in order to propose improvements to conservation policy. Changes in primary and secondary vegetation cover in common and private properties were identified by performing a historical spatial analysis. Questionnaires were used to survey 32 populations of seven states of the Mexican Republic to determine the conservation status of common property resources, as well as the ability of the community to continue conservation activities initially undertaken by government programs. Some 53% of the primary and secondary vegetation in Mexico is found in common property areas, but the change from primary and secondary vegetation to other uses is the same for common and private property. Communities with a high level of conservation of communal areas and with the ability to continue conservation projects were those that had dedicated the areas to recreation and conservation, had stronger community organization and were less marginalized. A recognition of the heterogeneity of the socioeconomic and cultural context of communities with common property is necessary to design governmental conservation programs that achieve long-term conservation. To meet the needs of a region that is both degraded and marginalized, the creation of synergies between programs that combat poverty and programs that promote conservation is needed. In addition, the continuation of payments with public funds for work that preserves or rehabilitates natural areas is needed, thereby acknowledging the environmental services that these areas generate.

Blockchain has unique advantages in data privacy protection and data integrity. We can solve many security problems, such as the single point of failure and data sharing in the current centralized system through blockchain approach. Existing studies have demonstrated that the application of blockchain in the medical system could improve the patient's medical experience. However, we found that the blockchain-based medical systems didn't consider the problems of long insurance claim cycle and complicated procedures. There are also very few healthcare systems that provide targeted sharing protocols for medical data and personal health data. In this paper, we propose a multi-role healthcare data sharing system framework based on blockchain. In this system, we reduce the storage cost through the collaborative storage of blockchain and IPFS. Then we design a smart contract on insurance to help patients achieve automatic insurance claim. In addition, we design two different sharing protocols to realize the fine management of personal data. The system analysis shows that our proposed blockchain-based multi-role healthcare data sharing system can effectively address the actual needs of users and has perfect performance in data storage, privacy protection, insurance claims and personal data management.

How private is your mental health app data? An empirical study of mental health app privacy policies and practices

Industrial data ecosystems are inter-organizational forms of cooperation emerging around sharing data. They arise from a digital infrastructure, giving data providers and data users a platform to share and (re-)use data. Data spaces are among the digital infrastructures frequently associated with data ecosystems, as they supply a shared digital space for its participants to share data in a sovereign way. Data spaces aim to close a gap in the digital infrastructure landscape, addressing concerns of organizations when sharing data, such as data misappropriation or a lack of control of shared data. They do this by implementing data sovereignty—typically through Usage Control Policies—that give data providers the means to formalize semantically and technically how data users are allowed to use their data. In this fundamentals article, we address the following issues: (1) contextualizing and demarcating data spaces and data ecosystems, (2) systematizing data spaces in the research and policy landscape, and (3) elaborating on a research agenda for Information Systems (IS) research.

Despite large investment cancer continues to be a major source of mortality and morbidity throughout the world. Traditional methods of detection and diagnosis such as biopsy and imaging, tend to be expensive and have risks of complications. As data becomes more abundant and machine learning continues advancing, it is natural to ask how they can help solve some of these problems. In this paper we show that using a person's personal health data it is possible to predict their risk for a wide variety of cancers. We dub this process a “statistical biopsy.” Specifically, we train two neural networks, one predicting risk for 16 different cancer types in females and the other predicting risk for 15 different cancer types in males. The networks were trained as binary classifiers identifying individuals that were diagnosed with the different cancer types within 5 years of joining the PLOC trial. However, rather than use the binary output of the classifiers we show that the continuous output can instead be used as a cancer risk allowing a holistic look at an individual's cancer risks. We tested our multi-cancer model on the UK Biobank dataset showing that for most cancers the predictions generalized well and that looking at multiple cancer risks at once from personal health data is a possibility. While the statistical biopsy will not be able to replace traditional biopsies for diagnosing cancers, we hope there can be a shift of paradigm in how statistical models are used in cancer detection moving to something more powerful and more personalized than general population screening guidelines.

Improving Diagnosis Estimation by Considering the Periodic Span of the Life Cycle Based on Personal Health Data

An experimental study exposed participants (N = 299) to different message frames to investigate whether Family Communication Patterns (FCP) and message characteristics influenced willingness to communicate about Family Health History (FHH). Message frames were either collective (our), individual (mine), or control (no pronouns). Afterward, participants were asked about their perceived collective psychological ownership of health information, attitudes, subjective norms, and FHH communication intentions. Although the message frames had no impact on perceived collective psychological ownership of health information, conversation orientation and conformity orientation (respecting parental authority) were positively associated with perceived collective psychological ownership of health information. Additionally, perceived collective psychological ownership, attitudes, and subjective norms were found to have indirect effects between FCP and FHH communication intentions. These findings provide further support that FCP influences how health message appeals are processed and suggest interventions could be tailored to FCP orientations for effective FHH behaviors.