Personal Health Data Research Articles

Viewing the Law and Ethics of Premortem Interventions Through an Interpretive Lens.

Viewing the Law and Ethics of Premortem Interventions Through an Interpretive Lens.

Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review

This paper reviews nationally representative public opinion surveys on artificial intelligence (AI) in the United States, with a focus on areas related to health care. The potential health applications of AI continue to gain attention owing to their promise as well as challenges. For AI to fulfill its potential, it must not only be adopted by physicians and health providers but also by patients and other members of the public. This study reviews the existing survey research on the United States' public attitudes toward AI in health care and reveals the challenges and opportunities for more effective and inclusive engagement on the use of AI in health settings. We conducted a systematic review of public opinion surveys, reports, and peer-reviewed journal articles published on Web of Science, PubMed, and Roper iPoll between January 2010 and January 2022. We include studies that are nationally representative US public opinion surveys and include at least one or more questions about attitudes toward AI in health care contexts. Two members of the research team independently screened the included studies. The reviewers screened study titles, abstracts, and methods for Web of Science and PubMed search results. For the Roper iPoll search results, individual survey items were assessed for relevance to the AI health focus, and survey details were screened to determine a nationally representative US sample. We reported the descriptive statistics available for the relevant survey questions. In addition, we performed secondary analyses on 4 data sets to further explore the findings on attitudes across different demographic groups. This review includes 11 nationally representative surveys. The search identified 175 records, 39 of which were assessed for inclusion. Surveys include questions related to familiarity and experience with AI; applications, benefits, and risks of AI in health care settings; the use of AI in disease diagnosis, treatment, and robotic caregiving; and related issues of data privacy and surveillance. Although most Americans have heard of AI, they are less aware of its specific health applications. Americans anticipate that medicine is likely to benefit from advances in AI; however, the anticipated benefits vary depending on the type of application. Specific application goals, such as disease prediction, diagnosis, and treatment, matter for the attitudes toward AI in health care among Americans. Most Americans reported wanting control over their personal health data. The willingness to share personal health information largely depends on the institutional actor collecting the data and the intended use. Americans in general report seeing health care as an area in which AI applications could be particularly beneficial. However, they have substantial levels of concern regarding specific applications, especially those in which AI is involved in decision-making and regarding the privacy of health information.

To have and to hold: An exploratory qualitative study exploring why research participants with treatment-resistant depression undergoing transcranial magnetic stimulation treatment requested copies of their research brain MRI scans

PurposeThere has been little research providing an in-depth exploration of the reasons behind research participants, particularly in mental health settings, requesting copies of their research data, such as magnetic resonance imaging (MRI) scans. BRIGhTMIND is a large double blind randomised controlled trial using functional and structural magnetic resonance imaging to create personalised targets for transcranial magnetic stimulation delivery, and a number of trial participants requested copies of these scans. MethodsSeven participants involved in the BRIGhTMIND trial completed semi-structured interviews exploring their reasons behind their request for copies of their MRI scans. The qualitative data was co-analysed between researchers and patient and public involvement and engagement representatives using inductive thematic analysis. ResultsThe interviews produced consistent themes concerning curiosity to visualise their MRI scans, and the hope that their participation would result in a better understanding of the nature and future treatment of depression. Concerns around the rights to access their own personal health data emerged as a clear theme as did their own ability to interpret any radiological information. DiscussionThis study provides insight into the reasons why research participants with depression would like to retain copies of their MRI scans and the perceived role that such techniques may have for improving research and neuromodulation treatments in depression. Such first-hand experiential accounts emphasises the importance of listening to participants perspectives and lived experience, in order to improve research and health outcomes. Future research could aim to provide greater verbal and written information for participants, including details about the accessibility to their MRI scans, the difference between research and clinical MRI scans, and educational materials to help with the interpretation of MRI images.

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in each domain is driven by a growing body of annotated data, increased computational resources, and technological innovations. In medicine, the sensitivity of the data, the complexity of the tasks, the potentially high stakes, and a requirement of accountability give rise to a particular set of challenges. In this review, we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making. 1) Explainable AI aims to produce a human-interpretable justification for each output. Such models increase confidence if the results appear plausible and match the clinicians expectations. However, the absence of a plausible explanation does not imply an inaccurate model. Especially in highly non-linear, complex models that are tuned to maximize accuracy, such interpretable representations only reflect a small portion of the justification. 2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains. For example, a classification task based on images acquired on different acquisition hardware. 3) Federated learning enables learning large-scale models without exposing sensitive personal health information. Unlike centralized AI learning, where the centralized learning machine has access to the entire training data, the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates, not personal health data. This narrative review covers the basic concepts, highlights relevant corner-stone and state-of-the-art research in the field, and discusses perspectives.

The social representation and social action effect of critical issues: autonomic system and self-report measures

By combining words and images that impact emotions and generate empathetic storytelling, advertising (ADV) has evolved into a form of communication for promoting consumer awareness, positive social change, and ADV-related decisional processes, even on topics of high-social relevance such as crisis communication. This study explored consumers’ emotional and cognitive responses to crisis-related ADVs using implicit (autonomic) and explicit (self-report) measurements. Nineteen participants watched twelve high-impact social communications about Covid-19, personal health, safety, and prosociality, while autonomic and self-report data were collected. Personal health, safety, and prosociality had higher skin conductance than Covid-19 stimuli, indicating higher arousal and engagement. Personal health reported lower heart rate variability values than Covid-19, suggesting greater emotional reactions for personal health topics, but also lesser mental load for Covid-19 stimuli. Self-report results confirmed autonomic findings. In conclusion, communications about personal health, safety, and prosociality generate higher emotional impact and allow for effective storytelling that facilitates viewer identification, developing a high level of empathy.

NACDA: Naming-Based Access Control and Decentralized Authorization for Secure Many-to-Many Data Sharing

The rapid development of wearable technology has facilitated the collection and sharing of health data, allowing patients to benefit from caretakers and medical research. However, these personal health data often contain sensitive information and it is typically not known in advance with whom the information will be shared. Therefore, messages must be encrypted and shared while adhering to the decoupled communication model. This paper presents NACDA, a secure many-to-many data-sharing service on the Named Data Network (NDN). NACDA uses Identity-Based Encryption with Wildcard Key Derivation (WKD-IBE) to allow naming-based access control, enabling data subjects to share data securely and flexibly regardless of the data processor. In addition, NACDA supplements a decentralized authorization mechanism with blockchain to ensure data subjects’ data ownership and enforce access policies. We developed an NDN-based prototype and performed a security analysis to demonstrate NACDA’s feasibility.

Personal Genomes in Practice: Exploring Citizen and Healthcare Professionals' Perspectives on Personalized Genomic Medicine and Personal Health Data Spaces Using a Mixed-Methods Design.

Ongoing health challenges, such as the increased global burden of chronic disease, are increasingly answered by calls for personalized approaches to healthcare. Genomic medicine, a vital component of these personalization strategies, is applied in risk assessment, prevention, prognostication, and therapeutic targeting. However, several practical, ethical, and technological challenges remain. Across Europe, Personal Health Data Space (PHDS) projects are under development aiming to establish patient-centered, interoperable data ecosystems balancing data access, control, and use for individual citizens to complement the research and commercial focus of the European Health Data Space provisions. The current study explores healthcare users' and health care professionals' perspectives on personalized genomic medicine and PHDS solutions, in casu the Personal Genetic Locker (PGL). A mixed-methods design was used, including surveys, interviews, and focus groups. Several meta-themes were generated from the data: (i) participants were interested in genomic information; (ii) participants valued data control, robust infrastructure, and sharing data with non-commercial stakeholders; (iii) autonomy was a central concern for all participants; (iv) institutional and interpersonal trust were highly significant for genomic medicine; and (v) participants encouraged the implementation of PHDSs since PHDSs were thought to promote the use of genomic data and enhance patients' control over their data. To conclude, we formulated several facilitators to implement genomic medicine in healthcare based on the perspectives of a diverse set of stakeholders.

Liver cancer risk quantification through an artificial neural network based on personal health data

Background Liver cancer is one of the most common types of cancer and the third leading cause of cancer-related deaths globally. The most common type of primary liver cancer is called hepatocellular carcinoma (HCC) which accounts for 75–85% of cases. HCC is a malignant disease with aggressive progression and limited therapeutic options. While the exact cause of liver cancer is not known, habits/lifestyles may increase the risk of developing the disease. Material and methods This study is designed to quantify the liver cancer risk through a multi-parameterized artificial neural network (ANN) based on basic health data including habits/lifestyles. In addition to input and output layers, our ANN model has three hidden layers having 12, 13, and 14 neurons, respectively. We have used the health data from the National Health Interview Survey (NHIS) and Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) datasets to train and test our ANN model. Results We have found the best performance of the ANN model with an area under the receiver operating characteristic curve of 0.80 and 0.81 for training and testing cohorts, respectively. Conclusion Our results demonstrate a method that can predict liver cancer risk with basic health data and habits/lifestyles. This novel method could be beneficial to high-risk populations by enabling early detection.

Blockchain, Information Security, Control, and Integrity: Who Is in Charge?

Blockchain technology has attracted substantial interest in recent years, most notably for its effect on global economics through the advent of cryptocurrency. Within the health care domain, blockchain technology has been actively explored as a tool for improving personal health data management, medical device security, and clinical trial management. Despite a strong demand for innovation and cutting-edge technology in plastic surgery, integration of blockchain technologies within plastic surgery is in its infancy. Recent advances and mainstream adoption of blockchain are gaining momentum and have shown significant promise for improving patient care and information management. In this article, the authors explain what defines a blockchain and discuss its history and potential applications in plastic surgery. Existing evidence suggests that blockchain can enable patient-centered data management, improve privacy, and provide additional safeguards against human error. Integration of blockchain technology into clinical practice requires further research and development to demonstrate its safety and efficacy for patients and providers.

Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis

While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients’ trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients’ privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an “avatar data”, for each initial sensitive individual. This method, compared with 2 other synthetic data generation techniques (Synthpop, CT-GAN), is applied to real health data with a clinical trial and a cancer observational study to evaluate the protection it provides while retaining the original statistical information. Compared to Synthpop and CT-GAN, the Avatar method shows a similar level of signal maintenance while allowing to compute additional privacy metrics. In the light of distance-based privacy metrics, each individual produces an avatar simulation that is on average indistinguishable from 12 other generated avatar simulations for the clinical trial and 24 for the observational study. Data transformation using the Avatar method both preserves, the evaluation of the treatment’s effectiveness with similar hazard ratios for the clinical trial (original HR = 0.49 [95% CI, 0.39–0.63] vs. avatar HR = 0.40 [95% CI, 0.31–0.52]) and the classification properties for the observational study (original AUC = 99.46 (s.e. 0.25) vs. avatar AUC = 99.84 (s.e. 0.12)). Once validated by privacy metrics, anonymous synthetic data enable the creation of value from sensitive pseudonymized data analyses by tackling the risk of a privacy breach.

Examining public views on decentralised health data sharing.

In recent years, researchers have begun to explore the use of Distributed Ledger Technologies (DLT), also known as blockchain, in health data sharing contexts. However, there is a significant lack of research that examines public attitudes towards the use of this technology. In this paper, we begin to address this issue and present results from a series of focus groups which explored public views and concerns about engaging with new models of personal health data sharing in the UK. We found that participants were broadly in favour of a shift towards new decentralised models of data sharing. Retaining 'proof' of health information stored about patients and the capacity to provide permanent audit trails, enabled by immutable and transparent properties of DLT, were regarded as particularly valuable for our participants and prospective data custodians. Participants also identified other potential benefits such as supporting people to become more health data literate and enabling patients to make informed decisions about how their data was shared and with whom. However, participants also voiced concerns about the potential to further exacerbate existing health and digital inequalities. Participants were also apprehensive about the removal of intermediaries in the design of personal health informatics systems.

Moral autonomy of patients and legal barriers to a possible duty of health related data sharing

Informed consent bears significant relevance as a legal basis for the processing of personal data and health data in the current privacy, data protection and confidentiality legislations. The consent requirements find their basis in an ideal of personal autonomy. Yet, with the recent advent of the global pandemic and the increased use of eHealth applications in its wake, a more differentiated perspective with regards to this normative approach might soon gain momentum. This paper discusses the compatibility of a moral duty to share data for the sake of the improvement of healthcare, research, and public health with autonomy in the field of data protection, privacy and medical confidentiality. It explores several ethical-theoretical justifications for a duty of data sharing, and then reflects on how existing privacy, data protection, and confidentiality legislations could obstruct such a duty. Consent, as currently defined in the General Data Protection Regulation – a key legislative framework providing rules on the processing of personal data and data concerning health – and in the recommendation of the Council of Europe on the protection of health-related data – explored here as soft-law – turns out not to be indispensable from various ethical perspectives, while the requirement of consent in the General Data Protection Regulation and the recommendation nonetheless curtails the full potential of a duty to share medical data. Also other legal grounds as possible alternatives for consent seem to constitute an impediment.

Data governance spaces: The case of a national digital service for personal health data

Data governance spaces: The case of a national digital service for personal health data

Zk-AuthFeed: Protecting Data Feed to Smart Contracts With Authenticated Zero Knowledge Proof

The emerging blockchain technology, combined with the smart contract paradigm, is expected to transform traditional applications with decentralization. When the blockchain technology is applied to decentralize traditional applications, blockchain validators may need to take in sensitive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off-chain</i> data to execute a smart contract. On the one hand, decentralized applications (DApps) require authentic off-chain input data to correctly execute a given business procedure. On the other hand, users are reluctant to expose their sensitive privacy on the blockchain. For example, for a decentralized medical insurance DApp that takes as input personal health data, it is critical to guarantee authenticity and privacy of the data sent to the smart contract, such that the data can be verified by validators without leaking sensitive information. However, no satisfactory solution has been proposed to attain privacy and authenticity at the same time. In this work, we first present a highly efficient authenticated zero knowledge proof protocol called zk-DASNARK by extending the classical zk-SNARK scheme with data authentication. Based on zk-DASNARK, we design zk-AuthFeed, a zero-knowledge authenticated off-chain data feed scheme to achieve both data privacy and authenticity for blockchain-based DApps. Following the strategy of “compute off-chain and verify on-chain”, zk-AuthFeed can significantly reduce computation cost of blockchain validators. We fully implement a prototype of zk-AuthFeed, and conduct comprehensive experiments on a medical insurance DApp. We consider 4 typical computation models for insurance premium/reimbursement in the experiments. It shows that zk-AuthFeed is highly efficient: key generation takes about 10 seconds only, proof generation takes less than 4 seconds, and proof verification takes less than 40 ms.

A study on next-generation digital tool for health data management: the e-Pulse portal

PurposeThis study aims to investigate the working way of the e-Pulse portal in Türkiye as a sample of a next-generation digital tool for health data management. Accordingly, this study focuses on explaining the structure and key services of the e-Pulse portal in the context of health data management.Design/methodology/approachThis study is a technical paper that will explain how the e-Pulse portal works in Türkiye. Accordingly, the data are based on secondary sources and mostly the official website of the e-Pulse portal. As a sample case, this study investigates the e-Pulse portal from Türkiye. The data are categorized by tables, and some key factors are classified based on review results.FindingsAs a result of the review of the e-Pulse portal's sample account, it is seen that the e-Pulse portal provides comprehensive data for personal health data for both individuals and healthcare professionals. By permitting healthcare professionals, users or patients can share their personal health data on specific dates and numbers whenever they need and want. When sharing recorded personal health data, citizens or patients can get more efficient healthcare service on the time.Research limitations/implicationsBy giving descriptive evidence and review through the e-Pulse portal, countries with high-populated can see the key e-services and elements to manage health data through digital tools. On the other side, this study has some limitations. This study investigated the e-Pulse portal and its e-services for Türkiye and gave some findings mostly based on subjective deduction. Another digital portal can give different findings for the literature.Practical implicationsBased on the e-Pulse portal case, it is determined that by creating a digital portal with recorded personal up-to-date health data, healthcare services can be ensured more efficiently among high-populated countries in the long term. While population growth and pandemic possibilities such as COVID-19 increase throughout the world, serving more patients with these portals will increase efficiency and service quality, provided that patient information is well protected.Originality/valueThis study reveals key e-services and segments to provide personal health data management by a next-generation digital tool based on the e-Pulse portal. The main contribution of this study is expected to guide other countries when adapting next-generation technology or systems to manage health data in the future.

Processing personal health data in the context of the European online pharmacy market: lawful bases under the GDPR and Swedish law.

Over the past three years, the European online pharmacy market has grown significantly and is expected to continue to grow at a rapid pace. One of the key factors that may further catalyse this growth is the personal health data that online pharmacies can collect. If online pharmacies use these data lawfully and with the public interest in mind, this may provide the opportunity to positively transform not only the online pharmacy market but also the entire healthcare industry. In order to maximise the benefits of these personal health data for improving public health but at the same time protect European citizens’ privacy, further analysis of the data protection laws and their applicability in the context of the online pharmacy market is necessary. Currently, there still lacks clarity, for example, regarding the lawful bases under the GDPR for processing personal health data for scientific research purposes. Therefore, this article identifies the main purposes for which online pharmacies may choose to process personal health data and which lawful bases under the GDPR may be suitable to address them. This article will additionally address Swedish law in order to provide an example of the interaction between national laws and the GDPR when processing personal health data in the online pharmacy market.

Medical Image Encryption by Content-Aware DNA Computing for Secure Healthcare

There exists a rising concern on security of healthcare data and service. Even small lost, stolen, displaced, hacked, or communicated in personal health data could bring huge damage to patients. Therefore, we propose a novel content-aware deoxyribonucleic acid (DNA) computing system to encrypt medical images, thus guaranteeing privacy and promoting secure healthcare environment. The proposed system consists of sender and receiver to perform tasks of encryption and decryption, respectively, where both contain the same structure design, but perform opposite operations. In either sender or receiver, we design a randomly DNA encoding and a content-aware permutation and diffusion module. Considering introducing random mechanism to increase difficulty of cracking, the former module builds a random encryption rule selector in DNA encoding process by randomly mapping quantity of medical image pixels to outputs. Meanwhile, the latter module constructs a permutation sequence, which not only encodes information of pixel values, but also involves redundant correlation between adjacent pixels located in a patch. Such design brings awareness property of medical image content to greatly increase complexity in cracking by embedding semantical information for encryption. We demonstrate that the proposed system successfully improve cybersecurity of medical images against various attacks in robustness and effectiveness when transmitting data in wireless broadcasting scenarios.

Statistical biopsy: An emerging screening approach for early detection of cancers.

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.

Decentralized semantic provision of personal health streams

Personalized healthcare is nowadays driven by the increasing volumes of patient data, observed and produced continuously thanks to medical devices, mobile sensors, patient-reported outcomes, among other data sources. This data is made available as streams, due to their dynamic nature, which represents an important challenge for processing, querying and interpreting the incoming information. In addition, the sensitive nature of healthcare data poses significant restrictions regarding privacy, which has led to the emergence of decentralized personal data management systems. Data semantics play a key role in order to enable both decentralization and integration of personal health data, as they introduce the capability to represent knowledge and information using ontologies and semantic vocabularies. In this paper we describe the SemPryv system, which provides the means to manage personal health data streams enriched with semantic information. SemPryv is designed as a decentralized system, so that users have the possibility of hosting their personal data at different sites, while keeping control of access rights. The semantization of data in SemPryv is implemented through different strategies, ranging from rule-based annotation to machine learning-based suggestions, fed from third-party specialized healthcare metadata providers. The system has been made available as Open Source, and is integrated as part of the Pryv.io platform used and commercialized in the healthcare and personal data management industry.

Initial Development of User-Based Quality Evaluation Questionnaire of Smartwatch Technology for Applying to Healthcare.

Smartwatches are a consumer wearable device offering a potential, practical, and affordable method to collect personal health data in healthy adults. For patients with chronic diseases, this would enable symptom monitoring and aid clinical decision making. Therefore, providing customized checklists to recommend smartwatches is beneficial. However, few studies have evaluated the practical functions of smartwatches and their influence on user acceptance. We aimed at developing a reliable tool to assess the quality of smartwatches from the users' perspective. To develop the smartwatch rating scale (SWRS), we conducted a comprehensive literature review as well as reviewed relevant websites. The SWRS includes 22 items for the usability (usability, functionality, safety, material, and display) and five items for the acceptance and adoption domain (satisfaction and intention). We measured the scale's internal consistency and inter-rater reliability by evaluating seven smartwatches. The overall scale demonstrated an excellent level of internal consistency (Cronbach's alpha = 0.91), with each subscale's internal consistency above good level (0.74 ~ 0.92). Inter-rater reliability using intraclass correlation coefficients (ICC) was at good level (2-way random ICC = 0.82, 95% CI 0.09 - 0.97). The SWRS is reliable, which can meet the need for assessment of smartwatch technology for utilizing in personal healthcare. Accounting for users' perspectives will help make the most of technology without impairing the human aspects of care, this study can help consumers choose a smartwatch based on their preferences and provide guidelines for developing user-friendly wearable devices aimed at health behavior changes.