Evaluating the Balance Between Privacy and Access in Digital Information Sharing.

OBJECTIVES: Access to personal health records in an ICU by persons involved in the patient's care (referred to broadly as "family members" below) has the potential to increase engagement and reduce the negative psychologic sequelae of such hospitalizations. Currently, little is known about patient preferences for information sharing with a designated family member in the ICU. We sought to understand the information-sharing preferences of former ICU patients and their family members and to identify predictors of information-sharing preferences. DESIGN: We performed an internet survey that was developed by a broad, multidisciplinary team of stakeholders. Formal pilot testing of the survey was conducted prior to internet survey administration to study subjects. SETTING: Internet survey. SUBJECTS: Subjects included English-speaking adults who had an ICU experience or a family member with ICU experience between 2013 and 2016. We used panel sampling to ensure an ethnically representative sample of the U.S. population. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One thousand five hundred twenty surveys were submitted, and 1,470 were included in analysis. The majority of respondents (93.6%) stated that they would want to share present and past medical history, either all or that related to their ICU stay, with a designated family member of their choosing. The majority (79%) would also want their designated family member to be able to access that information from a home computer. Although most respondents preferred to share all types of information, they indicated varying levels of willingness to share specific types of more sensitive information. Information-sharing preferences did not differ by age, sex, ethnicity, or type of prior experience in the ICU (i.e., patient or family member). CONCLUSIONS: In the context of an ICU admission, sharing personal health information with a person of the patient's choosing appears desirable for most patients and family members. Policies and implementation of regulations should take this into consideration.

Fundamental privacy rights in a pandemic state.

Faced with the emergence of the Covid-19 pandemic, and to better understand and contain the disease's spread, health organisations increased the collaboration with other organisations sharing health data with data scientists and researchers. Data analysis assists such organisations in providing information that could help in decision-making processes. For this purpose, both national and regional health authorities provided health data for further processing and analysis. Shared data must comply with existing data protection and privacy regulations. Therefore, a robust de-identification procedure must be used, and a re-identification risk analysis should also be performed. De-identified data embodies state-of-the-art approaches in Data Protection by Design and Default because it requires the protection of direct and indirect identifiers (not just direct). This article highlights the importance of assessing re-identification risk before data disclosure by analysing a data set of individuals infected by Covid-19 that was made available for research purposes. We stress that it is highly important to make this data available for research purposes and that this process should be based on the state of the art methods in Data Protection by Design and by Default. Our main goal is to consider different re-identification risk analysis scenarios since the information on the intruder side is unknown. Our conclusions show that there is a risk of identity disclosure for all of the studied scenarios. For one, in particular, we proceed to an example of a re-identification attack. The outcome of such an attack reveals that it is possible to identify individuals with no much effort.

[Current Status and the Future of Protection and Utilization of Personal Information at Medical Facilities Based on the Understanding of Related Regulations].

In September 2015, "the Act on the Protection of Personal Information" was amended. Accordingly, "the Ethical Guidelines for Medical Research Involving Human Subjects" were also amended. "The Act on Anonymized Medical Data That Are Meant to Contribute to Research and Development in the Medical Field," which came into effect in May 2018, aims to collect and utilize medical information of each patient from medical institutions for the purpose of research and development in the medical field. Thus, the rules of personal information that need to be followed are changing considerably in the balance between importance of protection and utilization for medical development. Therefore, health care professionals and researchers are required to fully understand the current situation and the future.

Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study.

BACKGROUND: Word embeddings are dense numeric vectors used to represent language in neural networks. Until recently, there had been no publicly released embeddings trained on clinical data. Our work is the first to study the privacy implications of releasing these models. OBJECTIVE: This paper aims to demonstrate that traditional word embeddings created on clinical corpora that have been deidentified by removing personal health information (PHI) can nonetheless be exploited to reveal sensitive patient information. METHODS: We used embeddings created from 400,000 doctor-written consultation notes and experimented with 3 common word embedding methods to explore the privacy-preserving properties of each. RESULTS: We found that if publicly released embeddings are trained from a corpus anonymized by PHI removal, it is possible to reconstruct up to 68.5% (n=411/600) of the full names that remain in the deidentified corpus and associated sensitive information to specific patients in the corpus from which the embeddings were created. We also found that the distance between the word vector representation of a patient's name and a diagnostic billing code is informative and differs significantly from the distance between the name and a code not billed for that patient. CONCLUSIONS: Special care must be taken when sharing word embeddings created from clinical texts, as current approaches may compromise patient privacy. If PHI removal is used for anonymization before traditional word embeddings are trained, it is possible to attribute sensitive information to patients who have not been fully deidentified by the (necessarily imperfect) removal algorithms. A promising alternative (ie, anonymization by PHI replacement) may avoid these flaws. Our results are timely and critical, as an increasing number of researchers are pushing for publicly available health data.

Care, Convenience, Comfort, Confidentiality, and Contagion: The 5 C's that Will Shape the Future of Telemedicine.

The COVID-19 pandemic has driven rapid, widespread adoption of telemedicine. The distribution of clinicians, long travel distances, and disability all limit access to care, especially for persons with Parkinson's disease. Telemedicine is not a panacea for all of these challenges but does offer advantages. These advantages can be summarized as the 5 C's: accessible care, increased convenience, enhanced comfort, greater confidentiality to patients and families, and now reduced risk of contagion. Telemedicine also has its limitations, including the inability to perform parts of the physical examination and inequitable access to the Internet and related technologies. Future models will deliver care to patients from a diverse set of specialties. These will include mental health specialists, physiotherapists, neurosurgeons, speech-language therapists, dieticians, social workers, and exercise coaches. Along with these new care models, digital therapeutics, defined as treatments delivered through software programs, are emerging. Telemedicine is now being introduced as a bridge to restart clinical trials and will increasingly become a normal part of future research studies. From this pandemic will be a wealth of new telemedicine approaches which will fundamentally change and improve care as well as research for individuals with Parkinson's disease.

Longitudinal trends in the quality, effectiveness and attributes of highly rated smartphone health apps.

BACKGROUND: While there are numerous mental health apps on the market today, less is known about their safety and quality. This study aims to offer a longitudinal perspective on the nature of high visibility apps for common mental health and physical health conditions. METHODS: In July 2019, we selected the 10 top search-returned apps in the Apple App Store and Android Google Play Store using six keyword terms: depression, anxiety, schizophrenia, addiction, high blood pressure and diabetes. Each app was downloaded by two authors and reviewed by a clinician, and the app was coded for features, functionality, claims, app store properties, and other properties. RESULTS: Compared with 1 year prior, there were few statistically significant changes in app privacy policies, evidence and features. However, there was a high rate of turnover with only 34 (57%) of the apps from the Apple's App Store and 28 (47%) from the Google Play Store remaining in the 2019 top 10 search compared with the 2018 search. DISCUSSION: Although there was a high turnover of top search-returned apps between 2018 and 2019, we found that there were few significant changes in features, privacy, medical claims and other properties. This suggests that, although the highly visible and available apps are changing, there were no significant improvements in app quality or safety.

Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation.

The explosion of mobile health (mHealth) interventions has prompted significant investment and exploration that has extended past industry into academia. Although research in this space is emerging, it focuses on the clinical and population level impact across different populations. To realize the full potential of mHealth, an intimate understanding of how mHealth is being used by patients and potential differences in usage between various demographic groups must also be prioritized. In this viewpoint, we use our experiences in building an mHealth intervention that incorporates an iOS app, Bluetooth-enabled blood pressure cuff, and Apple Watch to share knowledge on (1) how user interaction data can be tracked in the context of health care privacy laws, (2) what is required for effective, nuanced communication between clinicians and engineers to design mHealth interventions that are patient-centered and have high clinical impact, and (3) how to handle and set up a process to handle user interaction data efficiently.

Barriers to and Facilitators of the Use of Mobile Health Apps From a Security Perspective: Mixed-Methods Study.

BACKGROUND: A large number of mobile health (mHealth) apps have been created to help users to manage their health or receive health care services. Many of these mHealth apps have proven to be helpful for maintaining or improving their users' health. However, many people still choose not to use mHealth apps or only use them for a short period. One of the reasons behind this lack of use is the concern for their health information security and privacy. OBJECTIVE: The goal of this study was to determine the relationship between users' characteristics and their security and privacy concerns and to identify desired security features in mHealth apps, which could reduce these concerns. METHODS: A questionnaire was designed and validated by the research team. This questionnaire was then used to determine mobile app users' security and privacy concerns regarding personal health data in mHealth apps as well as the security features most users' desire. A semistructured interview was used to identify barriers to and facilitators of adopting mHealth apps. RESULTS: In total, 117 randomly selected study participants from a large pool took part in this study and provided responses to the validated questionnaire and the semistructured interview questions. The results indicate that most study participants did have concerns about their privacy when using mHealth apps. They also expressed their preferences regarding several security features in mHealth apps, such as regular password updates, remote wipe, user consent, and access control. An association between their demographic characteristics and their concerns and preferences in security and privacy was identified; however, in most cases, the differences among the different demographic groups were not statistically significant, except for a few very specific aspects. These study participants also indicated that the cost of apps and lack of security features in mHealth apps were barriers for adoption, whereas having free apps, strong but easy-to-use security features, and clear user protection privacy policies might encourage them to use mHealth apps in their health management. CONCLUSIONS: This questionnaire and interview study verified the security and privacy concerns of mHealth app users, identified the desired security and privacy features, and determined specific barriers to and facilitators of users adopting mHealth apps. The results can be used to guide mHealth app developers to create apps that would be welcomed by users.

[Opportunities and challenges of blockchain technology in the healthcare industry]. / Opportunités et enjeux de la technologie blockchain dans le secteur de la santé.

Often described as a tool to build trust among stakeholders with divergent interests, blockchain technology has been of interest to many sectors since it was first used in 2008. Initially designed to record financial transactions between individuals, its applications have largely evolved with technological advances and the growing interest of international companies. In the healthcare sector, blockchain is interesting for many of its features: its immutability which makes it an excellent support for authenticating sensitive data such as clinical trials consents, the possibility of publishing smart contracts that automate and facilitate many processes or the constitution of a network that agrees on the state of the information. Much acclaimed, blockchain technology is still to be tested in real-life conditions and adapted to a particularly complex regulatory and economic context in the healthcare sector.

Geospatial blockchain: promises, challenges, and scenarios in health and healthcare.

A PubMed query run in June 2018 using the keyword 'blockchain' retrieved 40 indexed papers, a reflection of the growing interest in blockchain among the medical and healthcare research and practice communities. Blockchain's foundations of decentralisation, cryptographic security and immutability make it a strong contender in reshaping the healthcare landscape worldwide. Blockchain solutions are currently being explored for: (1) securing patient and provider identities; (2) managing pharmaceutical and medical device supply chains; (3) clinical research and data monetisation; (4) medical fraud detection; (5) public health surveillance; (6) enabling truly public and open geo-tagged data; (7) powering many Internet of Things-connected autonomous devices, wearables, drones and vehicles, via the distributed peer-to-peer apps they run, to deliver the full vision of smart healthy cities and regions; and (8) blockchain-enabled augmented reality in crisis mapping and recovery scenarios, including mechanisms for validating, crediting and rewarding crowdsourced geo-tagged data, among other emerging use cases. Geospatially-enabled blockchain solutions exist today that use a crypto-spatial coordinate system to add an immutable spatial context that regular blockchains lack. These geospatial blockchains do not just record an entry's specific time, but also require and validate its associated proof of location, allowing accurate spatiotemporal mapping of physical world events. Blockchain and distributed ledger technology face similar challenges as any other technology threatening to disintermediate legacy processes and commercial interests, namely the challenges of blockchain interoperability, security and privacy, as well as the need to find suitable and sustainable business models of implementation. Nevertheless, we expect blockchain technologies to get increasingly powerful and robust, as they become coupled with artificial intelligence (AI) in various real-word healthcare solutions involving AI-mediated data exchange on blockchains.

[Challenges of Digital Medicine]. / Herausforderungen der digitalen Medizin.

Challenges of Digital Medicine Abstract. Digitization is increasingly covering more and more sectors, including medicine. To ensure medical operation 365 × 24 hours, progressively more human and financial resources are necessary. The transformation of patient histories from paper into electronic patient records focused initially on documentation. Today, hospital information systems are increasingly used as a platform for the communication of all professionals involved in the patient process - in Switzerland, however, so far without providing patients direct access to their data. Digititizing processes intend to increase efficiency, but also to enhance clinical and administrative decision support and quality assurance. The introduction of the electronic patient record in Switzerland in 2020 is expected to provide cross-company, more complete documentation of patient care. Multimorbid patients, often treated in different institutions and by different specialists, should benefit from this in particular. Advances in artificial intelligence offer new opportunities in medicine. Challenges include ensuring reliable data protection, and better interoperability of the systems involved. Semantically structured, machine-readable data exchange is a necessity for both networked services and internationally competitive research.

Through Patients' Eyes: Regulation, Technology, Privacy, and the Future.

Privacy is commonly regarded as a regulatory requirement achieved via technical and organizational management practices. Those working in the field of informatics often play a role in privacy preservation as a result of their expertise in information technology, workflow analysis, implementation science, or related skills. Viewing privacy from the perspective of patients whose protected health information is at risk broadens the considerations to include the perceived duality of privacy; the existence of privacy within a context unique to each patient; the competing needs inherent within privacy management; the need for particular consideration when data are shared; and the need for patients to control health information in a global setting. With precision medicine, artificial intelligence, and other treatment innovations on the horizon, health care professionals need to think more broadly about how to preserve privacy in a health care environment driven by data sharing. Patient-reported privacy preferences, privacy portability, and greater transparency around privacy-preserving functionalities are potential strategies for ensuring that privacy regulations are met and privacy is preserved.

Systematic review of smartphone-based passive sensing for health and wellbeing.

OBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts.

Healthy and wellbeing activities' promotion using a Big Data approach.

The aging population and economic crisis specially in developed countries have as a consequence the reduction in funds dedicated to health care; it is then desirable to optimize the costs of public and private healthcare systems, reducing the affluence of chronic and dependent people to care centers; promoting healthy lifestyle and activities can allow people to avoid chronic diseases as for example hypertension. In this article, we describe a system for promoting an active and healthy lifestyle for people and to recommend with guidelines and valuable information about their habits. The proposed system is being developed around the Big Data paradigm using bio-signal sensors and machine-learning algorithms for recommendations.