Going Rogue: Mobile Research Applications and the Right to Privacy

This Article investigates whether nonsectoral state laws may serve as a viable source of privacy and security standards for mobile health research participants and other health data subjects until new federal laws are created or enforced. In particular, this Article (1) catalogues and analyzes the nonsectoral data privacy, security, and breach notification statutes of all fifty states and the District of Columbia; (2) applies these statutes to mobile-app-mediated health research conducted by independent scientists, citizen scientists, and patient researchers; and (3) proposes substantive amendments to state law that could help protect the privacy and security of all health data subjects, including mobile-app-mediated health research participants

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Virtual Trauma Team

The clinical motivation for Virtual Trauma Team is to improve quality of care in trauma care in the vital first "golden hour" where correct intervention can greatly improve likely health outcome. The motivation for Virtual Homecare Team is to improve quality of life and independence for patients by supporting care at home. The economic motivation is to replace expensive hospital-based care with homecare using wireless technology to support the patient and the carers. Results will be applied by international partners in healthcare service

Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare

Foreword: Health Care Reform in the United States—The Presidential Task Force

This essay serves as the foreword to Implementing U.S. Health Care Reform, a symposium held in 1993. The exact specifications of the new health care system depend on the package that President Clinton will send to Capitol Hill and the changes that Congress will make in the reform package. Some of the basic structures and organizing principles of the new system that are being considered by the President are already the subject of intense public scrutiny. The design being considered would involve new relations between the federal government and the states, between the public and private sectors, and between health care financing and delivery. The federal government would establish the parameters of the new system through national legislation, regulation, and guidelines, with implementation occurring principally at the state level. State flexibility would become a hallmark of the new system, with states having considerable leeway in implementation. Provided that states follow national parameters, they probably could establish very different kinds of health care systems, ranging from a single payer to managed competition within a budget. Given the strong preference among many health policy experts for a single-payer system, it is extremely important to emphasize the states\u27 authority to implement such a system. A large state, for example, might consider establishing a system of managed competition in urban areas, and a single-payer system in rural areas where effective competition is constrained by the small number of consumers and providers. The new system will provide the right to health care to all citizens and lawful residents of the United States, all of whom will receive health security cards, transferable to any area of the country. The card would also guarantee access to health care independent of employment or other eligibility criteria. Therefore, the new system will address the American public\u27s concern for long-term security in health care--coverage would be portable and move with the individual if he or she changed jobs or lived in another part of the country

Redescribing Health Privacy: The Importance of Health Policy

Current conversations about health information policy often tend to be based on three broad assumptions. First, many perceive a tension between regulation and innovation. We often hear that privacy regulations are keeping researchers, companies, and providers from aggregating the data they need to promote innovation. Second, aggregation of fragmented data is seen as a threat to its proper regulation, creating the risk of breaches and other misuse. Third, a prime directive for technicians and policymakers is to give patients ever more granular methods of control over data. This article questions and complicates those assumptions, which I deem (respectively) the Privacy Threat to Research, the Aggregation Threat to Privacy, and the Control Solution. This article is also intended to enrich our concepts of “fragmentation” and “integration” in health care. There is a good deal of sloganeering around “firewalls” and “vertical integration” as idealized implementations of “fragmentation” and “integration” (respective). The problem, though, is that terms like these (as well as “disruption”) are insufficiently normative to guide large-scale health system change. They describe, but they do not adequately prescribe. By examining those instances where: a) regulation promotes innovation, and b) increasing (some kinds of) availability of data actually enhances security, confidentiality, and privacy protections, this article attempts to give a richer account of the ethics of fragmentation and integration in the U.S. health care system. But, it also has a darker side, highlighting the inevitable conflicts of values created in a “reputation society” driven by stigmatizing social sorting systems. Personal data control may exacerbate social inequalities. Data aggregation may increase both our powers of research and our vulnerability to breach. The health data policymaking landscape of the next decade will feature a series of intractable conflicts between these important social values

Designing the Health-related Internet of Things: Ethical Principles and Guidelines

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System