How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Recommendations for Implementing Hepatitis C Virus Care in Homeless Shelters: The Stakeholder Perspective.

Compared with the general population, homeless individuals are at higher risk of hepatitis C infection (HCV) and may face unique barriers in receipt of HCV care. This study sought the perspectives of key stakeholders toward establishing a universal HCV screening, testing, and treatment protocol for individuals accessing homeless shelters. Four focus groups were conducted with homeless shelter staff, practice providers, and social service outreach workers (n = 27) in San Francisco, California, and Minneapolis, Minnesota. Focus groups evaluated key societal, system, and individual-level facilitators and barriers to HCV testing and management. Interviews were transcribed and analyzed thematically. The societal-level barriers identified were lack of insurance, high-out-of-pocket expenses, restriction of access to HCV treatment due to active drug and/or alcohol use, and excessive paperwork required for HCV treatment authorization from payers. System-level barriers included workforce constraints and limited health care infrastructure, HCV stigma, low knowledge of HCV treatment, and existing shelter policies. At the individual level, client barriers included competing priorities, behavioral health concerns, and health attitudes. Facilitators at the system level for HCV care service integration in the shelter setting included high acceptability and buy in, and linkage with social service providers. Conclusion: Despite societal, system, and individual-level barriers identified with respect to the scale-up of HCV services in homeless shelters, there was broad support from key stakeholders for increasing capacity for the provision of HCV services in shelter settings. Recommendations for the scale-up of HCV services in homeless shelter settings are discussed

Prospect patents, data markets, and the commons in data-driven medicine : openness and the political economy of intellectual property rights

Scholars who point to political influences and the regulatory function of patent courts in the USA have long questioned the courts’ subjective interpretation of what ‘things’ can be claimed as inventions. The present article sheds light on a different but related facet: the role of the courts in regulating knowledge production. I argue that the recent cases decided by the US Supreme Court and the Federal Circuit, which made diagnostics and software very difficult to patent and which attracted criticism for a wealth of different reasons, are fine case studies of the current debate over the proper role of the state in regulating the marketplace and knowledge production in the emerging information economy. The article explains that these patents are prospect patents that may be used by a monopolist to collect data that everybody else needs in order to compete effectively. As such, they raise familiar concerns about failure of coordination emerging as a result of a monopolist controlling a resource such as datasets that others need and cannot replicate. In effect, the courts regulated the market, primarily focusing on ensuring the free flow of data in the emerging marketplace very much in the spirit of the ‘free the data’ language in various policy initiatives, yet at the same time with an eye to boost downstream innovation. In doing so, these decisions essentially endorse practices of personal information processing which constitute a new type of public domain: a source of raw materials which are there for the taking and which have become most important inputs to commercial activity. From this vantage point of view, the legal interpretation of the private and the shared legitimizes a model of data extraction from individuals, the raw material of information capitalism, that will fuel the next generation of data-intensive therapeutics in the field of data-driven medicine

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Effects of Selection Systems on Job Search Decisions

On the basis of Gilliland\u27s (1993) model of selection system fairness, the present study investigated the relationships between selection procedures, perceived selection system fairness, and job search decisions in both hypothetical and actual organizations. We conducted two studies to test the model. In Study 1, we used an experimental method to examine job seekers\u27 perceptions of, and reactions to, five widely used selection procedures. Results suggested that applicants viewed employment interviews and cognitive ability tests as more job related than biographical inventories (biodata), personality tests, and drug tests, and that job relatedness significantly affected fairness perceptions, which in turn affected job search decisions. Study 2 examined the hypothesized relationships between the selection systems and job seekers\u27 pursuit of actual, relevant organizations. Results from both studies offer support for the hypothesized model, suggesting that selection tests have differential effects on perceived selection system validity and fairness, which affect subsequent job search decisions