Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and (...) Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Increasingly widespread adoption of health information technology tools in clinical care increases interest in ethical and legal issues related to the use of these tools for public health and the effects of these uses on the clinician-patient relationship. It is argued that patients, clinicians, and society have generally uncontroversial duties to support civil society's public health mission, information technology supports this mission, and the effects of automated and computerized public health surveillance are likely to have little if any effect on (...) the clinician-patient relationship. It is also suggested, nevertheless, that electronic public health surveillance raises interesting and important ethical issues, some of which can be addressed if not resolved by empirical research, especially regarding patient preferences about secondary use of health data and their moral obligation to contribute to population- based health. (shrink)

This is an important time to focus on the question of insurance discrimination based on health status. The nation once again is poised to embark on a major health care reform debate. Even as the number of uninsured stands at some 45 million persons, millions more may be poised to lose coverage during the worst economic downturn in generations. In addition, a large number of persons may be seriously under-insured, with coverage falling significantly below the cost of necessary health care. (...) In recent years, the proportion of insured persons who are underinsured has grown by 60% since 2003, reaching an estimated 25 million persons in 2007. Health care costs experienced by insured persons now account for more than 75% of all personal bankruptcies related to medical care. Underlying these figures is a national approach to health care financing for the non-elderly that effectively increases the odds that those who are in poor health status will be uninsured or underinsured. (shrink)

Actuarial underwriting, or discrimination based on an individual's health status, is a business feature of the voluntary private insurance market. The term “discrimination” in this paper is not intended to convey the concept of unfair treatment, but rather how the insurance industry differentiates among individuals in designing and administering health insurance and employee health benefit products. Discrimination can occur at the point of enrollment, coverage design, or decisions regarding scope of coverage. Several major federal laws aimed at regulating insurance discrimination (...) based on health status focus at the point of enrollment. However, because of multiple exceptions and loopholes, these laws offer relatively limited protections. This paper provides a brief overview of discrimination practices, the federal law, and federal reform options to manage discriminatory practices in the insurance and employee health benefit markets. (shrink)

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

One of the largest, oldest, and most interesting challenges in health care is the balancing act in which clinicians have generally uncontroversial duties both to individual patients and to communities. Physicians and nurses must — so we teach them — put patients first, and at the same time recognize that individuals are members of communities. Individuals affect the health of communities, and communities affect the health of individuals. Thus, the moral and professional duties that result are sometimes in conflict.Moreover, the (...) traditional, prosaic clinical encounter is evolving in an environment increasingly shaped by electronic health records, personal health records, pharmacogenomics and vast networks of data collection and storage for public health surveillance, human subjects research, health services evaluation, and comparative effectiveness research. Health information technology is changing everything. It would be perverse otherwise: imagine large amounts of data and information either ignored, missed, or collected and then ignored. (shrink)