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)

Calls are increasing for American health care to be organized as a learning health care system, defined by the Institute of Medicine as a health care system “in which knowledge generation is so embedded into the core of the practice of medicine that it is a natural outgrowth and product of the healthcare delivery process and leads to continual improvement in care.” We applaud this conception, and in this paper, we put forward a new ethics framework for it. No such (...) framework has previously been articulated. The goals of our framework are twofold: to support the transformation to a learning health care system and to help ensure that learning activities carried out within such a system are conducted in an ethically acceptable fashion. (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)

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)