Individual privacy versus public good: protecting confidentiality in health research

Abstract

Health and medical data are increasingly being generated, collected, and stored in electronic form in healthcare facilities and administrative agencies. Such data hold a wealth of information vital to effective health policy development and evaluation, as well as to enhanced clinical care through evidence-based practice and safety and quality monitoring. These initiatives are aimed at improving individuals' health and well-being. Nevertheless, analyses of health data archives must be conducted in such a way that individuals' privacy is not compromised. One important aspect of protecting individuals' privacy is protecting the confidentiality of their data. It is the purpose of this paper to provide a review of a number of approaches to reducing disclosure risk when making data available for research, and to present a taxonomy for such approaches. Some of these methods are widely used, whereas others are still in development. It is important to have a range of methods available because there is also a range of data-use scenarios, and it is important to be able to choose between methods suited to differing scenarios. In practice, it is necessary to find a balance between allowing the use of health and medical data for research and protecting confidentiality. This balance is often presented as a trade-off between disclosure risk and data utility, because methods that reduce disclosure risk, in general, also reduce data utility.

Position Statement on Population Data Science:

“…Challenge one is achieving a balance between the need for individual privacy (protecting access to and use of data) and the public good (16)(17)(18)(19)(20)(21). This is a consideration for many dataintensive domains of science, and across virtually all countries, but will have a different focus or importance depending on the users of data, the types and source of data, and the research questions being addressed.…”

Data Safe Havens and Trust: Toward a Common Understanding of Trusted Research Platforms for Governing Secure and Ethical Health Research

“…De-identification might, however, not always be the best approach to take: in 2006 the UK Academy of Medical Sciences identified in its report on using personal data in health research that meaningful research needed varying degrees of identifiable data because “...most important research using personal data requires access to identifiable data at some point for some purpose...” [ 31 ]. This issue has surfaced in practice, where de-identification is being used as a means to limit disclosure and protect the confidentiality of health care records at the expense of data utility for research [ 32 ] and is an impediment to research itself [ 33 ]. This is further illustrated when the risk of detrimental effects to data quality and efficiency is heightened if disclosure risk is handled in isolation.…”

Are patient relationships the driver for information governance?

“…Yet anonymity cannot be guaranteed 6 and the requirement for consent as enshrined in the Common Law Duty of Confidentiality may be set aside by statute. 7 Confidentiality can be further protected by limiting disclosure outside of the care setting at the expense of data utility 8 -but is that enough to protect the Again, whereas confidentiality is the driver for protecting the relationship between doctor and patient at a human level, information governance acts as a driver for protecting data at the digital processing level. This results in a mismatch between the information protection needs and the expectations established when data are first captured inside a confidential relationship, placing the research and commissioning communities in an unenviable position.…”