According to many experts, a public health emergency arising from an influenza pandemic, bioterrorism attack, or natural disaster is likely to develop in the next few years. Meeting the public health and medical response needs created by such an emergency will likely involve volunteers, health care professionals, public and private hospitals and clinics, vaccine manufacturers, governmental authorities, and many others. Conducting response activities in emergency circumstances may give rise to numerous issues of liability, and medical professionals and other potential responders have expressed concern about liability exposure. Providers may face inadequate resources, an insufficient number of qualified personnel, overwhelming demand for services, and other barriers to providing optimal treatment, which could lead to injury or even death in some cases. This article describes the different theories of liability that may be used by plaintiffs and the sources of immunity that are available to public health emergency responders in the public sector, private sector, and as volunteers. It synthesizes the existing immunity landscape and analyzes its gaps. Finally, the authors suggest consideration of the option of a comprehensive immunity provision that addresses liability protection for all health care providers during public health emergencies and that, consequently, assists in improving community emergency response efforts.

The accelerating adoption of electronic health record (EHR) systems will have far-reaching implications for public health research and surveillance, which in turn could lead to changes in public policy, statutes, and regulations. The public health benefits of EHR use can be significant. However, researchers and analysts who rely on EHR data must proceed with caution and understand the potential limitations of EHRs. Because of clinicians' workloads, poor user-interface design, and other factors, EHR data can be erroneous, miscoded, fragmented, and incomplete. In addition, public health findings can be tainted by the problems of selection bias, confounding bias, and measurement bias. These flaws may become all the more troubling and important in an era of electronic "big data," in which a massive amount of information is processed automatically, without human checks. Thus, we conclude the paper by outlining several regulatory and other interventions to address data analysis difficulties that could result in invalid conclusions and unsound public health policies.

Very large biomedical research databases, containing electronic health records (EHR) and genomic data from millions of patients, have been heralded recently for their potential to accelerate scientific discovery and produce dramatic improvements in medical treatments. Research enabled by these databases may also lead to profound changes in law, regulation, social policy, and even litigation strategies. Yet, is “big data” necessarily better data?This paper makes an original contribution to the legal literature by focusing on what can go wrong in the process of biomedical database research and what precautions are necessary to avoid critical mistakes. We address three main reasons for approaching such research with care and being cautious in relying on its outcomes for purposes of public policy or litigation. First, the data contained in biomedical databases is surprisingly likely to be incorrect or incomplete. Second, systematic biases, arising from both the nature of the data and the preconceptions of investigators, are serious threats to the validity of research results, especially in answering causal questions. Third, data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate ostensibly scientific but misleading research findings for the purpose of manipulating public opinion and swaying policymakers.In short, this paper sheds much-needed light on the problems of credulous and uninformed acceptance of research results derived from biomedical databases. An understanding of the pitfalls of big data analysis is of critical importance to anyone who will rely on or dispute its outcomes, including lawyers, policymakers, and the public at large. The Article also recommends technical, methodological, and educational interventions to combat the dangers of database errors and abuses.