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Electronic Health Records and Research: Privacy Versus Scientific Priorities
... The heterogeneity and networking of big data and global moves towards Bopen data^blur conceptual distinctions such as those between health-related data and non-health-related data, between personal and nonpersonal data, between individual and group-level privacy, and between Bprimary^and Bsecondary^uses of data-distinctions that often form the basis of existing confidentiality (and consent) rules (e.g. Terry 2013;Hoffman 2010Hoffman , 2014Hoffman , 2016Schadt 2012;Frizzo-Barker et al. 2016;Erdmann 2013;Chow-White et al. 2015;Stoeklé et al. 2016;Shoenbill et al. 2014). Furthermore, big data analytics have reached a level of sophistication that makes it impossible to promise perfect anonymity, even if all identifiers are removed from a particular segment of data (e.g. ...
... The heterogeneity and networking of big data and global moves towards Bopen data^blur conceptual distinctions such as those between health-related data and non-health-related data, between personal and nonpersonal data, between individual and group-level privacy, and between Bprimary^and Bsecondary^uses of data-distinctions that often form the basis of existing confidentiality (and consent) rules (e.g. Terry 2013;Hoffman 2010Hoffman , 2014Hoffman , 2016Schadt 2012;Frizzo-Barker et al. 2016;Erdmann 2013;Chow-White et al. 2015;Stoeklé et al. 2016;Shoenbill et al. 2014). Furthermore, big data analytics have reached a level of sophistication that makes it impossible to promise perfect anonymity, even if all identifiers are removed from a particular segment of data (e.g. ...
... The heterogeneity and networking of big data and global moves towards Bopen data^blur conceptual distinctions such as those between health-related data and non-health-related data, between personal and nonpersonal data, between individual and group-level privacy, and between Bprimary^and Bsecondary^uses of data-distinctions that often form the basis of existing confidentiality (and consent) rules (e.g. Terry 2013;Hoffman 2010Hoffman , 2014Hoffman , 2016Schadt 2012;Frizzo-Barker et al. 2016;Erdmann 2013;Chow-White et al. 2015;Stoeklé et al. 2016;Shoenbill et al. 2014). Furthermore, big data analytics have reached a level of sophistication that makes it impossible to promise perfect anonymity, even if all identifiers are removed from a particular segment of data (e.g. ...
Biomedical innovation and translation are increasingly emphasizing research using “big data.” The hope is that big data methods will both speed up research and make its results more applicable to “real-world” patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals.
... With the consolidation of EPR systems in modern healthcare, massive amounts of clinical data and phenotype data are gradually becoming available for researchers [1,2,3,4,5,6]. Alone, or integrated with existing biomedical resources, these EPR systems constitute a rich resource for many types of data driven knowledge discovery as we demonstrate in this paper. ...
... Alone, or integrated with existing biomedical resources, these EPR systems constitute a rich resource for many types of data driven knowledge discovery as we demonstrate in this paper. In the coming years, as these data are also coupled to the expected explosion in personal genomic data, the translational meeting of 'bench and bedside' is expected to push scientific advancements in personalized medicine [4,7,8,9,10]. EPR systems document patient morbidity, treatment and care over time. ...
Author Summary
Text mining and information extraction can be seen as the challenge of converting information hidden in text into manageable data. We have used text mining to automatically extract clinically relevant terms from 5543 psychiatric patient records and map these to disease codes in the International Classification of Disease ontology (ICD10). Mined codes were supplemented by existing coded data. For each patient we constructed a phenotypic profile of associated ICD10 codes. This allowed us to cluster patients together based on the similarity of their profiles. The result is a patient stratification based on more complete profiles than the primary diagnosis, which is typically used. Similarly we investigated comorbidities by looking for pairs of disease codes cooccuring in patients more often than expected. Our high ranking pairs were manually curated by a medical doctor who flagged 93 candidates as interesting. For a number of these we were able to find genes/proteins known to be associated with the diseases using the OMIM database. The disease-associated proteins allowed us to construct protein networks suspected to be involved in each of the phenotypes. Shared proteins between two associated diseases might provide insight to the disease comorbidity.
... Cross-border and interoperable electronic health-record systems make confidential data more easily and more rapidly accessible to a wider audience. However, by enabling greater access to a compilation of personal data concerning one's health and genetic information from different sources, and spanning a lifetime, they increase the risk that personal health data could accidentally be disclosed or distributed to unauthorised parties (Hoffman 2010). There is broad agreement that it is individuals who should not only control their own data but also have the right to make decisions about access to their data, and be informed about how they will be used (Kaye et al. 2011;Brent D Mittelstadt et al. 2012;Solove 2013;Sterckx et al. 2015). ...
Intensified and extensive data production and data storage are characteristics of contemporary western societies. Health data sharing is increasing with the growth of Information and Communication Technology (ICT) platforms devoted to the collection of personal health and genomic data. However, the sensitive and personal nature of health data poses ethical challenges when data is disclosed and shared even if for scientific research purposes.
With this in mind, the Science and Values Working Group of the COST Action CHIP ME ‘Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives’ (IS 1303) identified six core values they considered to be essential for the ethical sharing of health data using ICT platforms. We believe that using this ethical framework will promote respectful scientific practices in order to maintain individuals’ trust in research.
We use these values to analyse five ICT platforms and explore how emerging data sharing platforms are reconfiguring the data sharing experience from a range of perspectives. We discuss which types of values, rights and responsibilities they entail and enshrine within their philosophy or outlook on what it means to share personal health information. Through this discussion we address issues of the design and the development process of personal health data and patient-oriented infrastructures, as well as new forms of technologically-mediated empowerment.
... The exponential growth, in recent times, of the amount of biomedical information that is stored on purely electronic supports-Electronic Health Records, or EHR, spring promptly to our mind-has turned them into an element of undeniable relevance to the most diverse fields of scientific research ( Hoffman, 2010;Prokosch, & Ganslandt, 2009). One of these fields is that of Information Retrieval, and its traditional challenge of identifying those records which most efficiently answer a user's immediate needs for information; for this task to be accomplished, it is critical to first establish a recognition of patterns in medical histories which would permit, ultimately, the early detection of epidemic outbreaks, the prevention of disease, or the identification of cohort groups ( Roque, et al., 2011). ...
... This immediately undermines the privacy of the information, raising important confidentiality concerns for database managers and users. Because such annotation and cross-linking is crucial to the epidemiological and diagnostic utility of clinical data sets, an internal tension is created between enabling data mining to promote science or discouraging it in order to protect privacy [19]. ...
The advent and expansion of electronic medical record systems and open-access databases are creating a "data tsunami." As this wave descends, we must anticipate and address several ethical and social risks: threats to patient privacy, threats to the reputations of various social groups, and threats to public trust in biomedical research.
... The danger is that large segments of the population or subgroups of individuals . . . will decide to opt out of inclusion in databases " (Hoffman 2010 ). Prudent consent documents will avoid language that might unnecessarily encourage widespread opposition to participation. ...
... He suggests several ways of increasing individual control, including measures to reallocate a portion of revenue flowing from the research. Misha Angrist (2010) and Sharona Hoffman (2010) both give voice to an ambivalence I suspect is widespread among experts in research ethics who have considered this issue. For Angrist, " deidentification is already an inadequate compromise designed to retain a modicum of respect for subject privacy while relieving investigators of the ajob W1 ...
My recent American Journal of Bioethics target article, “Is Deidentification Sufficient to Protect Health Privacy in Research?” (Rothstein 2010), generated 13 open peer commentaries (OPCs). Twelve generally agree with my basic premise that it is ethically problematic for researchers to deidentify and use health information and biological specimens without the knowledge, consent, or authorization of the individual source of the information or sample. These 12 OPCs provide valuable insights, critiques, and suggestions about issues in the target article. I want to begin this response, however, by considering the one OPC highly critical of my assertion of the need to alter the status quo.
Traditional forms of surveillance addressed detecting outbreaks of dangerous contagious disease, case finding and contact tracing, and identifying environmental hazards. But public health today goes far beyond interpersonal contagion and environmental dangers to address patterns of ill health among the population. Increased rates of conditions such as diabetes or substance use disorders are called “epidemics” to be addressed by public health interventions including education, environmental design, economic incentives, regulation, and even prohibition. This chapter explores the ethics of this so-called “new” public health, arguing that paternalistic interference with behavior is justified only to prevent serious harm. Much information collection, however, aims not to protect individuals but to understand health or health disparities at the population level and thus is not properly regarded as paternalistic. The chapter also argues that interferences such as the requirement to wear masks in a pandemic are mistakenly perceived as protecting individuals against their own choices and are better viewed as protecting others from the possibility of harm.
Psychosocial and behavioral approaches to cancer treatment have been demonstrated to improve clinical outcomes and are recommended by the Institute of Medicine for improving the quality of cancer treatment across the continuum of care. Information ecosystems for cancer patients and survivors are complex, and this chapter will highlight emerging consumer-facing best practices in the field of cancer informatics. After providing an overview of the current state of cancer care in the USA, we will describe the critical role that informatics plays in the delivery of cancer care. Discussion will cover the role of informatics in screening for emotional distress; in care coordination; and in survivorship care planning. We will also address challenges associated with interoperability and end with a consumer-driven view of the future in which informatics is used to an even greater effect in the service of supporting people affected by cancer.
In the foreseeable future, electronic health record (EHR) systems are likely to become a fixture in medical settings. The potential benefits of computerization could be substantial, but EHR systems also give rise to new liability risks for health care providers that have received little attention in the legal literature. This Article features a first of its kind, comprehensive analysis of the liability risks associated with use of this complex and important technology. In addition, it develops recommendations to address these liability concerns. Appropriate measures include federal regulations designed to ensure the quality and safety of EHR systems along with agency guidance and well crafted clinical practice guidelines for EHR system users. In formulating its recommendations, the Article proposes a novel, uniform process for developing authoritative clinical practice guidelines and explores how EHR technology itself can enable experts to gather evidence of best practices. The authors argue that without thoughtful interventions and sound guidance from government and medical organizations, this promising technology may encumber rather than support clinicians and may hinder rather than promote health outcome improvements.
The revolution in health information technology has enabled the compilation and use of large data sets of health records for genomic and other research. Extensive collections of health records, especially those linked with biological specimens, are also extremely valuable for outcomes research, quality assurance, public health surveillance, and other beneficial purposes. The manipulation of large quantities of health information, however, creates substantial challenges for protecting the privacy of patients and research subjects. The strategy of choice for many health care providers and research institutions in dealing with this challenge has been to de-identify individual health information.
The Food and Drug Administration Amendments Act of 2007 (FDAAA) authorized the U.S. Food and Drug Administration (FDA) to develop a 100-million-person health data network known as the Sentinel system. When fully operational, the Sentinel network will offer a very rich, very large health data resource that has the potential to become one of history's most powerful engines of biomedical innovation and clinical translation of discoveries. Who controls this asset will be a matter of great scientific and commercial importance. This article explores two key questions--data access and use rights--that are under debate as various parties jostle for control of the network: First, does FDA have legal authority to require private healthcare data environments--such as insurers, healthcare providers, pharmacists and other entities that hold data in administrative and clinical databases--to make data available for inclusion in the network? Second, who will decide how the network is used, once it is built? The article explains why a neutral analysis of these questions is essential as FDA designs the governance framework for protecting the diverse stakeholders who will be touched by the Sentinel network. The conclusion describes threats to network operations, including federal and state constitutional claims and state legislative interventions, which could arise if FDA fails to devote timely attention to these issues.
Who owns a patient's medical information? The patient, the provider, or the insurer? All of the above? None of the above? In the emerging era of electronic medical records, no other legal question is more critical, more contested, or more poorly understood. Ownership was never much in doubt in an age of paper-based records, but now that information content can be easily digitized and freed from any particular storage medium, confusion reigns. How this issue is resolved can have huge impacts on how or whether massive anticipated developments in electronic and personal health records will take shape. The respective property rights of patients, providers and insurers will strongly influence, if not determine, what form of electronic health informatics ends up predominating. And, whether rights to access and use medical information can be commercialized may determine whether effective, comprehensive medical information networks can emerge at all, absent overt government mandate. This paper analyzes optimal property rights in medical information from the perspective of network economics. It proposes that patients be allowed to monetize their access and control rights by assigning them to a trusted intermediary who may then place these rights in a stream of commerce that determines their value and best use. The funds generated can then be distributed both to patients and providers to encourage their adoption and use of interconnected electronic records.
The widespread use of electronic health records (EHRs) in the United States is inevitable. EHRs will improve caregivers' decisions and patients' outcomes. Once patients experience the benefits of this technology, they will demand nothing less from their providers. Hundreds of thousands of physicians have already seen these benefits in their clinical practice. But inevitability does not mean easy transition. We have years of professional agreement and bipartisan consensus regarding the potential value of EHRs. Yet we have not moved significantly to extend the availability of EHRs from a few large institutions to the smaller clinics and practices where most Americans . . .
In June 2005, the FDA approved BiDil, a heart failure medication that is labeled for use only by African-Americans and thus is the first treatment of its kind. The drug likely portends a future of growing interest in "race-based" medicine. This phenomenon is emerging at the same time that scientists, in light of the Human Genome Project, are reaching an understanding that "race" has no biological meaning, and consequently, "racially-tailored" medicine is both puzzling and troubling. This Article explores the reasons for the new focus on "racial-profiling" in medicine. It analyzes the risks and dangers of this approach, including medical mistakes, stigmatizations, discrimination, exacerbation of health disparities, and violation of anti-discrimination mandates. The author does not argue against the pursuit of attribute-based therapies, but cautions that the attribute or attributes at issue must be carefully determined and will not be equivalent to what is conventionally thought of as "race." The article develops recommendations for safeguards that should be implemented by scientific review boards, IRBs, researchers, health care providers, and journalists involved with attribute-based research and therapeutic practices to ensure that this new approach promotes rather than diminishes public health and welfare.
Most clinical decisions involve bridging the inferential gap: Clinicians are required to "fill in" where they lack knowledge or where no knowledge yet exists. In this context we consider how the inferential gap is a product, in part, of how knowledge is created, the limits to gaining access to such knowledge, and the variable ways in which knowledge is translated into decisions. We consider how electronic health records (EHRs) will help narrow this gap by accelerating the creation of evidence relevant to everyday practice needs and facilitating real-time use of knowledge in practice.