Tag: Big Data

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Data-driven Medicine Needs a New Profession: Health Information Counseling

The Petrie-Flom Center Staff 2 Comments

By Barbara Prainsack, Alena Buyx, and Amelia Fiske

Have you ever clicked ‘I agree’ to share information about yourself on a health app on your smartphone? Wondered if the results of new therapy reported on a patient community website were accurate? Considered altering a medical device to better meet your own needs, but had doubts about how the changes might affect its function?

While these kinds of decisions are increasingly routine, there is no clear path for getting information on health-related devices, advice on what data to collect, how to evaluate medical information found online, or concerns one might have around data sharing on patient platforms.

It’s not only patients who are facing these questions in the age of big data in medicine. Clinicians are also increasingly confronted with diverse forms of molecular, genetic, lifestyle, and digital data, and often the quality, meaning, and actionability of this data is unclear.

The difficulties of interpreting unstructured data, such as symptom logs recorded on personal devices, add another layer of complexity for clinicians trying to decide which course of action would best meet their duty of beneficence and enable the best possible care for patients.

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‘Big Data, Health Law, and Bioethics’ Examines the Intersection of Major Issues in Health Care

The Petrie-Flom Center Staff Leave a comment

When data from all aspects of our lives can be relevant to our health – from our habits at the grocery store and our Google searches to our FitBit data and our medical records – can we really differentiate between big data and health big data? Will health big data be used for good, such as to improve drug safety, or ill, as in insurance discrimination? Will it disrupt health care (and the health care system) as we know it? Will it be possible to protect our health privacy? What barriers will there be to collecting and utilizing health big data? What role should law play, and what ethical concerns may arise? A new timely, groundbreaking volume explores these questions and more from a variety of perspectives, examining how law promotes or discourages the use of big data in the health care sphere, and also what we can learn from other sectors.

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Sharing Data for 21st Century Cures – Two Steps Forward…

Genetics in Medicine Leave a comment

By Mary A. Majumder, Christi J. Guerrini, Juli M. Bollinger, Robert Cook-Deegan, and Amy L. McGuire

The 21st Century Cures Act was passed with support from both sides of the aisle (imagine that!) and signed into law by then-President Obama late last year. This ambitious legislation drives action in areas as diverse as drug and device regulation and response to the opioid epidemic. It also tackles the issue of how to make data more broadly available for research use and clinical purposes. In our recently published GIM article, “Sharing data under the 21st Century Cures Act,” we examine the Act’s potential to facilitate data-sharing, in line with a recent position statement of the American College of Medical Genetics and Genomics. We highlight a number of provisions of the Act that either explicitly advance data-sharing or promote policy developments that have the potential to advance it. For example, Section 2014 of the Act authorizes the Director of National Institutes of Health to require award recipients to share data, and Section 4006 requires the Secretary of Health and Human Services to promote policies ensuring that patients have access to their electronic health information and are supported in sharing this information with others.

Just as relevant, the Act takes steps to reduce some major barriers to data sharing. An important feature of the Act, which has not been extensively publicized, is its incorporation of provisions from legislation originally proposed by Senators Elizabeth Warren and Mike Enzi to protect the identifiable, sensitive information of research subjects. Senator Warren, in particular, has been a vocal advocate of data sharing. Arguably, one of the biggest barriers to sharing is public concern about privacy. The relevant provisions address this concern chiefly via Certificates of Confidentiality. Among other things, the Act makes issuance of Certificates automatic for federally-funded research in which identifiable, sensitive information is collected and prohibits disclosure of identifiable, sensitive information by covered researchers, with only a few exceptions such as disclosure for purposes of other research. These protections became effective June 11, 2017. While NIH has signaled its awareness of the Act, it has not yet updated its Certificates of Confidentiality webpage. Read More

Standards, Data Exchange and Intellectual Property Rights in Systems Biology

Timo Minssen 1 Comment

By Timo Minssen

I am happy to announce that our recent paper on “Standards, Data Exchange and Intellectual Property Rights in Systems Biology” has been published in the Biotechnology Journal Vol 11, Issue 12, pp. 1477-1480.  The paper was co-authored by Esther Van Zimmeren from the University of Antwerp, Berthold Rutz from the European Patent Office and me. Please find a summary below:

Intellectual property rights (IPRs) represent a key concern for researchers and industry in basically all high-tech sectors. IPRs regularly figure prominently in scientific journals and at scientific conferences and lead to dedicated workshops to increase the awareness and “IPR savviness” of scientists. In 2015, Biotechnology Journal published a report from an expert meeting on “Synthetic Biology & Intellectual Property Rights” organized by the Danish Agency for Science, Technology and Innovation sponsored by the European Research Area Network (ERA-Net) in Synthetic Biology (ERASynBio), in which we provided a number of recommendations for a variety of stakeholders. The current article offers some deeper reflections about the interface between IPRs, standards and data exchange in Systems Biology resulting from an Expert Meeting funded by another ERA-Net, ERASysAPP. The meeting brought together experts and stakeholders (e.g. scientists, company representatives, officials from public funding organizations) in systems biology (SysBio) from different countries.  Despite the different profiles of the stakeholders at the meeting and the variety of interests, many concerns and opinions were shared. In case particular views were expressed by a specific type of stakeholder, this will be explicitly mentioned in the text. This article reflects on a number of particularly relevant issues that were discussed at the meeting and offers some recommendations. Read More

Big Data, Genetics, and Re-Identification

zshapiro1 2 Comments

by Zachary Shapiro

While all scientific research produces data, genomic analysis is somewhat unique in that it inherently produces vast quantities of data. Every human genome contains roughly 20,000-25,000 genes, so that even the most routine genomic sequencing or mapping will generate enormous amounts of data. Furthermore, next-generation sequencing techniques are being pioneered to allow researchers to quickly sequence genomes. These advances have resulted in both a dramatic reduction in the time needed to sequence a given genome, while also triggering a substantial reduction in cost. Along with novel methods of sequencing genomes, there have been improvements in storing and sharing genomic data, particularly using computer and internet based databases, giving rise to Big Data in the field of genetics.

While big data has proven useful for genomic research, there is a possibility that the aggregation of so much data could give rise to new ethical concerns. One concern is that promises of privacy made to individual participants might be undermined, if there exists a possibility of subject re-identification.

Re-identification of individual participants, from de-identified data contained in genetic databases, can occur when researchers apply unique algorithms that are able to cross-reference numerous data sets with the available genetic information. This can enable diligent researchers to re-identify specific individuals, even from data sets that are thought to be anonymized. Such re-identification represents a genuine threat to the privacy of the individual, as a researcher could learn about genetic risk factors for diseases, or other sensitive health and personal information, from combing through an individual’s genetic information.

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Affective Forecasting and Genetics

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by Zachary Shapiro

Psychological research on “affective forecasting,” studying individuals’ ability to predict their future emotional states, consistently shows that people are terrible at predicting their ability to adapt to future adversity. This finding has particular significance for medical decision-making, as so many serious health decisions hinge on quality-of-life judgments, generally made by an individual balancing risks and benefits they perceive of a future state that is likely to result from a given therapeutic regime.

Much of the research on affective forecasting has focused on high-stakes events, restricting study participation to those likely to find the study event particularly significant, such as tenure-track faculty, registered voters, or sports enthusiasts. Despite a growing body of research on forecasting biases in the medical domain, little work has previously systematically considered such biases in clinical genetics. However, as the prevalence of genetic testing has increased, scholars have noticed forecasting deficiencies with increasing regularity.[1]

While evidence suggests that those who receive genetic testing, whether they are non-carriers or carriers of specific genes, differ in terms of short-term general psychological distress, their long-term distress levels do not differ significantly. Results of research into the affective reactions of patients undergoing predictive genetic testing suggest that, in general, psychological outcomes are not as negative as one may expect.

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