Big data in the era of precision medicine: big promise or big liability?

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May 25, 2016 - Keywords: big data • IBM Watson Health • legal issues • liability • medical malpractice. • personalized medicine • precision medicine • predictive ...

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Big data in the era of precision medicine: big promise or big liability? “Big data … holds the potential for exciting advances and liabilities for the practice of precision medicine.” First draft submitted: 2 May 2016; Accepted for publication: 6 May 2016; Published online: 25 May 2016 Keywords:  big data • IBM Watson Health • legal issues • liability • medical malpractice • personalized medicine • precision medicine • predictive analytics

Last fall, Fox Studios made movie history during the prerelease of the film The Revenant, when it collaborated with Lightwave Technologies, Inc., to collect health data on 100 participants watching the movie [1] using a medical-grade biometric wearable. The data collected included repeated measurements of heart rate, skin conductivity to measure the fight or flight stress response and body temperature, all correlated with events in the movie. This novel use of health data is an example of the reach of big data. . Big data, the term coined to describe the massive amounts of data, both structured and unstructured, that is being increasingly used for predictive analytics in numerous areas of business and life, holds the potential for exciting advances and liabilities for the practice of precision medicine. The hallmark of big data are the three Vs: volume – the collection and storage of data from many varied sources; velocity – the data are becoming available at an unprecedented rate; and variety – the data arrive in a variety of structured and unstructured formats. Nowhere is big data more ubiquitous or timely than in precision medicine where such technologies as next-generation sequencing, whole genome sequencing, genome-wide association studies, phenomewide association studies and a variety of new digital health technologies are generating voluminous levels of usable data to improve patient outcomes. Indeed, several high-profile clinical examples, such as the story of Jackie

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Smith’s diagnosis of centronuclear myopathy, a rare neuromuscular condition, have demonstrated the power of collecting, storing and synthesizing large datasets from disparate sources to improve patient outcomes. Jackie Smith, diagnosed with a neuromuscular disease when she was 3 years old, finally received the specific diagnosis last February, 2015, when her DNA was sequenced and compared with thousands of others with similar neuromuscular conditions [2] , a sophisticated example of big data genomics. In addition to its use in precision genomics, big data hold enormous potential for the improvement of patient outcomes via consolidation of data from electronic health records, administrative data claims and data from other sources, such as mobile health apps and wearables. Particularly noteworthy is the use of cognitive computing as exemplified by the partnership established between IBM Watson and Memorial Sloan–Kettering [3] to mine massive amounts of data generated on a large population of cancer patients and importantly, to continually ‘learn’ and add to the information, so as to suggest diagnostic and treatment options. However, while successes in precision medicine big data are mounting, the use of big data raises new regulatory concerns and legal issues. One salient issue revolves around physician liability and medical malpractice. A concept that is critical to liability is the notion of standard of care and its evolving nature. Although used frequently in medical parlance,

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Amalia M Issa Author for correspondence: Personalized Medicine & Targeted Therapeutics, University of the Sciences in Philadelphia, 600 S. 43rd Philadelphia, PA 19104, USA [email protected] usciences.edu

Gary E Marchant Center for Law, Science & Innovation, Sandra Day O’Connor College of Law, Arizona State University, PO Box 877906, Tempe, AZ 85287-7906, USA

Douglas Campos-Outcalt Mercy Care Plan, 4350 E. Cotton Center Blvd., Bldg D, Phoenix, AZ 85040, USA

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Editorial  Issa, Marchant & Campos-Outcalt ‘standard of care’ originated as a legal concept to denote the type of care that a reasonably competent physician practicing in the same field and locality would provide under similar conditions. There is considerable variation in the understanding and the nuances of standard of care in the courts between different jurisdictions; however, in medical malpractice suits, the foundation of standard of care typically hinges on what would a reasonable ­ physician do under the same or similar ­circumstances.

“…regulation and governance for oversight

of precision medicine and big data analytics have emerged as critical factors in the future ­implementation of precision medicine in clinical practice, however existing regulatory systems and laws are struggling to keep up with the rapid tempo of genomic and digital health innovation, adoption and translation into precision ­medicine.



Big data and deep machine learning that is now possible with the use of contextual computing, raises new challenges to the standard of care concept. Contextual computing, of which IBM Watson is perhaps the most famous example, refers to context-aware devices and computers that use natural language processing and machine learning algorithms to provide appropriate insight into vast amounts of data to support decision making. Watson Health is already being utilized at the Memorial Sloan–Kettering Cancer Center to interpret cancer patients’ clinical information and identify evidence-based treatment options. A particularly salient question is if a Watson-like computer using algorithms suggests or recommends a particular treatment modality, to what degree will the treating physician be able to disagree with the computer? How would such a scenario alter the concept of fiduciary duty of care? Would the courts ever accept computer-guided diagnostics and prognostications as standard of care? Another nascent liability concern involves the protection of medical data and privacy in a society that has already seen litigation related to data breaches in both the health industry and other industries such as credit card data. Given that several of the class action lawsuits related to data breaches highlight the issue of questionable data anonymization or encryption, it will be important to develop more effective approaches to data anonymization and protection laws. An emerging issue relates to whether big data utilization for predictive purposes imposes a duty to inform patients of any incidentally identified risks. If so, would there be a duty to intervene in a preventive manner? Will a provider have a duty to update previous advice to patients in light of new information provided by predictive analytics?

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Predictive analytics engendered by big data in precision medicine also leads inevitably to errors or a lack of proper analysis that could result in possible adverse events, ­raising potential malpractice concerns for ­physicians. An as-yet nascent, but plausible new area for liability concerns insurers and the role of liability catastrophe. Liability catastrophes typically happen when adverse events occur on a large scale, such as when taking a drug for a specific indication correlates with one or more serious adverse events in a large number of patients and may be ultimately withdrawn, as happened with rofecoxib, [4] leading to 190 class action suits filed against the manufacturer, Merck. Thus, big data predictive analytics might act as an early warning surveillance system to prevent or minimize such massive adverse events, in the first place. On the other hand, predictive analytics might be used to sound the alarm on the existence of potentially serious massive adverse events thereby providing lawyers with an earlier opportunity to prepare liability catastrophe cases. Although regulation and governance for oversight of precision medicine and big data analytics have emerged as critical factors in the future implementation of precision medicine in clinical practice, the regulatory and legislative paradigms have not kept pace, and existing regulatory systems and laws are struggling to keep up with the rapid tempo of genomic and digital health innovation, adoption and translation into precision medicine. Ultimately, it is important for physicians and other healthcare providers to be aware of big data and how the use of predictive analytics in delivering precision medicine will give rise to potential liability and medical malpractice concerns, and take steps to educate themselves in order to avoid this new type of liability. Increasing calls by consumers and other stakeholders for data sharing [5] , and the use of mobile health apps are also driving the regulatory and legislative landscape, and it may be that a disruptive shift in the culture of biomedicine toward greater data sharing with patients to the degree that the patient views him or herself as the owner of the medical data and the associated decisions, might lead to less liability overall. We urge the development of new regulatory and governance structures in order to adopt and use big data in the practice of precision medicine. Financial & competing interests disclosure This work is supported by grant R01 HG006145 from the National Human Genome Research Institute (NHGRI) of the NIH. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

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Big data in the era of precision medicine 

References 1

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Liberatore S. The Revenant really is terrifying: sensors worn by cinemagoers reveal the ‘emotional rollercoaster’ of DiCaprio’s latest movie. Dailymail.com, 13 January 2016. www.dailymail.co.uk Cortez M. Sifting DNA databases for the right diagnosis. Bloomberg Businessweek (2015). www.bloomberg.com

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Parodi S, Riccardi G, Castagnino N et al. Systems medicine in oncology: signaling network modeling and new-generation decision-support systems. Methods Mol. Biol. 1386, 181–219 (2016).

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Wadman M. Painkiller in the dock. Nature 436, 459 (2005).

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Mandl KD, Kohane IS. Time for a patient-driven health information economy? N. Engl. J. Med. 374, 205–208 (2016).

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