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Data-intensive resourcing in healthcare

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Big Data promises to heal many of the complex problems in healthcare. Departing from conventional ways of thinking about what constitutes relevant health data and how to analyze it, data-intensive resourcing entails different practices of collecting, sorting, circulating, and interpreting data, while making it available to multiple users. In the process, big data and the infrastructures instilled to support it are creating new forms of value and reordering relationships as distinctions between medical and nonmedical data and research and care are blurred. This paper situates big data in healthcare within social-political and economic conditions in the U.S., and charts emerging assemblages in order to make visible the cultural work that big data does in healthcare.

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  1. I interviewed scientists in the Health e-Heart project in May 2013. The web site can be found at The second case was presented by a data scientist at an April 2016 medical informatics workshop I attended. He  works for LexisNexis, a large data analytics company that has recently added a healthcare division, and claims to have 45 billion records from more than 10,000 different sources.

  2. 1 petabyte = 1015 or one quadrillion bytes; 1 zetabyte = 1021 bytes. One conference speaker said that if every cancer patient in the U.S. had their genes sequenced every 2 weeks (a possibility given the recently launched cancer ‘moonshot’ project), it would be about 493 exabytes (10 bytes)18 (Big Data in Biomedicine conference, May 20, 2015). Note that most of these estimates come from private consulting companies that do not share how the numbers were derived.

  3. France Cordova presentation at the Big Data in Biomedicine conference, Stanford University, May 2015.

  4. See also volume 8 of BioSocieties (2013) on ‘big science.’

  5. One report estimated the growth of data mining of hospital records at 40 % per year (Frost & Sullivan, 2012). Mining has been used to analyze everything from genome-wide associations to hospital admissions to clinician performance evaluations (Bates et al, 2014; Denny et al, 2011; Halamka, 2014; Kohane, 2011; Raghupathi and Raghupathi, 2013; Xu et al, 2011). Mining of eMR is also commonly used to identify subjects for trials (Elkhenini et al, 2015), although consumer databases are now also used (Walker, 2013).

  6. The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 PL42.USC 201, enacted as a part of the American Recovery and Reinvestment Act, mandates adoption of electronic health records (eHR) with “meaningful use” of the data. This dramatically increased the number of hospitals with eHR. The Medicare Access and CHIP Reinvestment Act further facilitates sharing of eHR (see note 13).

  7. Kitchin (2014) and Leonelli (2014) suggest that in practice, data-driven science is less inductive than hybrid. Kitchin argues that blending theoretically informed choices about how best to design a query while leaving insights and conclusions to emerge from the data constitutes abductive reasoning; it is neither a return to empiricism or purely inductive, nor is it an entirely new epistemology of science.

  8. Critics counter this controversial statement by arguing that insights and hypotheses are generated from the data, rather than from theory, but are guided by both theoretical and practical knowledge, not simply left to ‘speak for themselves’ (Kitchin, 2014). Stevens points out that theories are built into the hardware, software, database infrastructures, and front-end platforms themselves (2013).

  9. Interview with Etheredge, found at:

  10. The emphasis was on adaptable, systems with flexibility toward ethical oversight and product regulation (cf Hogle, 2017).

  11. New rules for informed consent and research have been proposed with provisions that further blur what counts as research (Hogle, 2016; Hudson and Collins, 2015; see also new policy regarding human subjects from the Department of Health and Human Services, 2015, available at:

  12. Note that these figures come from a consulting firm and cannot be confirmed because the source and manner of analysis is not provided. Yet, such numbers are repeatedly circulated in big data publications and conferences. There is a certain irony here about claiming cost savings, when in fact new opportunities will be created for building revenues for private participants, potentially diverting some funds.

  13. Public Law No: 114-10. Recipients must be “affiliated covered entities” (ACE) under the Health Insurance Portability and Accountability Act (Pub.L. 104–191), enacted to protect patient privacy. ACE are allowed to have direct access to patient information as organizations doing business with healthcare organizations (e.g., billing companies), with no informed consent or notification required.

  14. For example, ‘person-generated’ data such as the tweet ‘I feel bad about…’ might be interpreted by natural language processing as a social gesture or a health indicator.

  15. While eMR usage has increased dramatically in the U.S., certain groups and transactions will not appear. For example, homeless people, migrant workers, residents of many long-term care facilities, those giving birth at home, or those without insurance or an assigned caregiver are unlikely to have a complete record.

  16. Information voluntarily uploaded into such sites is not covered by the Health Insurance Portability and Accountability Act (HIPAA) protections for health privacy.

  17. Acxiom has an estimated 1600 pieces of data on 98 % of individuals in the U.S. (Solove, 2012; Singer, 2014). Examples of loyalty programs include Walgreen’s drug stores, which offers rewards for customers to use wellness trackers. Output can be automatically linked to purchases (drugs and other). More recently, customers receive discounts in exchange for waiving HIPAA protections and allowing direct access to medical records.

  18. The Family Educational Rights and Protection Act (FIRPA) (20 U.S.C. § 1232 g; 34 CFR 99) has no provisions for protecting personal information in education records from third-party health researchers, and in fact, school and college records are often mined for population health purposes.

  19. While it is beyond the scope of this paper, encryption and other data security measures can change the content and nature of the data in significant ways. For example, to prevent an incursion, data may be partially obscured or modified.

  20. While data on individuals is the target, ‘precision prevention’ programs aim to extrapolate tracked data—especially from quantified-self data—to the population level (Barrett et al, 2013).

  21. See for example the National Council on Patient Information report, found at

  22. ACA and HITECH require metric reporting.

  23. This score was devised in a collaboration between Pfizer pharmaceuticals and Medco (now ExpressScripts), a pharmacy benefits management company. Drug companies are interested in compliance to encourage ongoing use of their products.

  24. Micropricing is based on geographic locale, day-to-day use and other information from big data analytics, rather than fixed pricing.

  25. A pilot program with clients (employers) will allow patients to get their own genetic information. See (accessed Aug 28, 2013). Direct information about one’s genetic information has proven to be highly controversial, and would be particularly problematic when provided by the payer contracted by one’s employer (Miller, 2014).

  26. See note 13 regarding access to personal health information.

  27. See also their white paper prepared for pharmaceutical companies at: Accretive Health was sued in 2011 by Minnesota for misusing patient records and aggressive collection tactics. ( Information about FICO health scores can be found at:

  28. About $4.5 billion in new U.S. venture capital was invested in digital health in 2015, up from $1.2 billion in 2010, (Wang et al, 2015). Of this, 23 % was in personal health and tracking devices (Graham, 2014; Sullivan, 2014).

  29. Wildly optimistic market estimates range from 100 million wearable sensing devices in 2014 (global) to 485 million by 2018 million (cf Business Intelligence web site at

  30. (accessed 24 April 2015). See also reports from management consulting firm Deloitte (Eggers et al2013) and Frost & Sullivan (2012).

  31. While data from eMR are generally stripped of identifiers (‘de-identified’), it is relatively easy to re-identify specimens and data files using publicly available information (Gymek, 2013; Gutmann and Wagner, 2013). Also, data from devices (especially consumer devices such as smart phones) exist in an ambiguous space between data security agreements and data use agreements (which may not be required to notify users that their personal data are being shared) and privacy regulations for clinical settings through HIPAA (Pasquale and Ragone, 2014; Terry, 2012; Zang et al, 2015). Volunteered information and personal medical information uploaded to online or clinical services may be protected by data security rules but not HIPAA. Furthermore, de-identifying patients for privacy purposes decreases the power and hence value of analytics, particularly for predictive purposes.


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I gratefully acknowledge helpful comments from anonymous reviewers. Discussions with Sabina Leonelli, Klaus Hoeyer, and Rayna Rapp were also extremely valuable. Parts of this study were funded by a grant from the University of Wisconsin Graduate School.

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Correspondence to Linda F. Hogle.

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Hogle, L.F. Data-intensive resourcing in healthcare. BioSocieties 11, 372–393 (2016).

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