Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

Summary

Objectives: As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges.

Methods: A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale.

Results: Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways.

Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful.

Keywords

Science - healthcare - higher education - big data - analytics - quantified self Introduction - connectivism

 

• References

• 1 Rasid Z. Big data infographic: What is big data?. [Internet] 2013. [Cited March 17, 2014]. Available from: http://www.asigra.com/blog/big-data-info-graphic-what-big-data.
• 2 Dugas AF, Hsieh YH, Levin SR, Pines JM, Mareiniss DP. Mohareb et al. Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clin Infect Dis 2012; 54 (Suppl. 04) 463-9.
• 3 Smith C. How many people use the top social media?. [Internet] 2011. [Cited March 17, 2014]. Available from: http://expandedramblings.com/index.php/resource-how-many-people-use-thetop-social-media/#.UxvcnuddWL/index.php/resource-how-many-people-use-thetop-social-media/ #.UxvcnuddWL.
• 4 Gkoulalis-Divanis A, Loukides G. Privacy Challenges and Solutions for Medical Data Sharing. Zurich. Medical privacy tutorial. [Internet] 2011. [Cited March 17, 2014]. Available from: www.zurich.ibm.com/medical-privacy-tutorial
• 5 Butte A. Wearable devices getting more niche. [Internet] 2014. http://blogs.wsj.comdigits/2014/03/04/wearable-devices-getting-more-niche/?utm_content=buffer7044b&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer&mg=blogs-wsj&url=http%253A%252F%252Fblogs.wsj.com%252Fdigits%252F2014%252F03%252F04%252Fwe-arable-devices-getting-more-niche%253Futm_content%253Dbuffer7044b%2526utm_medium%253Dsocial%2526utm_source%253Dtwitter. com%2526utm_campaign%253Dbuffer
• 6 Paton C. Will Google Glass become standard eyeware for Doctors?. [Internet] 2014. [Cited March 17, 2014]. Available from: http://www.healthinformaticsforum.com/forum/topic/show?id=2068976%3ATopic%3A149324&xgs=1&xg_source=msg_share_topic
• 7 O’Connor F. Health-IT early adopters well poised for big-data advances in clinical medicine. [Internet] 2013. [Cited March 17, 2014]. Available from: http://www.computerworld.com/s/article/9238063/Health_IT_early_adopters_well_poised_for_big_data_advances_in_clinical_medicine.
• 8 Mayer-Schönberger V, Cukie K. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt 2013 Chapter 2.
• 9 Barrett MA, Humblet O, Hiatt RA, Adler NE. Big data and disease prevention: From quantified self to quantified communities. Big Data 2013; 1 (Suppl. 03) 168-75.
• 10 Coakley MF, Leerkes MR, Barnett J, Gabrielian AE, Noble K, Weber MN. et al. Unlocking the power of big data at the national institute of health. Big Data 2013 1. 03
• 11 NIH Data Sharing Repositories [Internet] 2014.. [Cited March 5, 2014]. Available from: www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html.
• 12 Kirkpatrick R. Big data for development. Big Data 2013; 1 (Suppl. 01) 3-4.
• 13 Swan M. The quantified self: Fundamental disruption in big data science and biological discovery.“. Big Data 2013; Jun 1 (Suppl. 02) 85-99.
• 14 Whittemore A. Improving Health Systems with Big Data. Big Data in Biomedicine conference. Stanford School of Medicine. [Internet] 2013. [Cited March 18, 2014]. Available from: https://mediaspace.stanford.edu/media/Improving+Health+Systems+with+Big+Data/0_vyo4glde
• 15 Bourne PE. What Big Data means to me. J Am Med Inform Assoc 2014; 21 (Suppl. 02) 194.
• 16 Kum H-C, Krishnamurthy A, Machanavajjhala A, Reiter MK, Ahalt S. Privacy preserving interactive record linkage (PPIRL). J Am Med Inform Assoc 2014; 21 (Suppl. 02) 212-20.
• 17 Shivade C, Raghavan P, Fosler-Lussier E, Embi PJ, Elhadad N, Johnson SB. et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc 2014; Mar-Apr 21 (Suppl. 02) 221-30.
• 18 Shoenbill K, Fost N, Tachinardi U, Mendonca EA. Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations. J Am Med Inform Assoc 2014; 21 (Suppl. 01) 171-80.
• 19 Tenenbaum JD, Sansone S-A, Haendel M. A sea of standards for omics data: sink or swim?. Journal of the American Medical Informatics Association. 2014; 21 (Suppl. 02) 200-3.
• 20 Yau N. A year of food consumption visualized. FlowingData [Internet] 2011 [Cited March 17, 2014]. Available from: http://flowingdata. com/2011/06/29/a-year-of-food-consumption-visualized/
• 21 McCormick T. Video of my healthier information talk. [Internet] 2012. [Cited November 29, 2013]. Available from: http://tjm.org/2012/04/17/videoof-my-healthier-information-talk/
• 22 Tasse D. Quantified self 2012: Some cool things. Tales ‘n’ ideas. [Internet] 2012. [Cited November 29, 2013]. Available from: http://talesnideas.blogspot.com/2012_09_01_archive.html
• 23 Wolf G. The data-driven life. The New York Times. [Internet] 2012. [Cited November 29, 2013]. Available from: www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html
• 24 Chua S. Notes from the quantified self 2012 conference. [Internet] 2012. [Cited November 29, 2013]. Available from: http://sachachua.com/blog/p/23723/
• 25 Asthmapolis.. [Internet] 2013. [Cited November 29, 2013]. Available from: http://propellerhealth.com/
• 26 Glooko.. [Internet] 2013. [Cited November 29, 2013]. Available from: http://www.glooko.com
• 27 Harrison V, Proudfoot J, Wee PP, Parker G, Pavlovic DH, Manicavasagar V. Mobile mental health: review of the emerging field and proof of concept study. J Ment Healt 2011; 20 (Suppl. 06) 509-24.
• 28 FitBit.. [Internet] 2013. [Cited November 29, 2013]. Available from: http://www.fitbit.com
• 29 JawboneUp.. [Internet] 2013. [Cited November 29, 2013]. Available from: https://jawbone.com/up
• 30 RunKeeper.. [Internet] 2013. [Cited November 29, 2013]. Available from: http://runkeeper.com
• 31 Carter MC, Burley VJ, Nykjaer C, Cade JE. Adherence to a smartphone application for weight loss compared to website and paper diary: Pilot randomized controlled trial. J Med Internet Res 2013; 15 (Suppl. 04) e32.
• 32 Lark.. [Internet] 2013. [Cited November 29, 2013]. Available from: http://lark.com
• 33 Dayer L, Heldenbrand S, Anderson P, Gubbins PO, Martin BC. Smartphone medication adherence apps: Potential benefits to patients and providers. J Am Pharm Assoc 2003; 53 (Suppl. 02) 172-81.
• 34 de Nazelle A, Seto E, Donaire-Gonzalez D, Mendez M, Matamala J, Nieuwenhuijsen MJ. et al. Improving estimates of air pollution exposure through ubiquitous sensing technologies. [Internet] 2013. [Cited November 30, 2013]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23416743
• 35 Cerin E, Lee KY, Barnett A, Sit CH, Cheung MC, Chan WM. Objectively-measured neighborhood environments and leisure-time physical activity in Chinese urban elders. Prev Med 2013; 56 (Suppl. 01) 86-9.
• 36 Robinson PL, Dominguez F, Teklehaimanot S, Lee M, Brown A, Goodchild M. Does distance decay modeling of supermarket accessibility predict fruit and vegetable intake by individuals in a large metropolitan area?. J Healthcare Poor Underserved 2013; 24 (Suppl. 01) 172-85.
• 37 HealthMap.. [Internet] 2013 [Cited March 18, 2014]. Available from: http://healthmap.org.
• 38 Sullivan SJ, Schneiders AG, Cheang CW, Kitto E, Lee H, Redhead J. et al. What‘s happening?. A content analysis of concussion-related traffic on twitter. Br J Sports Med [Internet] 2011. [Cited November 30, 2013]. Available from: www.academia.edu/1037228/Whats_happen-ing_A_content_analysis_of_concussion-related_traffic_on_Twitter
• 39 Scanfeld D, Scanfeld V, Larson EL. Dissemination of health information through social networks: twitter and antibiotics. Am J Infect Control 2010; 38 (Suppl. 03) 182-8.
• 40 Bosley JC, Zhao NW, Hill S, Shofer FS, Asch DA, Becker LB. et al. Decoding twitter: Surveillance and trends for cardiac arrest and resuscitation communication. Resuscitation. 2013; 84 (Suppl. 02) 206-12.
• 41 Lyles CR, Lopez A, Pasick R, Sarkar U. 5 mins of uncomfyness is better than dealing with cancer 4 a lifetime: an exploratory qualitative analysis of cervical and breast cancer screening dialogue on twitter.“. J Cancer Educ 2013; 28 (Suppl. 01) 127-33.
• 42 De Choudhury M, Counts S, Horvitz E. Predicting postpartum changes in emotion and behavior via social media. 2013 ACM Annual Conference on Human Factors in Computing Systems. 2013; 3267-76.
• 43 Chew C, Eysenbach G. Pandemics in the age of twitter: Content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One 2010; 5 (Suppl. 11) e14118.
• 44 Chunara R, Andrews JR, Brownstein JS. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am J Trop Med Hyg 2012; 86 (Suppl. 01) 39-45.
• 45 Cassa CA, Chunara R, Mandl K, Brownstein JS. Twitter as a sentinel in emergency situations: lessons from the Boston marathon explosions. PLoS Curr 2013 5
• 46 Paul MJ, Dredze M. A Model for Mining Public Health Topics from Twitter. Baltimore: Technical report. Johns Hopkins University; 2011
• 47 Patients Like Me.. [Internet] 2013. [Cited November 30, 2013]. Available from: http://www.patients-likeme.com
• 48 FluNearYou.. [Internet] 2013. [Cited November 30, 2013]. Available from: https://flunearyou.org
• 49 GermTracker.. [Internet] 2013. [Cited November 30, 2013]. Available from: http://germtracker.org
• 50 Sickweather.. [Internet] 2013. [Cited November 30, 2013]. Available from: http://www.sickweather.com
• 51 Mao JJ, Chung A, Benton A, Hill S, Ungar L, Leonard CE. et al. Online discussion of drug side effects and discontinuation among breast cancer survivors. Pharmacoepidemiol Drug Saf 2013; 22 (Suppl. 03) 256-62.
• 52 Chan EH, Sahai V, Conrad C, Brownstein JS. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis 2011; 5 (Suppl. 05) e1206.
• 53 Ayers JW, Althouse BM, Allem JP, Rosenquist JN, Ford DE. Seasonality in seeking mental health information on Google. Am J Prev Med 2013; 44 (Suppl. 05) 520-5.
• 54 Seifter A, Schwarzwalder A, Geis K, Aucott J. The utility of „Google Trends“ for epidemiological research: Lyme disease as an example. Geospat Health 2010; 4 (Suppl. 02) 135-7.
• 55 Ayers JW, Ribisl KM, Brownstein JS. Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance. Am J Prev Med 2011; 40 (Suppl. 04) 448-53.
• 56 Hill S, Merchant R, Ungar L. Lessons Learned About Public Health from Online Crowd Surveil-lance. Big Data September 2013; 1 (Suppl. 03) 160-7.
• 57 West R, White RW, Horvitz E. From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. 22nd International Conference on World Wide Web, International World Wide Web Conferences Steering Committee 2013 p. 1399-410.
• 58 Neff G. Why BD Won‘t Cure Us. Big Data September 2013; 1 (Suppl. 03) 117-23.
• 59 Luciano SJ, Cumming GP, Wilkinson MD, Kahana E. The emergent discipline of health web science. J Med Internet Res 2013; August 15 (Suppl. 08) e166.
• 60 Swan M. Crowdsourced health research studies: An important emerging complement to clinical trials in the public health research ecosystem. J Med Internet Res 2012; Mar-Apr 14 (Suppl. 02) e46.
• 61 Hanoch Y, Rolison J, Miron-Shatz T. What do men understand about lifetime risk following genetic testing? The effect of context and numeracy. Health Psychology 2011; 31 (Suppl. 04) 530-3.
• 62 Hanoch Y, Miron-Shatz T, Himmelstein M. Genetic testing and risk interpretation: How do women understand lifetime risk results?. Judgment and Decision Making 2012; 5 (Suppl. 02) 116-23.
• 63 Miron-Shatz T, Hanoch Y, Graef D, Sagi M. Presentation format, numeracy, and emotional reactions: The case of prenatal screening tests. J Health Commun 2009; 14 (Suppl. 05) 439-50.
• 64 Miron-Shatz T, Bowen B, Diefenbach M, Goldacre B, Mühlhauser I, Smith RSW. et al. From blind acceptance to active inquiry: Jumping the barriers to health literacy. In: Gigerenzer G, Gray JAM. editors. Better doctors, better patients, better decisions: Envisioning healthcare 2020. Strüngmann Forum Report (Vol. 6). Cambridge: MIT Press; 2011. p. 191-212
• 65 Hanoch Y, Miron-Shatz T, Cole H, Himmelstein M, Federman AD. Choice, numeracy and physicians-in-training performance: The case of Medicare Part. D. Health Psychol 2010; 29 (Suppl. 04) 454-9.
• 66 Miron-Shatz T, Barron G, Hanoch Y, Gummerman M, Doniger GM. To give or not to give: A word to the wise, two to the wiser: Parental experience and adherence to the food and drug administration warning about over-the-counter cough and cold medicine usage. Judgment and Decision Making 2010; 5 (Suppl. 06) 428-36.
• 67 Kim J. The intersection of the quantified self movement and big data. [Internet] 2013. [Cited March 18, 2014]. Available from: searchhealthit.techtarget.com/opinion/The-intersection-of-the-quantified-self-movement-and-big-data
• 68 Feinberg HV. From shared decision making to patient-centered decision making. Isr J Health Policy Res 2012; 1: 6.
• 69 Aetna: Carepass.. [Internet] 2013. [Cited November 30, 2013]. Available from: https://www.carepass.com/carepass/getstarted
• 70 Turner MR, Wicks P, Brownstein CA, Massagli MP, Toronjo M, Talbot K. et al. Concordance between site of onset and limb dominance in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry 2011; Aug 82 (Suppl. 08) 853-4.
• 71 Hanoch Y, Gummerum M, Miron-Shatz T. Trust and adherence to the FDA warning regarding cough and cold medicine for children under two. Child Care Health Dev 2010; 36 (Suppl. 06) 795-804.
• 72 Brownstein CA, Wicks P. The potential research impact of patient reported outcomes on osteo-genesis imperfecta. Clin Orthop Relat Res 2010; Oct 468 (Suppl. 10) 2581-5.
• 73 Wicks P, Massagli M, Frost J, Brownstein C, Okun S, Vaughan T. et al. Sharing health data for better outcomes on PatientsLikeMe. J Med Internet Res 2010; 12 (Suppl. 02) e19. [Cited November 30, 2013]. Available from: http://www.jmir.org/2010/2/e19/v12i2e19
• 74 Siemens G. [Internet] 2004. [Cited November 30, 2013]. Available from: Connectivism: A learning theory for the digital age. www.elearnspace.org/Articles/connectivism.htm
• 75 McHorney CA. The Adherence Estimator: a brief, proximal screener for patient propensity to adhere to prescription medications for chronic disease. Curr Med Res Opin 2009; 25 (Suppl. 01) 215-38.
• 76 Hussain F. E-learning 3.0 = e-learning 2.0 + web 3.0?. IADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2012).