The application of Big Data in medicine: current implications and future directions

Article

Abstract

Since the mid 1980s, the world has experienced an unprecedented explosion in the capacity to produce, store, and communicate data, primarily in digital formats. Simultaneously, access to computing technologies in the form of the personal PC, smartphone, and other handheld devices has mirrored this growth. With these enhanced capabilities of data storage and rapid computation as well as real-time delivery of information via the internet, the average daily consumption of data by an individual has grown exponentially. Unbeknownst to many, Big Data has silently crept into our daily routines and, with continued development of cheap data storage and availability of smart devices both regionally and in developing countries, the influence of Big Data will continue to grow. This influence has also carried over to healthcare. This paper will provide an overview of Big Data, its benefits, potential pitfalls, and the projected impact on the future of medicine in general and cardiology in particular.

Keywords

Big Data Cardiology Electrophysiology Analytics Data management 

References

  1. 1.
    Hilbert, M., & Lopez, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60–65.CrossRefPubMedGoogle Scholar
  2. 2.
    Cox, M. & D. Ellsworth, Application-controlled demand paging for out-of-core visualization. Proceedings of the 8th conference on Visualization’97, 1997: p. 235-ff.Google Scholar
  3. 3.
    Oxford english dictonary. http://www.oed.com/view/Entry/18833#eid301162177. Accessed 27 Sep 2015.
  4. 4.
    Press, G. (2015). 12 Big Data definitions: What’s Yours? Forbes. http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/. Accessed 27 Sep 2015.
  5. 5.
    Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Boston: Eamon Dolan/Houghton Mifflin Harcourt.Google Scholar
  6. 6.
    Maury’s wind and current chart, 3rd Edition, 1852. http://collections.lib.uwm.edu/cdm/ref/collection/agdm/id/1717. Accessed 27 Sep 2015.
  7. 7.
    Laney, D. (2001). 3D data management: controlling data volume, velocity, and varity. Meta Group. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 27 Sep 2015.
  8. 8.
    Bringing big data to the enterprise. IBM. http://www-01.ibm.com/software/data/bigdata/what-is-big-data.html. Accessed 27 Sep 2015.
  9. 9.
    The digital universe of opportunities: rich data and the increasing value of the internet of things. EMC Digital Universe with Research & Analysis by ICD. (2014). http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm. Accessed 27 Sep 2015.
  10. 10.
    Amazon S3 Pricing. https://aws.amazon.com/s3/pricing/. Accessed 27 Sep 2015.
  11. 11.
    Hughes G. (2011). How big is ‘big data’ in healthcare?. SAS Blogs. http://blogs.sas.com/content/hls/2011/10/21/how-big-is-big-data-in-healthcare/. Accessed 27 Sep 2015.
  12. 12.
    Internet live stats. http://www.internetlivestats.com/one-second/#youtube-band. Accessed 27 Sep 2015.
  13. 13.
    Statistics Youtube. (2015). https://www.youtube.com/yt/press/statistics.html. Accessed 27 Sep 2015.
  14. 14.
    Hartman, M., et al. (2015). National health spending in 2013: growth slows, remains in step with the overall economy. Health Affairs, 34(1), 150–160.CrossRefPubMedGoogle Scholar
  15. 15.
    Baum, S. (2015). 4 Ways healthcare is putting artificial intelligence, machine learning to use. MedCity News. http://medcitynews.com/2015/02/4-ways-healthcare-putting-artificial-intelligence-machine-learning-use/. Accessed 27 Sep 2015.
  16. 16.
    Winters-Miner, L. (2014). Seven ways predictive analytics can improve healthcare. Elsevier. http://www.elsevier.com/connect/seven-ways-predictive-analytics-can-improve-healthcare. Accessed 27 Sep 2015.
  17. 17.
  18. 18.
    Emilsson, L., et al. (2015). Review of 103 Swedish healthcare quality registries. Journal of Internal Medicine, 277(1), 94–136.CrossRefPubMedGoogle Scholar
  19. 19.
    Webster, P. C. (2014). Sweden’s health data goldmine. CMAJ, 186(9), E310.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Weintraub, W. S. (1998). Development of the American college of cardiology national cardiovascular data registry. The Journal of Invasive Cardiology, 10(8), 489–491.PubMedGoogle Scholar
  21. 21.
    Oetgen, W. J., Mullen, J. B., & Mirro, M. J. (2011). Cardiologists, the PINNACLE registry, and the “meaningful use” of electronic health records. Journal of the American College of Cardiology, 57(14), 1560–1563.CrossRefPubMedGoogle Scholar
  22. 22.
    Published manuscripts based on NCDR registries. National cardiovascular data registry. American College of Cardiology. (2015). http://cvquality.acc.org/~/media/QII/NCDR/Published%20Research%20Page/Aug%202015%20NCDR%20Published%20Manuscripts%20by%20Registry.ashx. Accessed 27 Sep 2015.
  23. 23.
    Wetterstrand K. DNA sequencing costs: data from the NHGRI genome sequencing program. http://www.genome.gov/sequencingcosts/. Accessed 27 Sep 2015.
  24. 24.
    FACT SHEET: President Obama’s precision medicine initiative. https://www.whitehouse.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative. Accessed 27 Sep 2015.
  25. 25.
    Chawla, N. V., & Davis, D. A. (2013). Bringing big data to personalized healthcare: a patient-centered framework. Journal of General Internal Medicine, 28(Suppl 3), S660–S665.CrossRefPubMedGoogle Scholar
  26. 26.
    Health eHeart Study. University of California, San Francisco. https://www.health-eheartstudy.org/. Accessed 6 Oct 2015.
  27. 27.
    Google flu trends. http://www.google.org/flutrends/about/; Accessed 26 Dec 2015.
  28. 28.
    Ginsberg J, Mohebbi MH, Patel RS, ABrammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/papers/detecting-influenza-epidemics.pdf. Accessed 26 Dec 2015.
  29. 29.
    Lazer, D., et al. (2014). Big data. The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203–1205.CrossRefPubMedGoogle Scholar
  30. 30.
    Kuehn, B. M. (2014). Agencies use social media to track foodborne illness. JAMA, 312(2), 117–118.CrossRefPubMedGoogle Scholar
  31. 31.
    Ram, S., et al. (2015). Predicting asthma-related emergency department visits using big data. IEEE Journal of Biomedical and Health Informatics, 19(4), 1216–1223.CrossRefPubMedGoogle Scholar
  32. 32.
    Kuehn, B. M. (2015). Twitter streams fuel Big Data approaches to health forecasting. JAMA, 314(19), 2010–2012.CrossRefPubMedGoogle Scholar
  33. 33.
    Body guardian system. Preventice medical systems. http://www.preventice.com/index.html. Accessed 6 Oct 2015.
  34. 34.
    Marewski, J. N., & Gigerenzer, G. (2012). Heuristic decision making in medicine. Dialogues in Clinical Neuroscience, 14(1), 77–89.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Abascal, V. M., et al. (1988). Echocardiographic evaluation of mitral valve structure and function in patients followed for at least 6 months after percutaneous balloon mitral valvuloplasty. Journal of the American College of Cardiology, 12(3), 606–615.CrossRefPubMedGoogle Scholar
  36. 36.
    Benza, R. L., et al. (2012). The REVEAL registry risk score calculator in patients newly diagnosed with pulmonary arterial hypertension. Chest, 141(2), 354–362.CrossRefPubMedGoogle Scholar
  37. 37.
    Conway Morris, A., et al. (2006). TIMI risk score accurately risk stratifies patients with undifferentiated chest pain presenting to an emergency department. Heart, 92(9), 1333–1334.CrossRefPubMedGoogle Scholar
  38. 38.
    Lip, G. Y., et al. (2010). Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest, 137(2), 263–272.CrossRefPubMedGoogle Scholar
  39. 39.
    Wilkins, G. T., et al. (1988). Percutaneous balloon dilatation of the mitral valve: an analysis of echocardiographic variables related to outcome and the mechanism of dilatation. British Heart Journal, 60(4), 299–308.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Serruys, P. W., et al. (2009). Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease. The New England Journal of Medicine, 360(10), 961–972.CrossRefPubMedGoogle Scholar
  41. 41.
    Janke, A. T., et al. (2015). Exploring the potential of predictive analytics and Big Data in emergency care. Annals of Emergency Medicine. doi:10.1016/j.annemergmed.2015.06.024.
  42. 42.
    Baxt, W. G. (1992). Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction. Annals of Emergency Medicine, 21(12), 1439–1444.CrossRefPubMedGoogle Scholar
  43. 43.
    Hindricks, G., et al. (2014). Quarterly vs. yearly clinical follow-up of remotely monitored recipients of prophylactic implantable cardioverter-defibrillators: results of the REFORM trial. European Heart Journal, 35(2), 98–105.CrossRefPubMedGoogle Scholar
  44. 44.
    Ricci, R. P., et al. (2013). Effectiveness of remote monitoring of CIEDs in detection and treatment of clinical and device-related cardiovascular events in daily practice: the HomeGuide Registry. Europace, 15(7), 970–977.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Slotwiner, D., et al. (2015). HRS expert consensus statement on remote interrogation and monitoring for cardiovascular implantable electronic devices. Heart Rhythm, 12(7), e69–e100.CrossRefPubMedGoogle Scholar
  46. 46.
    Saxon, L. A., et al. (2010). Long-term outcome after ICD and CRT implantation and influence of remote device follow-up: the ALTITUDE survival study. Circulation, 122(23), 2359–2367.CrossRefPubMedGoogle Scholar
  47. 47.
    Varma, N., et al. (2015). The relationship between level of adherence to automatic wireless remote monitoring and survival in pacemaker and defibrillator patients. Journal of the American College of Cardiology, 65(24), 2601–2610.CrossRefPubMedGoogle Scholar
  48. 48.
    Hayes, D. L., et al. (2011). Cardiac resynchronization therapy and the relationship of percent biventricular pacing to symptoms and survival. Heart Rhythm, 8(9), 1469–1475.CrossRefPubMedGoogle Scholar
  49. 49.
    Gilliam, F. R., et al. (2011). Real world evaluation of dual-zone ICD and CRT-D programming compared to single-zone programming: the ALTITUDE REDUCES study. Journal of Cardiovascular Electrophysiology, 22(9), 1023–1029.CrossRefPubMedGoogle Scholar
  50. 50.
    Health insurer anthem struck by massive data breach. Forbes. (2015). http://www.forbes.com/sites/gregorymcneal/2015/02/04/massive-data-breach-at-health-insurer-anthem-reveals-social-security-numbers-and-more/. Accessed 27 Sep 2015.
  51. 51.
    UCLA Health System data breach affects 4.5 million patients. Los Angeles Times. (2015). http://www.latimes.com/business/la-fi-ucla-medical-data-20150717-story.html. Accessed 27 Sep 2015.
  52. 52.
    Hacker Breached HealthCare.gov Insurance Site. (2014). The wall street journal. http://www.wsj.com/articles/hacker-breached-healthcare-gov-insurance-site-1409861043. Accessed 27 Sep 2015.
  53. 53.
    Ohm, Paul. (2009). Broken promises of privacy: responding to the surprising failure of anonymization. UCLA Law Review, Vol. 57, p. 1701, 2010; U of Colorado Law Legal Studies Research Paper No. 9–12. Available at SSRN: http://ssrn.com/abstract=1450006.
  54. 54.
    Benitez, K., & Malin, B. (2010). Evaluating re-identification risks with respect to the HIPAA privacy rule. Journal of the American Medical Informatics Association, 17(2), 169–177.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Xian, Y., Hammill, B. G., & Curtis, L. H. (2013). Data sources for heart failure comparative effectiveness research. Heart Failure Clinics, 9(1), 1–13.CrossRefPubMedGoogle Scholar
  56. 56.
    Dunlay, S. M., et al. (2008). Medical records and quality of care in acute coronary syndromes: results from CRUSADE. Archives of Internal Medicine, 168(15), 1692–1698.CrossRefPubMedGoogle Scholar
  57. 57.
    Lyu, H., et al. (2015). Prevalence and data transparency of national clinical registries in the United States. Journal for Healthcare Quality.Google Scholar
  58. 58.
    Roger, V. L. (2015). Of the importance of motherhood and apple pie. Circulation. Cardiovascular Quality and Outcomes, 8(4), 329–331.CrossRefPubMedGoogle Scholar
  59. 59.
    Roger, V. L., et al. (2015). Strategic transformation of population studies: recommendations of the working group on epidemiology and population sciences from the National Heart, Lung, and Blood Advisory Council and Board of External Experts. American Journal of Epidemiology, 181(6), 363–368.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Brown, M. T., & Bussell, J. K. (2011). Medication adherence: WHO cares? Mayo Clinic Proceedings, 86(4), 304–314.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Division of Cardiovascular DiseaseMayo Clinic FloridaJacksonvilleUSA

Personalised recommendations