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Elements of Healthcare Big Data Analytics

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Big Data Analytics in Healthcare

Part of the book series: Studies in Big Data ((SBD,volume 66))

Abstract

As the focus of healthcare industry shifts towards patient-centric model, healthcare is increasingly becoming data-driven in nature. Alongside this, the newer developments in technology are opening ways for harnessing healthcare big data for improved services. The application of analytics over big data in healthcare offers a wide range of possibilities for the delivery of high quality patient care at affordable price. Although a lot has been discussed about the promise of big data analytics in healthcare, there is still a lack of its usability in the real world scenario. The healthcare organizations are starting to embrace this technology, yet many of them are still far from achieving its benefits to the full potential. The challenges that these organizations face are complex. This chapter begins with discussing about the key issues and challenges that often afflicts the utilization of big data analytics in healthcare organizations. It then highlights the essential components for effective integration of big data analytics into healthcare. It also explores important foundational steps for beginning a big data analytics program within an organization. The objective is to provide the guiding principles for successful implementation of big data technology.

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References

  1. Barry, M.J., Edgman-levitan, S., Billingham, V.: Shared decision making—The Pinnacle of patient-centered care 780–781 (2016)

    Google Scholar 

  2. Thygeson, M., Frosch, D.L., Carman, K.L., Pande, N.: Patient + family engagement in a changing health care landscape. Gordon Betty Moore Found. Conv. Patient Fam. Engagem. (2014)

    Google Scholar 

  3. Cognos. Performance Management in Healthcare, Management 1–15 (2008). http://www-07.ibm.com/solutions/au/healthcare/pdf/IBM_Cognos_white_paper_performance_management_in_healthcare.pdf

  4. Belle, A., Thiagarajan, R., Soroushmehr, S.M.R., Navidi, F., Beard, D.A., Najarian, K.: Big data analytics in healthcare, Hindawi Publ. Corp. 1–16 (2015). https://doi.org/10.1155/2015/370194

  5. Jee, K., Kim, G.H.: Potentiality of big data in the medical sector: focus on how to reshape the healthcare system. Healthc. Inform. Res. 19, 79–85 (2013). https://doi.org/10.4258/hir.2013.19.2.79

    Article  Google Scholar 

  6. Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014). https://doi.org/10.1186/2047-2501-2-3

    Article  Google Scholar 

  7. Kraft, M.R., Desouza, K.C., Androwich, I.: Data mining in healthcare information systems: case study of a veterans’ administration spinal cord injury population, IEEE (2002)

    Google Scholar 

  8. Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V.: Critical analysis of big data challenges and analytical methods. J. Bus. Res. 70, 263–286 (2017). https://doi.org/10.1016/j.jbusres.2016.08.001

    Article  Google Scholar 

  9. Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Shahabi, C.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)

    Article  Google Scholar 

  10. Keen, J., Calinescu, R., Paige, R., Rooksby, J.: Big data + politics = open data: the case of health care data in England. Policy Internet 5, 228–243 (2013). https://doi.org/10.1002/1944-2866.POI330

    Article  Google Scholar 

  11. Salas-Vega, S., Haimann, A., Mossialos, E.: Big data and health care: challenges and opportunities for coordinated policy development in the EU. Health Syst. Reform. 1, 285–300 (2015). https://doi.org/10.1080/23288604.2015.1091538

    Article  Google Scholar 

  12. Ernst & Young: Big data: changing the way businesses compete and operate (2014). http://www.ey.com/Publication/vwLUAssets/EY_-_Big_data:_changing_the_way_businesses_operate/$FILE/EY-Insights-on-GRC-Big-data.pdf

  13. University of Illinois: Why data security is the biggest concern of health care (2019). https://healthinformatics.uic.edu/blog/why-data-security-is-the-biggest-concern-of-health-care/. Accessed 12 Mar 2019

  14. Meingast, M., Roosta, T., Sastry, S.: Security and privacy issues with health care information technology. In: Conference on Proceedings of IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 5453–5458 (2006). https://doi.org/10.1109/iembs.2006.260060

  15. Ingrammicroadvisor: The top six challenges of healthcare data management (2017). http://www.ingrammicroadvisor.com/data-center/the-top-six-challenges-of-healthcare-data-management. Accessed 30 May 2018

  16. Suyati: 8 challenges of big data in healthcare (2015). https://suyati.com/blog/8-challenges-of-bigdata-in-healthcare/. Accessed 3 June 2018

  17. UNC Kenan-Flagler: 4 steps to bridge the big data talent gap (2016). http://execdev.kenan-flagler.unc.edu/blog/4-steps-to-bridge-the-big-data-talent-gap. Accessed 4 June 2018

  18. Big data talent shortage a potential challenge at fortune 1000 organizations (2012). http://newvantage.com/wp-content/uploads/2012/11/NVP-Press-Release-Big-Data-Talent-Survey-111312.pdf

  19. Agrawal, D., Bernstein, P., Bertino, E.: Challenges and opportunities with big data 2011-1. Proc. VLDB Endow. 1–16 (2011). http://dl.acm.org/citation.cfm?id=2367572%5Cnhttp://docs.lib.purdue.edu/cctech/1/

  20. Zhou, L., Hripcsak, G.: Temporal reasoning with medical data—a review with emphasis on medical natural language processing. J. Biomed. Inform. 40, 183–202 (2007). https://doi.org/10.1016/j.jbi.2006.12.009

    Article  Google Scholar 

  21. Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 1–11 (2017). https://doi.org/10.1093/bib/bbx044

  22. Kuipers, P., Kendall, E., Ehrlich, C.: Complexity in healthcare: Implications for clinical education. Focus Health (2013). https://search.informit.com.au/documentSummary;dn=155911887123225;res=IELHEA

  23. TEK System: Executive summary big data: the next frontier (2013)

    Google Scholar 

  24. Sharma, A.: Organizations continue to face challenges with big data: lets deep dive. Anal. India Mag. (2018). https://analyticsindiamag.com/organizations-continue-to-face-challenges-with-big-data-lets-deep-dive/. Accessed 5 June 2018

  25. Marx, V.: Biology: the big challenges of big data. Nature 498, 255–260 (2013). https://doi.org/10.1038/498255a

    Article  Google Scholar 

  26. Dykes, B.: Actionable insights: the missing link between data and business value. Forbes (2016). https://www.forbes.com/sites/brentdykes/2016/04/26/actionable-insights-the-missing-link-between-data-and-business-value/#4e89b7cd51e5

  27. Harvey, C.: Big data challenges. Datamation (2017). https://www.datamation.com/big-data/big-data-challenges.html. Accessed 7 June 2018

  28. Bean, R.: Executives report measurable results from big data, but challenges remain. Forbes (2017). https://www.forbes.com/sites/ciocentral/2017/01/10/executives-report-measurable-results-from-big-data-but-challenges-remain/#536770a92287

  29. Bresnick, J.: Top 10 challenges of big data analytics in healthcare. Health IT Anal. (2017). https://healthitanalytics.com/news/top-10-challenges-of-big-data-analytics-in-healthcare (accessed June 6, 2018)

  30. Balachandran, B.M., Prasad, S.: Challenges and benefits of deploying big data analytics in the cloud for business intelligence. Proc. Comput. Sci. 112, 1112–1122 (2017). https://doi.org/10.1016/j.procs.2017.08.138

    Article  Google Scholar 

  31. Marr, B.: Building your big data infrastructure. Forbes (2016). https://www.forbes.com/sites/bernardmarr/2016/06/15/building-your-big-data-infrastructure-4-key-components-every-business-needs-to-consider/#39f6d7e7577e

  32. Moore, C.: Big data and analytics—five foundational elements. SiriusDecisions. (2014). https://www.siriusdecisions.com/blog/big-data-and-analytics-five-foundational-elements. Accessed 8 June 2018

  33. Singh, P., Mathur, R., Mondal, A., Bhattacharya, S.: The big ‘V’ of big data. Analytics (2014). http://analytics-magazine.org/the-big-v-of-big-data/

  34. Biesdorf, S., Court, D., Willmott, P.: Big data: what’s your plan? McKinsey Co. (2013). https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-whats-your-plan. Accessed 7 June 2018

  35. Güemes, C.: Data analytics as a service: unleashing the power of cloud and big data 18 (2013)

    Google Scholar 

  36. Grover, M.: Processing frameworks for Hadoop. O’Reilly Media (2015). https://www.oreilly.com/ideas/processing-frameworks-for-hadoop. Accessed 14 Mar 2019

  37. Eckerson, W.: Analytical modeling is both science and art. TechTarget (2013). https://searchbusinessanalytics.techtarget.com/opinion/Analytical-modeling-is-both-science-and-art. Accessed 4 June 2018

  38. Anaraki, A.K., Ayati, M., Kazemi, F.: Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern. Biomed. Eng. 39(1), 63–74 (2019)

    Article  Google Scholar 

  39. Araújo, F.H., Santana, A.M., Neto, P.D.A.S.: Using machine learning to support healthcare professionals in making preauthorisation decisions. Int. J. Med. Inform. 94, 1–7 (2016)

    Article  Google Scholar 

  40. Ernst & Young Analytics: Don’t forget the human element. Forbes Insights 1–42 (2015). http://www.forbes.com/forbesinsights/ey_data_analytics_2015/

  41. Ottenheimer, D.: The six elements of securing big data. MapR (2016). https://mapr.com/ebooks/securing-big-data-six-elements/chapter-6-six-elements-of-big-data-security.html

  42. Cisco Healthcare Security Perspectives: Protect your patients, your practice, yourself technical implementation guide cisco healthcare security perspectives (2007). https://www.cisco.com/c/dam/global/en_ca/solutions/strategy/healthcare/assets/docs/health_security_impgd.pdf

  43. European Patients Forum: The new EU regulation on the protection of personal data: what does it mean for patients? A guide for patients and patients’ organisations 2 The new EU Regulation on the protection of personal data: what does it mean for patients? 23 (2017). http://www.eu-patient.eu/globalassets/policy/data-protection/data-protection-guide-for-patients-organisations.pdf

  44. Eagle Consulting Partners: Health IT security risks and vulnerability management (2016). https://eagleconsultingpartners.com/newsletter/do-you-have-a-healthcare-it-vulnerability/. Accessed 8 June 2018

  45. Røstad, L.: Access control in healthcare information systems (2009). http://www.idi.ntnu.no/research/doctor_theses/lilliaro.pdf

  46. IBM, Health Analytics Roadmap (n.d.). https://www-03.ibm.com/industries/ca/en/healthcare/files/health_analytics_roadmap.pdf

  47. Allen, B., Coates, S., Karp, B., Lerchner, H.-P., Lourens, J., Penrose, T., Rai, S.: Understanding and auditing big data 22–23 (2017). https://www.iia.nl/SiteFiles/Publicaties/GTAG-Understanding-and-Auditing-Big-Data.pdf

  48. Think Big: Big data strategy definition 1–2 (n.d.). https://www.thinkbiganalytics.com/wp-content/uploads/2017/02/Think-Big-Big-Data-Strategy-Definition-Data-Sheet.pdf

  49. Hodgson-Abbott, G.: 12 key components of your data and analytics capability. Capgemini (2017). https://www.capgemini.com/gb-en/2017/01/12-key-components-of-your-data-and-analytics-capability/. Accessed 11 June 2018

  50. Eccella: Big data is nothing without the right people and culture (2016). http://blog.eccellaconsulting.com/human-element-big-data. Accessed 10 June 2018

  51. Smith, S.: 5 elements of a successful data analytics program. Audimation Serv. Inc. (2017). www.audimation.com/Resources/Articles/5-elements-of-a-successful-data-analytics-program-3073. Accessed 12 June 2018

  52. Eckerson, W.: Analytics program requires adaptability, collaboration and results. TechTarget (2012). https://searchbusinessanalytics.techtarget.com/feature/Analytics-program-requires-adaptability-collaboration-and-results. Accessed 12 June 2018

  53. Pal, S.: How to design a big data architecture in 6 easy steps. Saama (2016). https://www.saama.com/blog/design-big-data-architecture-6-easy-steps/. Accessed 9 June 2018

  54. Kalvakuntla, R.: How a modern data architecture will revolutionize the healthcare industry. https://cdn2.hubspot.net/hubfs/2106877/WhitePapers/MDA_revolutionizing_HC_Industry_WP_pdf?t=1528979762553&utm_campaign=ModernDataWarehouse2017&utm_source=hs_automation&utm_medium=email&utm_content=57227465&_hsenc=p2ANqtz-97yeTB_LwuzoJz6NBzmARnUIKKXmE5ADS48QDItkzwRb9ygE44b3QfCBxAAG7As_JgmOcyQi-4U2GFXTyloT932t54XQ7mL929rWBPsYuJbknNM2s&_hsmi=57227465

  55. Johnson, J.: The intersection of data analytics and data governance. Infogix (2018). https://www.infogix.com/blog/the-intersection-of-data-analytics-and-data-governance/. Accessed 12 June 2018

  56. Bresnick, J.: 56% of hospitals lack big data governance. Analytics plans. Health IT Anal. (2017). https://healthitanalytics.com/news/56-of-hospitals-lack-big-data-governance-analytics-plans. Accessed 11 June 2018

  57. Stoltzfus, J.: What are some of the biggest challenges with legacy migration? Techopedia (2016). https://www.techopedia.com/7/32194/technology-trends/big-data/what-are-some-of-the-biggest-challenges-with-legacy-migration. Accessed 10 June 10 2018

  58. Bresnick, J.: Healthcare big data analytics confuses half of providers. Health IT Anal. (2015). https://healthitanalytics.com/news/healthcare-big-data-analytics-confuses-half-of-providers. Accessed 11 June 2018

  59. Infomgmtexec: Transformational leadership for big data & analytics success—part 3: organizational design & cultural adoption (2014). https://infomgmtexec.me/2014/07/20/transformational-leadership-for-big-data-analytics-success-part-3-organizational-design-cultural-adoption/. Accessed 11 June 2018

  60. Bean, R.: Why cultural change is necessary for big data adoption. Forbes (2016). https://www.forbes.com/sites/ciocentral/2016/11/08/another-side-of-big-data-big-data-for-social-good-2/#1035e34a6628

  61. Félix, B.M., Tavares, E., Cavalcante, N.W.F.: Critical success factors for big data adoption in the virtual retail: magazine Luiza case study. Rev. Bus. Manag. 20, 112–126 (2018). https://doi.org/10.7819/rbgn.v20i1.3627

    Article  Google Scholar 

  62. Optum Clinical Solutions: Optum perioperative analytics and reporting solutions. https://www.optum.com/content/dam/optum/resources/brochures/Analytics_Reporting_Solutions_Brochure.pdf

  63. Health E Connections: Analytics & reporting (2015). https://www.healtheconnections.org/what-we-do/analytics-and-reporting/. Accessed 13 June 2018

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Author information

Authors and Affiliations

  1. Symbiosis International (Deemed University), Pune, India

    Nishita Mehta

  2. Symbiosis Institute of Health Sciences, Pune, India

    Anil Pandit & Meenal Kulkarni

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  1. Nishita Mehta
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  2. Anil Pandit
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  3. Meenal Kulkarni
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Correspondence to Nishita Mehta .

Editor information

Editors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India

    Anand J. Kulkarni

  2. Université Paris-Est Créteil Val de Marne, Créteil, France

    Patrick Siarry

  3. ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, Madhya Pradesh, India

    Pramod Kumar Singh

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  5. School of Engineering and Computer Science, Victoria University of Wellington, Kelburn, New Zealand

    Mengjie Zhang

  6. School of Computer Science, University of Sydney, Sydney, Australia

    Albert Zomaya

  7. Odette School of Business, University of Windsor, Windsor, ON, Canada

    Fazle Baki

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Mehta, N., Pandit, A., Kulkarni, M. (2020). Elements of Healthcare Big Data Analytics. In: Kulkarni, A., et al. Big Data Analytics in Healthcare. Studies in Big Data, vol 66. Springer, Cham. https://doi.org/10.1007/978-3-030-31672-3_2

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