Skip to main content

Advertisement

Log in

Panacea of challenges in real-world application of big data analytics in healthcare sector

  • Original Article
  • Published:
Journal of Data, Information and Management Aims and scope Submit manuscript

Abstract

Big data analytics is emerging ever since it has been introduced in the healthcare sector. It has given tools to gather, operate, assess, and associate large volumes of disparate, structured and unstructured data that are generated by present healthcare systems. Big data has been lately functional towards helping in the process of care delivery and disease exploration. Howbeit, due to some fundamental problems, the progress in care delivery and disease exploration is blocked. Fundamental problems such as data cleaning, capturing, security and privacy, storage, and how data is visualized hinder the expansion of big data in the healthcare sector. In this paper, we discuss these challenges, methods used to overcome these challenges, and results obtained. Based on the obtained results; the conclusion has been drawn to keep advancing in the healthcare sector.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Data availability

All relevant data and material are presented in the main paper.

References

  • Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ (2015) Big data for health. IEEE J Biomed Health Inform 19:1193–1208

    Article  Google Scholar 

  • Archenaa J, Anita EM (2015) A survey of big data analytics in healthcare and government. Procedia Comput Sci 50:408–413

    Article  Google Scholar 

  • Bachiri M, José A, Alemán FL, Toval A (2016) Mobile personal health records for pregnancy monitoring functionalities: analysis and potential. Computer Methods and Program in Bio medicine 134:121–135

    Article  Google Scholar 

  • Barsalou, M., 2018. Defect vs. defective know the differences. Qual Prog

  • Beauchamp A, Buchbinder R, Dodson S, Batterham RW, Elsworth GR, McPhee C, Sparkes L, Hawkins M, Osborne RH (2015) Distribution of health literacy strengths and weaknesses across socio-demographic groups: a cross-sectional survey using the health literacy questionnaire (HLQ). BMC Public Health 15:678

    Article  Google Scholar 

  • Borne, K. 2014. Top 10 big data challenges – a serious look at 10 big data V’s. MAPR, 2014:NO4, 80

  • Bracco D, Favre JB, Bissonnette B, Wasserfallen JB, Revelly JP, Ravussin P, Chiolero R (2001) Human errors in a multidisciplinary intensive care unit: a 1-year prospective study. Intensive Care Med 27:137–145. https://doi.org/10.1007/s001340000751

    Article  Google Scholar 

  • Costa FF (2014) Big data in biomedicine. Drug Discov Today 19:433–440

    Article  Google Scholar 

  • Coulter A (2012) Patient engagement--what works? J Ambul Care Manage 35(2):80–89

    Article  MathSciNet  Google Scholar 

  • Dodson S, Beauchamp A, Batterham RW, Osborne RH (2017) Information sheet 1: what is health literacy? In: Ophelia toolkit: a step-by-step guide for identifying and responding to health literacy needs within local communities, pp 1–52

    Google Scholar 

  • El aboudi N, Benhlima L (2018) Big data Management for Healthcare Systems: architecture, requirements, and implementation. Adv Bioinforma 2018:1–10

    Article  Google Scholar 

  • Elwyn G, Laitner S, Coulter A, Walker E, Watson P (2010) Thomson R. implementing shared decision making in the NHS. BMJ 341:c5146

    Article  Google Scholar 

  • Evans, L.M., 2013. System and method for thematically arranging clusters in a visual display 1–24

  • Evans, R., 2015. Apache storm, a hands on tutorial. 2015 IEEE international conference on cloud engineering. https://doi.org/10.1109/IC2E.2015.67

  • Fluhrer S, Mantin I, Shamir A (2001) Weaknesses in the key scheduling algorithm of RC4. International Workshop on Selected Areas in Cryptography:1–24

  • Garrouste-Orgeas M, Timsit JF, Vesin A, Schwebel C, Arnodo P, Lefrant JY, Souweine B, Tabah A, Charpentier J, Gontier O, Fieux F, Mourvillier B, Troché G, Reignier J, Dumay MF, Azoulay E, Reignier B, Carlet J, Soufir L (2010) Selected medical errors in the intensive care unit: results of the IATROREF study: parts I and II on behalf of the Outcomerea study group. Am J Respir Crit Care Med 181:134–142

    Article  Google Scholar 

  • Gschwandtner, T., Aigner, W., Kaiser, K., Miksch, S., Seyfang, A., 2011. CareCruiser: exploring and visualizing plans, events, and effects interactively in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 43–50

  • Guyon A, Bock A, Buback L, Knittela B (2016) Mobile-Based Nutrition and Child Health Monitoring to Inform Program Development: An Experience from Liberia. Global Health: Scienceand Practice 4(4):661–674

  • Hermon, R., Williams, P.A. 2014. Big data in healthcare: what is it used for? In: Australian Ehealth Informatics and Security Conference 40–9

  • Hsieh J, Li A, Yang C (2013) Mobile, cloud, and big data computing: contributions, challenges, and new directions in Telecardiology. Int J Environ Res Public Health 10(11):6131–6153. https://doi.org/10.3390/ijerph10116131

    Article  Google Scholar 

  • IBM Initiate workbench CloverETL User's Guide 9(5):1–102

  • Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture 2:1–12

    Article  Google Scholar 

  • Kakkad V, Patel M, Shah M (2019) Biometric authentication and image encryption for image security in cloud framework. Multiscale and Multidiscip Model Exp and Des:1–16. https://doi.org/10.1007/s41939-019-00049-y

  • Linden H, Kalra D, Hasman A, Talmon J (2009) Inter-organizational future proof EHR systems: a review of the security and privacy related issues. Int J Med Inform 78(3):141–160

    Article  Google Scholar 

  • Liu K, Kargupta H, Ryan J (2006) Random projection-based multiplicative data perturbation for privacy preserving distributed data mining. in IEEE Transactions on Knowledge and Data Engineering 18(1):92–106

    Article  Google Scholar 

  • Liu Z, Jiangz B, Heer J (2013) imMens: real-time visual querying of big data. Eurographics Conference on Visualization (EuroVis) 32(3):421–430

    Google Scholar 

  • Mckinsey Global Institute (2011) Big Data: The next frontier for innovation, competition and productivity:1–20

  • Mohammed N, Fung BCM, Hung PCK, Lee CK (2010) Centralized and distributed anonymization for high-dimensional healthcare data. ACM Trans Knowl Discov Data 4:1–33

    Article  Google Scholar 

  • Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M (2013) Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry 35(4):332–338

    Article  Google Scholar 

  • Munzner, T., 2011. Visualization principles, keynote at workshop on visualizing biological data (VIZBI 2011),

  • Naqishbandi TC, Sheriff I, Qazi S (2015) Big data, CEP and IoT: redefining holistic healthcare information systems and analytics. Int J Eng Res and Technol 4(1):1–6

    Article  Google Scholar 

  • Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: distributed stream computing platform. IEEE International Conference on Data Mining Workshops:170–177. https://doi.org/10.1109/ICDMW.2010.172

  • Pelegris P, Banitsas K, Orbach T, Marias K (2010) A novel method to detect heart beat rate using a mobile phone. In: 2010 annual international conference of the IEEE engineering in medicine and biology, pp 5488–5491

    Chapter  Google Scholar 

  • Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Information Science and Systems 2(1). https://doi.org/10.1186/2047-2501-2-3

  • rasad, S., Sha, M.S.N., 2013. NextGen data persistence pattern in healthcare: polyglot persistence, in Proceedings of the 4th 14 BioMed Research international international conference on computing, communications and networking technologies (ICCCNT ‘13), 1–8

  • Rind A, Wang TD, Aigner W, Miksch S, Wongsuphasawat K, Plaisant C, Shneiderman B (2011) Interactive Information Visualization to Explore and Query Electronic Health Records. Foundations and TrendsR in Human–Computer Interaction 5(3):207–298c. https://doi.org/10.1561/1100000039

    Article  Google Scholar 

  • Senthilkumar SA, Rai BK, Meshram AA, Gunasekaran A, Chandrakumarmangalam S (2018) Big data in healthcare management: a review of literature. American Journal of Theoretical and Applied Business 4(2):57–69

    Article  Google Scholar 

  • Shah R, Ecchpal R, Nair S (2015) Big data in healthcare analytics. International Journal of Recent and Innovations Trends in Computing and Communications 4(10):134–138

    Google Scholar 

  • Shahrivari S (2014) Beyond batch processing: towards real-time and streaming big data. Computers 3:117–129

    Article  Google Scholar 

  • Shneiderman B, Plaisant C, Hesse B (2013) Improving heathcare with interactive visualization. Computer 46(5):58–66

    Article  Google Scholar 

  • Snee RD, DeVeaux RD, Hoerl RW (2014) Follow the fundamentals: four data analysis basics will help you do big data projects the right way, pp 1–6

    Google Scholar 

  • Song TM, Song J, An JY, Hayman LL, Woo JM (2014) Psychological and social factors affecting internet searches on suicide in Korea: a big data analysis of Google search trends. Yonsei Med J 55(1):254–263

    Article  Google Scholar 

  • Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, Llewellyn-Thomas H, Lyddiatt A, Thomson R, Trevena L, Wu JH (2011) Decision aids for people facing health treatment or screening decisions. Cochrane database Syst rev (10):Cd001431 WHO, 2007. Commission on social determinants of health. Achieving health equity: from root causes to fair outcomes. WHO, Geneva, p 2007

    Google Scholar 

  • Sucharitha V, Subash SR, Prakash P (2014) Visualization of big data: its tools and challenges. Int J Appl Eng Res 9(18):5277–5290

    Google Scholar 

  • Sukumar, S.R., Natarajan, R., Ferrell, R.K., 2014. Data Quality Challenges in Healthcare Claims Data: Experiences and Remedies. U.S. Department of Energy, 1–14

  • Sukumar SR, Natarajan R, Ferrell RK (2015a) Quality of big data in health care. Int J Health Care QualAssur 28(6):621–634

    Article  Google Scholar 

  • Sukumar SR, Natarajan R, Ferrell RK (2015b) Big data’ in health care: how good is it? International Journal of Health Care Quality Assurance:1–9

  • Vahdat S, Hamzehgardeshi L, Hessam S, Hamzehgardeshi Z (2014) Patient involvement in health care decision making: a review. Iran Red Crescent Med J 16(1):e12454

    Article  Google Scholar 

  • Van de Bovenkamp HM, Trappenburg MJ, Grit KJ (2010) Patient participation in collective healthcare decision making: the Dutch model. Health Expect 13(1):73–85

    Article  Google Scholar 

  • Viceconti M, Hunter P, Hose R (2015) Big data, big knowledge: big data for personalized healthcare. IEEE J Biomed Health Inform 19:1209–1215

    Article  Google Scholar 

  • Vinay, B., Truta, T., 2006. Privacy protection: p-sensitive k-anonymity property. 22nd international conference on data engineering workshops. 1–94

  • Wang L, Alexander CA (2015) Big data in medical applications and health care. Current Research in Medicine 6(1):1–8

    Article  Google Scholar 

  • Wang L, Wang G, Alexander CA (2015) Big data and visualization: methods, challenges and technology Progress. Digital Technologies 1(1):33–38

    Google Scholar 

  • Xu L, Jiang C, Wang G, Yuan G, Ren Y (2014) Information security in big data: privacy and data mining. IEEE 2:1149–1176

    Google Scholar 

  • Yu, W.D., Kollipara, M., Penmetsa, R., Elliadka, S. 2013. A distributed storage solution for cloud based e-healthcare information system,” in proceedings of the IEEE 15th international conference on e-health networking, Applications & services (Healthcom ‘13), 476–480, Lisbon, Portugal, October 2013

  • Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin MJ, Ghodsi A, Gonzalez J, Shenker S, Stoica I (2016) Apache spark: a unified engine for big data processing. Commun ACM 59(11):56–65. https://doi.org/10.1145/2934664

    Article  Google Scholar 

  • Zhang R, Liu L (2010) Security models and requirements for healthcare application clouds. 2010 IEEE 3rd international conference on cloud. Computing.:268–275

  • Zhu Z, Hoon HB, Teow KL (2017) Interactive data visualization techniques applied to healthcare decision making. Decision Management: Concepts, Methodologies, Tools, and Applications:1–15

  • Van de Bovnkamp HM, Trappenburg MJ, Grit KJ, (2010) Patient participation in collective healthcare decision making: the Dutch model. Health Expect 13(1):73–85

Download references

Acknowledgements

The authors are grateful to Department of Computer engineering, Indus University and School of Technology, Pandit Deendayal Petroleum University for the permission to publish this research.

Author information

Authors and Affiliations

Authors

Contributions

All the authors make substantial contribution in this manuscript. GS, AS and MS participated in drafting the manuscript. GS and AS wrote the main manuscript, all the authors discussed the results and implication on the manuscript at all stages.

Corresponding author

Correspondence to Manan Shah.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shah, G., Shah, A. & Shah, M. Panacea of challenges in real-world application of big data analytics in healthcare sector. J. of Data, Inf. and Manag. 1, 107–116 (2019). https://doi.org/10.1007/s42488-019-00010-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42488-019-00010-1

Keywords

Navigation