Skip to main content
SpringerLink
Log in

The Effectiveness of Big Data in Health Care: A Systematic Review

  • Conference paper
Metadata and Semantics Research (MTSR 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 478))

Included in the following conference series:

Abstract

There is a consensus among scientists that the analysis of Big Data in health care (such as electronic health records, patient reported outcomes or in-motion data) can improve clinical research and the quality of care provided to patients. Yet there is little knowledge about the actual effectiveness of Big Data in the health care sector. The aim of this study was to perform a systematic review of the literature in order to determine the extent to which Big Data applications in health care systems have managed to improve patient experiences and clinicians’ behavior as well as the quality of care provided to patients. All searches for relevant articles were performed in the PubMed database. From the 108 potentially relevant articles 12 satisfied the inclusion criteria for this study. The findings showed that in the case of nine articles the researchers reported positive effect of Big Data. However, some negative results were recorded in the case of three articles. The main benefits of Big Data application involved positive behavior change, improved usability and efficient decision support. However, problems were identified for technology acceptance. Most problems occurred in the case of systems processing heterogeneous datasets, patient reported outcomes and in motion data, as opposed to electronic health record systems. The paper concludes by highlighting some areas of investigation where further research is needed to understand the use of Big Data in health care and improve its effectiveness.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., Tufano, P.: Analytics: The real-world use of Big Data: How innovative enterprises extract value from uncertain data. IBM Global Business Services, Business Analytics and Optimization, Executive Report (2012), http://public.dhe.ibm.com/common/ssi/ecm/en/gbe03519usen/GBE03519USEN.PDF

  2. Holbrook, A., Grootendorst, P., Willison, D., Goldsmith, G., Sebaldt, R., Keshavjee, K.: Can current electronic systems meet drug safety and effectiveness requirements? In. In: AMIA Annual Symposium Proceedings 2005, pp. 335–339 (2005), http://www.ncbi.nlm.nih.gov/pubmed/16779057

  3. Hrovat, G., Stiglic, G., Kokol, P., Ojsteršek, M.: Contrasting temporal trend discovery for large healthcare databases. Computer Methods and Programs in Biomedicine 113(1), 251–257 (2014)

    Article  Google Scholar 

  4. Valuck, R.J., Anderson, H.O., Libby, A.M., Brandt, E., Bryan, C., Allen, R.R., Staton, E.W., West, D.R., Pace, W.D.: Enhancing electronic health record measurement of depression severity and suicide ideation: a Distributed Ambulatory Research in Therapeutics Network (DARTNet) study. Journal of the American Board of Family Medicine 25(5), 582–593 (2012), http://www.jabfm.org/content/25/5/582.long

    Article  Google Scholar 

  5. Shen, C.P., Zhou, W., Lin, F.S., Sung, H.Y., Lam, Y.Y., Chen, W., Lin, J.W., Pan, M.K., Chiu, M.J., Lai, F.: Epilepsy analytic system with cloud computing. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1644–1647 (2013)

    Google Scholar 

  6. Holzner, B., Giesinger, J.M., Pinggera, J., Zugal, S., Schöpf, F., Oberguggenberger, A.S., Gamper, E.M., Zabernigg, A., Weber, B., Rumpold, G.: The Computer-based Health Evaluation Software (CHES): a software for electronic patient-reported outcome monitoring. BMC Medical Informatics and Decision Making 12(126) (November 9, 2012), http://www.biomedcentral.com/1472-6947/12/126

  7. Wu, A.W., Jensen, R.E., Salzberg, C., Snyder, C.: Advances in the use of Patient Reported Outcome measures in Electronic Health Records: including case studies. Technical report (2013), http://www.pcori.org/assets/2013/11/PCORI-PRO-Workshop-EHR-Landscape-Review-111913.pdf

  8. Wang, W., Krishnan, E.: Big Data and Clinicians: a review of the state of the science. JMIR Medical Informatics 2(1) (2014)

    Google Scholar 

  9. Slonim, N., Carmeli, B., Goldsteen, A., Keller, O., Kent, C., Rinott, R.: Knowledge-Analytics synergy in clinical decision support. Studies in Health Technology and Informatics 180, 703–707 (2012)

    Google Scholar 

  10. Simpao, A.F., Ahumada, L.M., Galnez, J.A., Rehman, M.A.: A review of analytics anaclinical informatics in health care. Journal of Medical Systems 38(4), 45 (2014)

    Article  Google Scholar 

  11. Ainsworth, J., Buchan, I.: COCPIT: a tool for integrated care pathway variance analysis. Studies in Health Technology and Informatics 180, 995–999 (2012)

    Google Scholar 

  12. Olive, M., Laswood, A., Solomonides, T.: Care pathway records with ontologies: potential uses in medical research and healthcare. International Journal of Care Pathways 15(1), 15–17 (2011)

    Article  Google Scholar 

  13. Jensen, R.E., Snyder, C.F., Abernethy, A.P., Basch, E., Potosky, A.L., Roberts, A.C., Loeffler, D.R., Reeve, B.B.: Review of electronic patient-reported outcomes systems used in cancer clinical care. Journal of Oncology Practice (December 2013)

    Google Scholar 

  14. Ainsworth, J., Palmier-Claus, J.E., Machin, M., Barrowclough, C., Dunn, G., Rogers, A., Buchan, I., Barkus, E., Kapur, S., Wykes, T., Hopkins, R.S., Lewis, S.: A comparison of two delivery modalities of a mobile phone-based assessment for serious mental illness: native smartphone application vs text-messaging only implementations. Journal of Medical Internet Research 15(4) (2013)

    Google Scholar 

  15. Kelty, T.L., Morgan, P.J., Lubans, D.R.: Efficacy and feasibility of the “girls’ recreational activity support program using information technology”: a pilot randomized controlled trial. Advances in Physical Education 2(1), 10–16 (2012)

    Article  Google Scholar 

  16. Chew, C., Eysenbach, G.: Pandemics in the age of twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLOS One 5(11) (2010), http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0014118

  17. Kohli, M.D., Warrnock, M., Daly, M., Toland, C., Meenan, C., Nagy, P.G.: Building blocks for a clinical imaging informatics environment. Journal of Digital Imaging 27(2), 174–181 (2014)

    Article  Google Scholar 

  18. Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M.: A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroengineering and Rehabilitation 9(21) (2012), http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3354997/

  19. Clifton, L., Clifton, D.A., Pimentel, M.A.F., Watkinson, P.J., Tarassenko, L.: Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors. IEEE Journal of Biomedical and Health Informatics 18(3), 722–730 (2014)

    Article  Google Scholar 

  20. Jayapandian, C.P., Chen, C.H., Bozorgi, A., Lhatoo, S.D., Zhang, G.Q., Sahoo, S.S.: Electrophysiological signal analysis and visualization using Cloudwave for epilepsy clinical research. Studies in Health Technology and Informatics 192, 817–821 (2013)

    Google Scholar 

  21. Tseng, K.C., Hsu, C.L., Chuang, Y.H.: Designing an intelligent health monitoring system and exploring user acceptance for the elderly. Journal of Medical Systems 37(6), 9967 (2013)

    Article  Google Scholar 

  22. Shams, I., Ajorlou, S., Yang, K.: A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD. Health Care Management Science (May 2014)

    Google Scholar 

  23. Graf, T., Erskine, A., Steele Jr., G.D.: Leveraging data to systematically improve care: coronary artery disease management at Geisinger. Journal of Ambulatory Care Management 37(3), 199–205 (2014)

    Article  Google Scholar 

  24. Suh, M.K., Moin, T., Woodbridge, J., Lan, M., Ghasemzadeh, H., Bui, A., Ahmadi, S., Sarrafzadeh, M.: Dynamic self-adaptive remote health monitoring system for diabetics. In: 2010 Annual International Conference of the IEEE Proceedings of Engineering in Medicine and Biology Society (EMBC), pp. 2223–2226 (2012)

    Google Scholar 

  25. Perer, A., Sun, J.: MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression. In: AMIA Annual Symposium Proceedings, pp. 716–725 (2012), http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540494/

  26. Quinn, C.C., Clough, S.S., Minor, J.M., Lender, D., Okafor, M.C., Gruber-Baldini, A.: WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technology & Therapeutics 10(3), 160–168 (2008)

    Article  Google Scholar 

  27. Holroyd-Leduc, J.M., Lorenzetti, D., Straus, S.E., Sykes, L., Quan, H.: The impact of the electronic medical record on structure, process, and outcomes within primary care: a systematic review of the evidence. Journal of the American Medical Informatics Association 18, 732–737 (2011), http://jamia.bmj.com/content/18/6/732.full.pdf+html

    Article  Google Scholar 

  28. Kyoungyoung, J., Gang-Hoon, K.: Potentiality of Big Data in the Medical Sector: Focus on How to Reshape the Healthcare System. Health Informatics Resources 19(2), 79–85 (2013), http://synapse.koreamed.org/search.php?where=aview&id=10.4258/hir.2013.19.2.79&code=1088HIR&vmode=FULL

    Google Scholar 

  29. Acquadro, C., Berzon, R., Dubois, D., Leidy, N.K., Marquis, P., Revicki, D., Rothman, M.: Incorporating the patient’s perspective into drug development and communication: an ad hoc task force report of the patient-reported outcomes (PRO) harmonization group meeting at the Food and Drug Administration, February 16, 2001. Value Health 6(5), 522–531 (2003)

    Article  Google Scholar 

  30. Wu, X., Zhu, X., Wu, G.-Q., Ding, W.: Data mining with big data. IEEE Transactions on Knowledge and Data Engineering 26(1), 97–107 (2014)

    Article  Google Scholar 

  31. Shaikh, A.R., Butte, A.J., Schully, S.D., Dalton, W.S., Khoury, K.J., Hesse, B.W.: Collaborative biomedicine in the age of big data: the case of cancer. Journal of Medical Internet Research 16(4), 101 (2014)

    Article  Google Scholar 

  32. Yoo, C., Ramirez, L., Liuzzi, J.: Big Data Analysis Using Modern Statistical and Machine Learning Methods in Medicine. International Neurourology Journal 18(2), 50–57 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Database & Information Systems Group (DBIS), Laboratory on Digital Libraries and Electronic Publishing, Department of Archives, Library Science and Museum Studies, Ionian University, Ioannou Theotoki 72, 49100, Corfu, Greece

    Panorea Gaitanou

  2. Benaki Museum Library, Koumbari 1, 10674, Athens, Greece

    Panorea Gaitanou

  3. Department of Library Science and Information Systems, Alexander Technological Educational Institute (ATEI) of Thessaloniki, 57400, Thessaloniki, Greece

    Emmanouel Garoufallou

  4. HeRC, University of Manchester, UK

    Panos Balatsoukas

Authors
  1. Panorea Gaitanou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emmanouel Garoufallou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Panos Balatsoukas
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Technik und Wirtschaft Fakulät, Hochschule Karlsruhe, Amalienstr. 81-87, 76133, Karlsruhe, Germany

    Sissi Closs

  2. Karlsruhe Institute of Technology, Germany

    Rudi Studer

  3. Department of Library Science & Information Systems, Alexander Technological Educational Institute of Thessaloniki, Sindos,Thessaloniki, Greece

    Emmanouel Garoufallou

  4. Information Engineering Research Unit, Computer Science Department, University of Alcalá, Ctra. Barcelona km. 33.6, 28871, Alcalá de Henares, Madrid, Spain

    Miguel-Angel Sicilia

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Gaitanou, P., Garoufallou, E., Balatsoukas, P. (2014). The Effectiveness of Big Data in Health Care: A Systematic Review. In: Closs, S., Studer, R., Garoufallou, E., Sicilia, MA. (eds) Metadata and Semantics Research. MTSR 2014. Communications in Computer and Information Science, vol 478. Springer, Cham. https://doi.org/10.1007/978-3-319-13674-5_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13674-5_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13673-8

  • Online ISBN: 978-3-319-13674-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation