Big Data in Healthcare Management: A Review of Literature

Senthilkumar SA; Bharatendara K Rai; Amruta A Meshram; Angappa Gunasekaran; Chandrakumarmangalam S

doi:doi:10.11648/j.ajtab.20180402.14

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Big Data in Healthcare Management: A Review of Literature

Senthilkumar SA, Bharatendara K Rai, Amruta A Meshram, Angappa Gunasekaran, Chandrakumarmangalam S

Published in American Journal of Theoretical and Applied Business (Volume 4, Issue 2)

Received: 4 March 2018 Accepted: 3 May 2018 Published: 3 July 2018

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Abstract

A systematic literature review of papers on big data in healthcare published between 2010 and 2015 was conducted. This paper reviews the definition, process, and use of big data in healthcare management. Unstructured data are growing very faster than semi-structured and structured data. 90 percentages of the big data are in a form of unstructured data, major steps of big data management in healthcare industry are data acquisition, storage of data, managing the data, analysis on data and data visualization. Recent researches targets on big data visualization tools. In this paper the authors analysed the effective tools used for visualization of big data and suggesting new visualization tools to manage the big data in healthcare industry. This article will be helpful to understand the processes and use of big data in healthcare management.

Published in	American Journal of Theoretical and Applied Business (Volume 4, Issue 2)
DOI	10.11648/j.ajtab.20180402.14
Page(s)	57-69
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Big Data, Data Acquisition, Data Storage, Data Analytics, Data Visualization, Healthcare Management

References

[1]	J. W. Cortada, D. Gordon, B. Lenihan, The value of analytics in healthcare: From insights to outcomes, IBM Global Business Services, Executive Report, 2012.
[2]	T. Huang, L. Lan, X. Fang, P. An, J. Min, F. Wang, Promises and Challenges of Big Data Computing in Health Sciences, Big Data Res. 2 (2015) 2–11. doi:10.1016/j. bdr.2015.02.002.
[3]	M. W. Stanton, Expanding patient-centered care to empower patients and assist providers, Research in Action. 5 (2002) 1-12.
[4]	K. Feldman, D. Davis, N. V. Chawla, Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration, J. Biomed. Inform. (2015) 1–9.
[5]	Global big data spending in the healthcare industry 2014-2019, Infiniti Research Limited. (Accessed on 2015, Sep 02). http://www.rnrmarketresearch.com/global-big-data-spending-in-healthcare-industry-2015-2019-market-report.html
[6]	O. Y. Al-Jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, K. Taha, Efficient Machine Learning for Big Data: A Review, Big Data Res. 2 (2015) 87–93. doi:10.1016/j. bdr.2015.04.001.
[7]	W. Raghupathi, V. Raghupathi, Big data analytics in healthcare: promise and potential, Heal. Inf. Sci. Syst. 2 (2014) 1–10. doi:10.1186/2047-2501-2-3.
[8]	D. I. Sessler, Big Data and its contributions to peri-operative medicine, Anaesthesia. 69 (2014) 100–105.
[9]	S. V. Nuti, B. Wayda, I. Ranasinghe, S. Wang, R. P. Dreyer, S. I. Chen, et al., The Use of Google Trends in Health Care Research: A Systematic Review, PLoS One. 9 (2014) e109583. doi:10.1371/journal. pone.0109583.
[10]	P. Groves, B. Kayyali, D. Knott, S. Van Kuiken, The “big data” revolution in healthcare: Accelerating value and innovation, McKinsey & Company, 2013. http://www.pharmatalents.es/assets/files/Big_Data_Revolution.pdf.
[11]	N. V. Chawla, D. A. Davis, Bringing big data to personalized healthcare: a patient-centered framework, J. General Internal Med. 28 (2013) 660-665. doi: 10.1007/s11606-013-2455-8.
[12]	U. Srinivasan, B. Arunasalam, Leveraging big data analytics to reduce healthcare costs, IT Professional, 15 (2013) 21-28.
[13]	A. D. Mauro, M. Greco, M. Grimaldi, What is big data? A consensual definition and a review of key research topics, 4th International Conference on Integrated Information, 2014.
[14]	A. Gandomi, M. Haider, Beyond the hype: big data concepts, methods, and analytics, Int. J. Inf. Manage. 35 (2015) 137–144.
[15]	J. D. Halamka, Early experiences with big data at an academic medical center, Health Aff. 33(2014) 1132–1138.
[16]	E. Baro, S. Degoul, R. Beuscart, E. Chazard, Toward a Literature-Driven Definition of Big Data in Healthcare, BioMed Research International. 2015 (2015) 1–9. doi: 10.1155/2015/639021.
[17]	IHTT: Transforming Health Care through Big Data Strategies for leveraging big data in the health care industry, 2013. http://ihealthtran.com/ wordpress/2013/03/iht%C2%B2-releases-big-data-research-report- download-today/.
[18]	R. Leventhal, Trend: big data. Big data analytics: from volume to value, Healthcare Inform., Bus. Mag. Inf. Commun. Syst. 30 (2013) 12-14.
[19]	Y. Wang, L. Kung, W. Y. C. Wang, C. G. Cegielski, Developing a Big Data-Enabled Transformation Model in Healthcare : A Practice Based View, in: Thirty Fifth Int. Conf. Inf. Syst., Auckland, 2014: pp. 1–12. doi:10.13140/2.1.2843.3601.
[20]	L. Wang, C. A. Alexander, Big Data in Medical Applications and Health Care, Am. Med. J. 6 (2015) 1–8. doi:10.3844/amjsp.2015.1.8.
[21]	X. Jin, B. W. Wah, X. Cheng, Y. Wang, Significance and Challenges of Big Data Research, Big Data Res. 2 (2015) 59–64. doi:10.1016/j. bdr.2015.01.006.
[22]	M. L. Berger and V. Doban, Big data, advanced analytics and the future of comparative effectiveness research, Journal of Comparative Effectiveness Research. 3 (2014) 167–176.
[23]	M. Sepulveda, Public health informatics and the public health workforce in an era of change, Am. J. Prev. Med. 47(2014) S386-S387.
[24]	B. Feldman, E. M. Martin, T. Skotnes, Big Data in Healthcare - Hype and Hope, Dr. Bonnie 360 degree (Business Development for Digital Health), 2012. http://www.riss.kr/link?id=A99883549.
[25]	G. Bello-Orgaz, J. J. Jung, D. Camacho, Social Big Data: Recent achievements and new challenges, Inf. Fusion. 000 (2015) 1–15. doi:10.1016/j. inffus.2015.08.005.
[26]	A. O’Driscoll, J. Daugelaite, R. D. Sleator, Bigdata, Hadoop and cloud computing in genomics, Journal of Biomedical Informatics. 46 (2013) 774–781.
[27]	N. M. S. Kumar, T. Eswari, P. Sampath, S. Lavanya, Predictive Methodology for Diabetic Data Analysis in Big Data, Procedia Comput. Sci. 50 (2015) 203–208. doi:10.1016/j. procs.2015.04.069.
[28]	S. Bonney, HIM's role in managing big data: turning data collected by an EHR into information, Journal of American Health Information Management Association. 84-9 (2013) 62–64.
[29]	J. D. van Horn, A. W. Toga, Human neuro imaging as a ‘Big Data’ science, Brain Imaging and Behaviour. 8 (2014) 323– 331.
[30]	M. Viceconti, P. Hunter, R. Hose, Big Data, Big Knowledge: Big Data for Personalized Healthcare, IEEE J. Biomed. Heal. Informatics. 19 (2015) 1209–1215. doi:10.1109/JBHI.2015.2406883.
[31]	G. O. Matheson, M. Klugl, L. Engebretsen, Prevention and management of non communicable disease: the IOC consensus statement, Clinical Journal of Sport Medicine. 23 (2013) 419–429.
[32]	K. D. Moore, K. Eyestone, D. C. Coddington, The big deal about big data, Healthcare Financial Management. 67 (2013) 60–68.
[33]	T. H. Davenport, D. J. Patil, Data scientist: the sexiest job of the 21st century, Harvard Business Review. 90 (2012) 70–128.
[34]	G. A. Ebenezer, S. Durga, Big Data Analytics in Healthcare: A Survey, ARPN J. Eng. Appl. Sci. 10 (2015) 3645–3650. doi:10.1155/2015/370194.
[35]	K. Jee and G. H. Kim, Potentiality of big data in the medical sector: Focus on how to reshape the healthcare system, Healthc. Inform. Res. 19 (2013) 79–85. doi:10.4258/hir.2013.19.2.79.
[36]	F. M. Afendi, N. Ono, Y. Nakamura et al., Data mining methods for omics and knowledge of crude medicinal plants toward big data biology, Computational and Structural Biotechnology Journal. 4 (2013) 1–14.
[37]	J. C. Ward, Oncology reimbursement in the era of personalized medicine and big data, Journal of Oncology Practice.10 (2014) 83–86.
[38]	O. S. Lupse, M. Crisan-Vida, L. Stoicu-Tivadar, E. Bernard, Supporting diagnosis and treatment in medical care based on big data processing, Studies in Health Technology and Informatics. 197 (2014) 65–69.
[39]	S. Kaisler, F. Armour, J. A. Espinosa, W. Money, Big Data: Issues and Challenges Moving Forward, IEEE Comput. Soc. 46th Hawaii Int. Conf. Syst. Sci. (2013) 995–1004. doi:10.1109/HICSS.2013.645.
[40]	R. Nambiar, R. Bhardwaj, A. Sethi, R. Vargheese, A look at challenges and opportunities of Big Data analytics in healthcare, Proc. - 2013 IEEE Int. Conf. Big Data, (2013) 17–22. doi:10.1109/BigData.2013.6691753.
[41]	H. Chen, S. S. Fuller, C. Friedman, W. Hersh, Medical Informatics: Knowledge Management and Data Mining in Biomedicine, Springer Science & Business Media. 8 (2006).
[42]	M. Stempniak, Beyond buzzwords: two state hospital associations collaborate around big data, Hosp. Health Netw. 88 (2014) 18.
[43]	PatientsLikeMe. [Internet] 2015. [Cited, October 9, 2015]. Available from: https://www.patientslikeme.com
[44]	A. Sadilek, H. Kautz, V. Silenzio, Modeling spread of disease from social interactions, Sixth AAAI International Conference on Weblogs and Social Media (ICWSM), 2012, http://www.cs.rochester.edu/~kautz/papers/Sadilek-Kautz-Silenzio_Modeling-Spread-of-Disease-from-Social-Interactions_ICWSM-12.pdf
[45]	P. Wicks, M. Massagli, J. Frost, C. Brownstein, S. Okun, T. Vaughan et al., Sharing health data for better outcomes on PatientsLikeMe, J Med Internet Res. 12 (2010)e19. doi:10.2196/jmir.1549.
[46]	A. van Heerden, S. Norris, S. Tollman, L. Richter, M. J. Rotheram-Borus, Collecting maternal health information from HIV positive pregnant women using mobile phone assisted face-to-face interviews in Southern Africa, J. Med. Internet Res. 15 (2013) e116.
[47]	S. Zhang, Q. Wu, M. H. van Velthoven, L. Chen, J. Car, I. Rudan, Y. Zhang, Y. Li, R. W. Scherpbier, Smartphone versus pen-and-paper data collection of infant feeding practices in rural China, J. Med. Internet Res. 14 (2012) e119.
[48]	M. Almalki, K. Gray, F. M. Sanchez, The use of self-quantification systems for personal health information: big data management activities and prospects, Heal. Inf. Sci. Syst. 3 (2015) 1–11. doi:10.1186/2047-2501-3-S1-S1.
[49]	I. A. T. Hashem, I. Yaqoob, N. Badrul Anuar, S. Mokhtar, A. Gani, S. Ullah Khan, The rise of “Big Data” on cloud computing: Review and open research issues, Inf. Syst. 47 (2014) 98–115. doi:10.1016/j. is.2014.07.006.
[50]	J. Archenaa, E. A. M. Anita, A Survey of Big Data Analytics in Healthcare and Government, Procedia Comput. Sci. 50 (2015) 408–413. doi:10.1016/j. procs.2015.04.021.
[51]	L. Z. Andrzej Chluski, The application of big data in the management of healthcare organizations: A review of selected practical solutions, Bus. Informatics. 1 (2015) 9–18. doi:10.15611/ie.2015.1.01.
[52]	L. Wang, R. Ranjan, J. Kołodziej, A. Zomaya, L. Alem, Software Tools and Techniques for Big Data Computing in Healthcare Clouds, Futur. Gener. Comput. Syst. 43 (2015) 38–39. doi:10.1016/j. future.2014.11.001.
[53]	U. S Government, Department of Health and Human Services, Fedral Register, Rules and Regulations, 74(2009) 56123-56131, Available from: https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/administrative/enforcementrule/enfir.pdf
[54]	Atchinson, Brian K., Fox, Daniel M. (May–June 1997), The Politics Of The Health Insurance Portability And Accountability Act, 16 (3): 146–15 doi: 10.1377/hlthaff.16.3.146.
[55]	Martin Wiesner, Daniel Pfeifer, Health Recommender Systems: Concepts, Requirements, Technical basics and Challenges, International Journal of Environmental Research and Public Health, 11(2014) 2580-2607. doi:10.3390/ijerph110302580.
[56]	J. Perrey, D. Spillecke, A. Umblijs, Smart analytics: How marketing drives short-term and long-term growth. McKinsey Quarterly (2013).
[57]	T. Schultz, Turning healthcare challenges into big data opportunities: A use-case review across the pharmaceutical development lifecycle, Bull. Association Inform. Sci. Technol. 39 (2013) 34-40. doi: 10.1002/bult.2013.1720390508.
[58]	Rohil Shah, Ria Echhpal, Sindhu Nair, Big Data in Healthcare Analytics, International journal on Recent and Innovation trends in Computing and Communication., 10(2015) 134-138.
[59]	M. M. Hansen, T. Miron-Shatz, a Y. S. Lau, C. Paton, Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group, Yearb. Med. Inform. 9 (2014) 21–6. doi:10.15265/IY-2014-0004.
[60]	S. Li, L. Kang, M Zhao X, A survey on evolutionary algorithm based hybrid intelligence in bioinformatics, BioMed Research International. 2014 (2014) 8. doi: 10.1155/2014/362738.362738.
[61]	Genomics and World Health: Report of the Advisory Committee on Health research, Geneva, WHO (2002), http://www.who.int/genomics/geneticsVSgenomics/en/.
[62]	A. Belle, R. Thiagarajan, S. M. R. Soroushmehr, F. Navidi, D. A. Beard, K. Najarian, Big Data Analytics in Healthcare, (n. d.). doi:10.1155/2015/370194.
[63]	E. M. van Allen, N. Wagle, M. A. Levy, Clinical analysis and interpretation of cancer genome data, Journal of Clinical Oncology. 31 (2013)1825–1833.
[64]	F. Andre, E. Mardis, M. Salm, J. C. Soria, L. L. Siu, C. Swanton, Prioritizing targets for precision cancer medicine, Annals of Oncology. 25 (2014) 2295–2303.
[65]	D. W. Huang, B. T. Sherman, R. A. Lempicki, Bioin- formatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists, Nucleic Acids Research. 37 (2009) 1-13.
[66]	Khatri, M. Sirota, A. J. Butte, Ten years of pathway analysis: current approaches and outstanding challenges, PLoS Computational Biology. 8 (2012) Article ID e1002375.
[67]	S. Draghici, P. Khatri, R. P. Martins, G. C. Ostermeier, S. A. Krawetz, Global functional profiling of gene expression, Genomics. 81 (2003) 98–104.
[68]	A. Subramanian, P. Tamayo, V. K. Mootha, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proceedings of the National Academy of Sciences of the United States of America. 102 (2005) 15545–15550.
[69]	S. Draghici, P. Khatri, A. L. Tarca, A systems biology approach for pathway level analysis, Genome Research. 17 (2007) 1537–1545.
[70]	H. K. Yalamanchili, Q. W. Xiao, J. Wang, A novel neural response algorithm for protein function prediction, BMC Syst. Biol. 6 (2012) S19.
[71]	M. R. Wilkins, et al., High-throughput mass spectrometric discovery of protein post-translational modifications, J. Mol. Biol. 289 (1999) 645–657.
[72]	J. Ren, et al., CSS-Palm 2.0: an updated software for palmitoylation sites pre- diction, Protein Eng. Des. Sel. 21 (2008) 639–644.
[73]	N. T. Issa, S. W. Byers, S. Dakshanamurthy, Big data: the next frontier for innovation in therapeutics and healthcare, Expert Rev. Clin. Pharmacol. 7 (2014) 293–298. doi:10.1586/17512433.2014.905201.
[74]	P. Bharal, A. Halfon, Making Sense of Big Data in Insurance, ACORD and MarkLogic, 2013.
[75]	A. G. Erdman, D. F. Keefe, Grand challenge: Applying regulatory science and big data to improve medical device innovation, IEEE Ttrans. Biomed. Eng. 60 (2013) 700-706. doi: 10.1109/TBME.2013.2244600.
[76]	R. Bellazzi, Big data and biomedical informatics: a challenging opportunity, Yearb. Med. Inform. 9 (2014) 8–13.

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Senthilkumar SA, Bharatendara K Rai, Amruta A Meshram, Angappa Gunasekaran, Chandrakumarmangalam S. (2018). Big Data in Healthcare Management: A Review of Literature. American Journal of Theoretical and Applied Business, 4(2), 57-69. https://doi.org/10.11648/j.ajtab.20180402.14

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ACS Style

Senthilkumar SA; Bharatendara K Rai; Amruta A Meshram; Angappa Gunasekaran; Chandrakumarmangalam S. Big Data in Healthcare Management: A Review of Literature. Am. J. Theor. Appl. Bus. 2018, 4(2), 57-69. doi: 10.11648/j.ajtab.20180402.14

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AMA Style

Senthilkumar SA, Bharatendara K Rai, Amruta A Meshram, Angappa Gunasekaran, Chandrakumarmangalam S. Big Data in Healthcare Management: A Review of Literature. Am J Theor Appl Bus. 2018;4(2):57-69. doi: 10.11648/j.ajtab.20180402.14

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@article{10.11648/j.ajtab.20180402.14,
  author = {Senthilkumar SA and Bharatendara K Rai and Amruta A Meshram and Angappa Gunasekaran and Chandrakumarmangalam S},
  title = {Big Data in Healthcare Management: A Review of Literature},
  journal = {American Journal of Theoretical and Applied Business},
  volume = {4},
  number = {2},
  pages = {57-69},
  doi = {10.11648/j.ajtab.20180402.14},
  url = {https://doi.org/10.11648/j.ajtab.20180402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtab.20180402.14},
  abstract = {A systematic literature review of papers on big data in healthcare published between 2010 and 2015 was conducted. This paper reviews the definition, process, and use of big data in healthcare management. Unstructured data are growing very faster than semi-structured and structured data. 90 percentages of the big data are in a form of unstructured data, major steps of big data management in healthcare industry are data acquisition, storage of data, managing the data, analysis on data and data visualization. Recent researches targets on big data visualization tools. In this paper the authors analysed the effective tools used for visualization of big data and suggesting new visualization tools to manage the big data in healthcare industry. This article will be helpful to understand the processes and use of big data in healthcare management.},
 year = {2018}
}

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TY  - JOUR
T1  - Big Data in Healthcare Management: A Review of Literature
AU  - Senthilkumar SA
AU  - Bharatendara K Rai
AU  - Amruta A Meshram
AU  - Angappa Gunasekaran
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VL  - 4
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Author Information

Senthilkumar SA

Department of Management, Pondicherry University, Pondicherry, India
Bharatendara K Rai

Charlton College of Business, University of Massachusetts Dartmouth, North Dartmouth, USA
Amruta A Meshram

Charlton College of Business, University of Massachusetts Dartmouth, North Dartmouth, USA
Angappa Gunasekaran

School of Business and Public Administration, California State University, Bakersfield, USA
Chandrakumarmangalam S

Department of Management Studies, Anna University Regional Campus, Coimbatore, India

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APA Style

Senthilkumar SA, Bharatendara K Rai, Amruta A Meshram, Angappa Gunasekaran, Chandrakumarmangalam S. (2018). Big Data in Healthcare Management: A Review of Literature. American Journal of Theoretical and Applied Business, 4(2), 57-69. https://doi.org/10.11648/j.ajtab.20180402.14

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ACS Style

Senthilkumar SA; Bharatendara K Rai; Amruta A Meshram; Angappa Gunasekaran; Chandrakumarmangalam S. Big Data in Healthcare Management: A Review of Literature. Am. J. Theor. Appl. Bus. 2018, 4(2), 57-69. doi: 10.11648/j.ajtab.20180402.14

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AMA Style

Senthilkumar SA, Bharatendara K Rai, Amruta A Meshram, Angappa Gunasekaran, Chandrakumarmangalam S. Big Data in Healthcare Management: A Review of Literature. Am J Theor Appl Bus. 2018;4(2):57-69. doi: 10.11648/j.ajtab.20180402.14

Copy | Download

@article{10.11648/j.ajtab.20180402.14,
  author = {Senthilkumar SA and Bharatendara K Rai and Amruta A Meshram and Angappa Gunasekaran and Chandrakumarmangalam S},
  title = {Big Data in Healthcare Management: A Review of Literature},
  journal = {American Journal of Theoretical and Applied Business},
  volume = {4},
  number = {2},
  pages = {57-69},
  doi = {10.11648/j.ajtab.20180402.14},
  url = {https://doi.org/10.11648/j.ajtab.20180402.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtab.20180402.14},
  abstract = {A systematic literature review of papers on big data in healthcare published between 2010 and 2015 was conducted. This paper reviews the definition, process, and use of big data in healthcare management. Unstructured data are growing very faster than semi-structured and structured data. 90 percentages of the big data are in a form of unstructured data, major steps of big data management in healthcare industry are data acquisition, storage of data, managing the data, analysis on data and data visualization. Recent researches targets on big data visualization tools. In this paper the authors analysed the effective tools used for visualization of big data and suggesting new visualization tools to manage the big data in healthcare industry. This article will be helpful to understand the processes and use of big data in healthcare management.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Big Data in Healthcare Management: A Review of Literature
AU  - Senthilkumar SA
AU  - Bharatendara K Rai
AU  - Amruta A Meshram
AU  - Angappa Gunasekaran
AU  - Chandrakumarmangalam S
Y1  - 2018/07/03
PY  - 2018
N1  - https://doi.org/10.11648/j.ajtab.20180402.14
DO  - 10.11648/j.ajtab.20180402.14
T2  - American Journal of Theoretical and Applied Business
JF  - American Journal of Theoretical and Applied Business
JO  - American Journal of Theoretical and Applied Business
SP  - 57
EP  - 69
PB  - Science Publishing Group
SN  - 2469-7842
UR  - https://doi.org/10.11648/j.ajtab.20180402.14
AB  - A systematic literature review of papers on big data in healthcare published between 2010 and 2015 was conducted. This paper reviews the definition, process, and use of big data in healthcare management. Unstructured data are growing very faster than semi-structured and structured data. 90 percentages of the big data are in a form of unstructured data, major steps of big data management in healthcare industry are data acquisition, storage of data, managing the data, analysis on data and data visualization. Recent researches targets on big data visualization tools. In this paper the authors analysed the effective tools used for visualization of big data and suggesting new visualization tools to manage the big data in healthcare industry. This article will be helpful to understand the processes and use of big data in healthcare management.
VL  - 4
IS  - 2
ER  -

Copy | Download