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Challenges and opportunities of big data analytics in healthcare
Abstract
Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.
References
1
Big data analytics in healthcare market. https://www.factmr.com/report/369/big-data-analytics-healthcare-market. Accessed March 2023.
2
Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J. 2018;39(16):1481–95. https://doi.org/10.1093/eurheartj/ehx487
3
Wang L, Alexander CA. Big data analytics in medical engineering and healthcare: methods, advances and challenges. J Med Eng Technol. 2020;44(6):267–83. https://doi.org/10.1080/03091902.2020.1769758
4
Koppad S, Annappa B, Gkoutos GV, Acharjee A. Cloud computing enabled big multi‐omics data analytics. Bioinform Biol Insights. 2021;15:117793222110359. https://doi.org/10.1177/11779322211035921
5
Morris MA, Saboury B, Burkett B, Gao J, Siegel EL. Reinventing radiology: big data and the future of medical imaging. J Thorac Imaging. 2018;33(1):4–16. https://doi.org/10.1097/RTI.0000000000000311
6
Dhir A, Talwar S, Kaur P, Malibari A. Food waste in hospitality and food services: a systematic literature review and framework development approach. J Clean Prod. 2020;270:122861. https://doi.org/10.1016/j.jclepro.2020.122861
7
Amirian P, van Loggerenberg F, Lang T, Thomas A, Peeling R, Basiri A, et al. Using big data analytics to extract disease surveillance information from point of care diagnostic machines. Pervasive Mobile Computing. 2017;42:470–86. https://doi.org/10.1016/j.pmcj.2017.06.013
8
De Silva D, Burstein F, Jelinek HF, Stranieri A. Addressing the complexities of big data analytics in healthcare: the diabetes screening case. Australasian J Inform Sys. 2015;19:S99–S115. https://doi.org/10.3127/ajis.v19i0.1183
9
Austin C, Kusumoto F. The application of big data in medicine: current implications and future directions. J Interv Card Electrophysiol. 2016;47:51–9. https://doi.org/10.1007/s10840-016-0104-y
10
Babar MI, Jehanzeb M, Ghazali M, Jawawi DN, Sher F, Ghayyur SAK. Big data survey in healthcare and a proposal for intelligent data diagnosis framework. In 2nd IEEE International Conference on Computer and Communications (ICCC). 2016, 7–12. https://doi.org/10.1109/CompComm.2016.7924654
11
Ozminkowski RJ, Wells TS, Hawkins K, Bhattarai GR, Martel CW, Yeh CS. Big data, little data, and care coordination for medicare beneficiaries with medigap coverage. Big Data. 2015;3(2):114–25. https://doi.org/10.1089/big.2014.0034
12
Zaragoza MG, Kim HK, Chung Y. U‐healthcare big data analytics process control. Int J Control Automation. 2017;10(11):165–74. https://doi.org/10.14257/ijca.2017.10.11.15
13
Gopal G, Suter‐Crazzolara C, Toldo L, Eberhardt W. Digital transformation in healthcare–architectures of present and future information technologies. Clin Chem Lab Med (CCLM). 2019;57(3):328–35. https://doi.org/10.1515/cclm-2018-0658
14
Ma X, Wang Z, Zhou S, Wen H, Zhang Y. Intelligent healthcare systems assisted by data analytics and mobile computing Limassol, Cyprus. 14th International Wireless Communications and Mobile Computing Conference (IWCMC), Limassol, Cyprus. 2018. 1317–22. https://doi.org/10.1109/IWCMC.2018.8450377
15
Bravo J, Hervás R, Fontecha J, González I. m‐Health: lessons learned by M‐experiences. Sensors. 2018;18(5):1569. https://doi.org/10.3390/s18051569
16
George M, Chacko A, Kurien SK. Proactive diabetes management: research directions. In Proceedings of the 20th International Conference on Distributed Computing and Networking, Bangalore, India. 2019, 486–91. https://doi.org/10.1145/3288599.3297119
17
Navaz AN, Serhani MA, Al‐Qirim N, Gergely M. Towards an efficient and energy‐aware mobile big health data architecture. Comput Methods Prog Biomed. 2018;166:137–54. https://doi.org/10.1016/j.cmpb.2018.10.008
18
Chen M, Hao Y, Hwang K, Wang L, Wang L. Disease prediction by machine learning over big data from healthcare communities. IEEE Access. 2017;5:8869–79. https://doi.org/10.1109/ACCESS.2017.2694446
19
Hadi MS, Lawey AQ, El‐Gorashi TEH, Elmirghani JMH. Patient‐centric cellular networks optimization using big data analytics. IEEE Access. 2019;7:49279–96. https://doi.org/10.1109/ACCESS.2019.2910224
20
Koliogeorgi K, Masouros D, Zervakis G, Xydis S, Becker T, Gaydadjiev G, et al. AEGLE's cloud infrastructure for resource monitoring and containerized accelerated analytics. In Computer Society Annual Symposium on VLSI, Bochum, Germany. 2017, 362–7. https://doi.org/10.1109/ISVLSI.2017.70
21
Patil HK, Seshadri R. Big data security and privacy issues in healthcare. In Proceedings of 2014 IEEE International Congress on Big Data, Anchorage, Alaska. 2014, 762–5. https://doi.org/10.1109/BigData.Congress.2014.112
22
Cheng CH, Kuo YH, Zhou Z. Tracking nosocomial diseases at individual level with a real‐time indoor positioning system. J Med Syst. 2018;42(11):222. https://doi.org/10.1007/s10916-018-1085-4
23
Sabharwal S, Gupta S, Thirunavukkarasu K. Insight of big data analytics in healthcare industry. In International Conference on Computing, Communication and Automation, Noida, India. 2016, 95–100. https://doi.org/10.1109/CCAA.2016.7813696
24
Gowsalya M, Krushitha K, Valliyammai C. Predicting the risk of readmission of diabetic patients using mapreduce. In 6th International Conference on Advanced Computing, Chennai, India. 2014, 297–301. https://doi.org/10.1109/ICoAC.2014.7229729
25
Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C. A new architecture of Internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gen Comp Systems. 2018;82:375–87. https://doi.org/10.1016/j.future.2017.10.045
26
Chehade A, Liu K. Structural degradation modeling framework for sparse data sets with an application on Alzheimer's disease. IEEE Trans Automation Sci Eng. 2019;16(1):192–205. https://doi.org/10.1109/TASE.2018.2829770
27
Kuo MH, Chrimes D, Moa B, Hu W. Design and construction of a big data analytics framework for health applications. In International Conference on Smart City/SocialCom/SustainCom, Chengdu, China. 2015, 631–6. https://doi.org/10.1109/SmartCity.2015.140
28
Li H, Wu J, Liu L, Li Q. Adoption of big data analytics in healthcare: the efficiency and privacy. In Proceedings of 19th Pacific Asia Conference on Information Systems. 2015, 181. https://aisel.aisnet.org/pacis2015/181
29
Wu J, Li H, Cheng S, Lin Z. The promising future of healthcare services: when big data analytics meets wearable technology. Inform Management. 2016;53(8):1020–33. https://doi.org/10.1016/j.im.2016.07.003
30
Agnihothri S, Banerjee A, Thalacker G. Analytics to improve service in a pre‐admission testing clinic. In 48th Hawaii International Conference on System Sciences, Kauai, HI, USA. 2015, 1325–31. https://doi.org/10.1109/HICSS.2015.162
31
Jindal A, Dua A, Kumar N, Das AK, Vasilakos AV, Rodrigues JJPC. Providing healthcare‐as‐a‐service using fuzzy rule based big data analytics in cloud computing. IEEE J Biomed Health Informatics. 2018;22(5):1605–18. https://doi.org/10.1109/JBHI.2018.2799198
32
Narayanan A, Greco M. Patient experience of Australian general practices. Big Data. 2016;4(1):31–46. https://doi.org/10.1089/big.2016.0010
33
Lin YK, Chen H, Brown RA, Li SH, Yang HJ. Healthcare predictive analytics for risk profiling in chronic care: a Bayesian multitask learning approach. MIS Quarterly. 2017;41:473–95. https://doi.org/10.25300/MISQ/2017/41.2.07
34
Boudhir AA, Ben Ahmed M, Soumaya F. Big data architecture for decision making in protocols and medications assignment. In Proceedings of the Mediterranean Symposium on Smart City Application, Tangier, Morocco. Vol. 5. 2017. p. 1–4.
35
Moreira MWL, Rodrigues JJPC, Kumar N, Al‐Muhtadi J, Korotaev V. Evolutionary radial basis function network for gestational diabetes data analytics. J Computational Sci. 2018;27:410–7. https://doi.org/10.1016/j.jocs.2017.07.015
36
Christensen JH, Petersen MK, Pontoppidan NH, Cremonini M. Big data analytics in healthcare: design and implementation for a hearing aid case study. In 14th International Conference on Signal‐Image Technology & Internet‐Based Systems, Las Palmas de Gran Canaria, Spain. 2018, 296–303. https://doi.org/10.1109/SITIS.2018.00052
37
Wang Y, Kung L, Wang WYC, Cegielski CG. An integrated big data analytics‐enabled transformation model: application to health care. Inform Management. 2018;55(1):64–79. https://doi.org/10.1016/j.im.2017.04.001
38
Tseng VS, Chou CH, Yang KQ, Tseng JC. A big data analytical framework for sports behavior mining and personalized health services. In Conference on Technologies and Applications of Artificial Intelligence, Taipei, Taiwan. 2017, 178–83. https://doi.org/10.1109/TAAI.2017.47
39
Wang Y, Kung L, Byrd TA. Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol Forecase Soc. Change. 2018;126:3–13. https://doi.org/10.1016/j.techfore.2015.12.019
40
Jin Q, Wu B, Nishimura S, Ogihara A. Ubi‐Liven: a human‐centric safe and secure framework of ubiquitous living environments for the elderly. In 2016 International Conference on Advanced Cloud and Big Data (CBD), Chengdu, China. 2016, 304–9. https://doi.org/10.1109/CBD.2016.059
41
Shao Y, Wang K, Shu L, Deng S, Deng DJ. Heuristic optimization for reliable data congestion analytics in crowdsourced eHealth networks. IEEE Access. 2016;4:9174–83. https://doi.org/10.1109/ACCESS.2016.2646058
42
Ventola CL. Big data and pharmacovigilance: data mining for adverse drug events and interactions. P & T: J Formulary Management. 2018;43(6):340–51.
43
Bihan K, Lebrun‐Vignes B, Funck‐Brentano C, Salem JE. Uses of pharmacovigilance databases: an overview. Therapies. 2020;75(6):591–8. https://doi.org/10.1016/j.therap.2020.02.022
44
Zhou X, Chen S, Liu B, Zhang R, Wang Y, Li P, et al. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif Intell Med. 2010;48(2–3):139–52. https://doi.org/10.1016/j.artmed.2009.07.012
45
Yang JJ, Li J, Mulder J, Wang Y, Chen S, Wu H, et al. Emerging information technologies for enhanced healthcare. Comp Industry. 2015;69:3–11. https://doi.org/10.1016/j.compind.2015.01.012
46
Cai T, Giannopoulos AA, Yu S, Kelil T, Ripley B, Kumamaru KK, et al. Natural language processing technologies in radiology research and clinical applications. Radiographics. 2016;36(1):176–91. https://doi.org/10.1148/rg.2016150080
47
Mohammed N, Fung BCM, Hung PCK, Lee CK. Centralized and distributed anonymization for high‐dimensional healthcare data. ACM Transac Knowledge Dis Data. 2010;4(4):1–33. https://doi.org/10.1145/1857947.1857950
48
Chong SA, Abdin E, Vaingankar JA, Heng D, Sherbourne C, Yap M, et al. A population‐based survey of mental disorders in Singapore. Ann Acad Med Singapore. 2012;41(2):49–66. https://doi.org/10.47102/annals-acadmedsg.V41N2p49
49
Panagiotakopoulos TC, Lyras DP, Livaditis M, Sgarbas KN, Anastassopoulos GC, Lymberopoulos DK. A contextual data mining approach toward assisting the treatment of anxiety disorders. IEEE Trans Inf Technol Biomed. 2010;14(3):567–81. https://doi.org/10.1109/TITB.2009.2038905
50
Kostkova P, Fowler D, Wiseman S, Weinberg JR. Major infection events over 5 years: how is media coverage influencing online information needs of health care professionals and the public? J Med Internet Res. 2013;15(7):e107. https://doi.org/10.2196/jmir.2146
51
Harpaz R, Vilar S, DuMouchel W, Salmasian H, Haerian K, Shah NH, et al. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J Am Med Inform Assoc. 2013;20(3):413–9. https://doi.org/10.1136/amiajnl-2012-000930
52
Harpaz R, Chase HS, Friedman C. Mining multi‐item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics. 2010;11:S7. https://doi.org/10.1186/1471-2105-11-S9-S7
53
Panda M, Ali SM, Panda SK. Big data in health care: a mobile based solution. In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). 2017, 149–52. https://doi.org/10.1109/ICBDACI.2017.8070826
54
Helm‐Murtagh SC. Use of big data by blue cross and blue shield of North Carolina. N C Med J. 2014;75(3):195–7. https://doi.org/10.18043/ncm.75.3.195
55
Wu X, Zhu X, Wu GQ, Ding W. Data mining with big data. IEEE Transac Knowledge Data Eng. 2013;26(1):97–107. http://doi.org/10.1109/TKDE.2013.109
56
Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP. Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology. Nat Rev Genet. 2011;12(3):224. https://doi.org/10.1038/nrg2857-c2
57
Marx V. The big challenges of big data. Nature. 2013;498(7453):255–60. https://doi.org/10.1038/498255a
58
Swarup V, Geschwind DH. From big data to mechanism. Nature. 2013;500(7460):34–5. https://doi.org/10.1038/nature12457
59
Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, Canese K, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2010;38:D5–D16. https://doi.org/10.1093/nar/gkp967
60
Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37:W623–33. https://doi.org/10.1093/nar/gkp456
61
Sayers EW, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2009;37:D5–D15. https://doi.org/10.1093/nar/gkn741
62
Sayers EW, Agarwala R, Bolton EE, Brister JR, Canese K, Clark K, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2019;47:D23–8. https://doi.org/10.1093/nar/gky1069
63
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: a large‐scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40:D1100–7. https://doi.org/10.1093/nar/gkr777
64
Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–82. https://doi.org/10.1093/nar/gkx1037
65
Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44:D1045–53. https://doi.org/10.1093/nar/gkv1072
66
Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, et al. A view of cloud computing. Commun ACM. 2010;53(4):50–8. https://doi.org/10.1145/1721654.1721672
67
Nickolls J, Dally WJ. The GPU computing era. IEEE micro. 2010;30(2):56–69. https://doi.org/10.1109/MM.2010.41
68
Malaka I, Brown I. Challenges to the organisational adoption of big data analytics: a case study in the South African telecommunications industry. In Proceedings of the 2015 Annual Research Conference on South African Institute of Computer Scientists and Information Technologists. 2015, 1–9. https://doi.org/10.1145/2815782.2815793
69
Moktadir MA, Ali SM, Paul SK, Shukla N. Barriers to big data analytics in manufacturing supply chains: a case study from Bangladesh. Comput Ind Eng. 2019;128:1063–75. https://doi.org/10.1016/j.cie.2018.04.013
70
Belle A, Thiagarajan R, Soroushmehr SMR, Navidi F, Beard DA, Najarian K. Big Data Analytics Inhealthcare, Hindawi Publishing Corporation; 2015. p. 1–16. https://doi.org/10.1155/2015/370194
71
Alaboudi A, Atkins A, Sharp B, Balkhair A, Alzahrani M, Sunbul T. Barriers and challenges in adopting Saudi telemedicine network: the perceptions of decision makers of healthcare facilities in Saudi Arabia. J Infection Public Health. 2016;9(6):725–33. https://doi.org/10.1016/j.jiph.2016.09.001
72
Busagala LS, Kawono GC. Perceptions and adoption of information and communication technology for healthcare services in Tanzania. Int J Comp ICT Res. 2013;7(1):12–21.
73
Kavitha R, Kannan E, Kotteswaran S. Implementation of cloud based electronic health record (EHR) for Indian healthcare needs. Indian J Sci Technol. 2016;9(3):1–5. https://doi.org/10.17485/ijst/2016/v9i3/86391
74
Luna DR, Mayan JC, García MJ, Almerares AA, Househ M. Challenges and potential solutions for big data implementations in developing countries. Yearb Med Inform. 2014;23(1):36–41. https://doi.org/10.15265/IY-2014-0012
75
Purkayastha S, Braa J. Big data analytics for developing countries—using the cloud for operational BI in health. Electron J Inf Syst Dev Ctries. 2013;59(1):1–17. https://doi.org/10.1002/j.1681-4835.2013.tb00420.x
Pages 328-338
DOI: 10.1002/hcs2.66
Cite this article:
Goyal P, Malviya R. Challenges and opportunities of big data analytics in healthcare. Health Care Science, 2023, 2(5): 328-338. https://doi.org/10.1002/hcs2.66
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