Big Data Analytics in Patient Care During Coronavirus Disease 2019 (COVID-19) Pandemic: A Systematic Review
DOI:
https://doi.org/10.18196/jmmr.v11i3.14657Keywords:
big data, application, patient care, covid, pandemicAbstract
The latest advancements in the field of computational techniques and big data analytics (BDA) are very helpful in dealing with the spread of coronavirus disease 2019 (COVID-19). The objective of this study was to systematically review BDA applications that have been implemented in patient care and the challenges during COVID-19 pandemic. We conducted a literature search in PubMed, Science Direct, Clinical Key, EBSCO, ProQuest, Cochrane Central, Wiley’s Library, and Springer to identify relevant articles published from 2020 to 2022. Studies were included if they reported BDA applications that had been implemented in real-life patient care during COVID-19 pandemic. We identified 14 relevant studies that reported the implementation and evaluation of BDA applications in patient care during COVID-19 pandemic, of which ten adopted an observational design and four adopted an experimental design. The BDA applications targeted various tasks, such as estimate or predict risk score (n=4), diagnosis (n=6), and healthcare decision making (n=4). Regarding the challenges faced, we identified four issues of BDA during COVID-19, such as data governance, economic challenges, big data technology challenges, and security and privacy issues. This review indicates applications of BDA have tremendous potential to bring a lot of advantages, especially in combatting COVID-19 pandemic, despite insufficient evidence to advocate the routine use of patient care BDA.References
Abdel-Basst, M., Mohamed, R., & Elhoseny, M. (2020). A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans. Health Informatics Journal, 26(4), 3088–3105. https://doi.org/10.1177/1460458220952918
Alsofayan, Y. M., Althunayyan, S. M., Khan, A. A., Hakawi, A. M., & Assiri, A. M. (2020). Clinical characteristics of COVID-19 in Saudi Arabia: A national retrospective study. Journal of Infection and Public Health, 13(7), 920–925. https://doi.org/10.1016/j.jiph.2020.05.026
Awan, M. J., Bilal, M. H., Yasin, A., Nobanee, H., Khan, N. S., & Zain, A. M. (2021). Detection of covid-19 in chest x-ray images: A big data enabled deep learning approach. International Journal of Environmental Research and Public Health, 18(19), 10147. https://doi.org/10.3390/ijerph181910147
Davis, R. (2020). Integrating Digital Technologies and Data-driven Telemedicine into Smart Healthcare during the COVID-19 Pandemic. American Journal of Medical Research, 7(2), 22–28.
Day, M. (2020). Covid-19: ibuprofen should not be used for managing symptoms, say doctors and scientists. BMJ (Clinical Research Ed.), 368(3), m1086. http://dx.doi.org/doi:10.1136/bmj.m1086
Drew, D. A., Nguyen, L. H., Steves, C. J., Wolf, J., Spector, T. D., & Chan, A. T. (2020). Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science, 368(6497), 1362–1367. https://doi.org/10.1101/2020.04.02.20051334
Fang, L., Karakiulakis, G., & Roth, M. (2020). Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? The Lancet Respiratory Medicine, 8(4), e21.
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497–506.
Izquierdo, Jose Luiz; Ancochea, Julio; Soriano, J. B. (2020). Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. Journal of Medical Internet Research, 22(10), e21801.
Jia, Q., Guo, Y., Wang, G., & Barnes, S. J. (2020). Big data analytics in the fight against major public health incidents (Including COVID-19): A conceptual framework. International Journal of Environmental Research and Public Health, 17(17), 1–21.
Kankanhalli, A., Hahn, J., Tan, S., & Gao, G. (2016). Big data and analytics in healthcare: Introduction to the special section. Information Systems Frontiers, 18(2), 233–235. https://doi.org/10.1007/s10796-016-9641-2
Khan, A. H., Sultana, M. S., Hossain, S., Hasan, M. T., Ahmed, H. U., & Sikder, M. T. (2020). The impact of COVID-19 pandemic on mental health & wellbeing among home-quarantined Bangladeshi students: A cross-sectional pilot study. Journal of Affective Disorders, 277(7), 121–128. https://doi.org/10.1016/j.jad.2020.07.135
Lan, F. Y., Filler, R., Mathew, S., Buley, J., Iliaki, E., Bruno-Murtha, L. A., Osgood, R., Christophi, C. A., Fernandez-Montero, A., & Kales, S. N. (2020). COVID-19 symptoms predictive of healthcare workers’ SARS-CoV-2 PCR results. PLoS ONE, 15(6), 1–12. https://doi.org/10.1371/journal.pone.0235460
Latif, S., Usman, M., Manzoor, S., Iqbal, W., Qadir, J., Tyson, G., Castro, I., Razi, A., Boulos, M. N. K., Weller, A., & Crowcroft, J. (2021). Leveraging Data Science to Combat COVID-19: A Comprehensive Review. IEEE Transactions on Artificial Intelligence, 1(1), 85–103.
Mackey, T., Purushothaman, V., Li, J., Shah, N., Nali, M., Bardier, C., Liang, B., Cai, M., & Cuomo, R. (2020). Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on Twitter: retrospective big data infoveillance study. JMIR Public Health and Surveillance, 6(2), 1–9. https://doi.org/10.2196/19509
Oussous, A., Benjelloun, F. Z., Ait Lahcen, A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University - Computer and Information Sciences, 30(4), 431–448. https://doi.org/10.1016/j.jksuci.2017.06.001
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ, 372(71), 1–9. https://doi.org/10.1136/bmj.n71
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 1–10. https://doi.org/10.1186/2047-2501-2-3
Sharp, A. L., Huang, B. Z., Broder, B., Smith, M., Yuen, G., Subject, C., Nau, C., Creekmur, B., Tartof, S., & Gould, M. K. (2021). Identifying patients with symptoms suspicious for COVID-19 at elevated risk of adverse events: The COVAS score. American Journal of Emergency Medicine, 46, 489–494.
Timmers, T., Janssen, L., Stohr, J., Murk, J. L., & Berrevoets, M. A. H. (2020). Using eHealth to support COVID-19 education, self-assessment, and symptom monitoring in the Netherlands: Observational study. JMIR MHealth and UHealth, 8(6), 1–13. https://doi.org/10.2196/19822
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology, 31(8), 6096–6104. https://doi.org/10.1007/s00330-021-07715-1
Weissman, G. E., Crane-Droesch, A., Chivers, C., Luong, T. B., Hanish, A., Levy, M. Z., Lubken, J., Becker, M., Draugelis, M. E., Anesi, G. L., Brennan, P. J., Christie, J. D., Hanson, C. W., Mikkelsen, M. E., & Halpern, S. D. (2020). Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic. Annals of Internal Medicine, 173(1), 21–28. https://doi.org/10.7326/M20-1260
WHO. (2020a). COVID-19 Public Health Emergency of International Concern (PHEIC) Global research and innovation forum. https://www.who.int/publications/m/item/covid-19-public-health-emergency-of-international-concern-(pheic)-global-research-and-innovation-forum
WHO. (2020b). WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020
WHO. (2021). Weekly epidemiological update on COVID-19 - 26 October 2021. https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---26-october-2021
Wong, ZSY; Zhou, J; Zhang, Q. (2019). Artificial Intelligence for infectious disease Big Data Analytics. Infection, Disease, & Health, 24(1), 44–48.
Wu, C., Chen, X., Cai, Y., Xia, J., Zhou, X., Xu, S., Huang, H., Zhang, L., Zhou, X., Du, C., Zhang, Y., Song, J., Wang, S., Chao, Y., Yang, Z., Xu, J., Zhou, X., Chen, D., Xiong, W., … Song, Y. (2020). Risk Factors Associated with Acute Respiratory Distress Syndrome and Death in Patients with Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Internal Medicine, 180(7), 934–943.
Xiong, S., Liu, L., Lin, F., Shi, J., Han, L., Liu, H., He, L., Jiang, Q., Wang, Z., Fu, W., Li, Z., Lu, Q., Chen, Z., & Ding, S. (2020). Clinical characteristics of 116 hospitalized patients with COVID-19 in Wuhan, China: a single-centered, retrospective, observational study. BMC Infectious Diseases, 20(787), 1–11. https://doi.org/10.1186/s12879-020-05452-2
Yang, X., Yu, Y., Xu, J., Shu, H., Xia, J., Liu, H., Wu, Y., Zhang, L., Yu, Z., Fang, M., Yu, T., Wang, Y., Pan, S., Zou, X., Yuan, S., & Shang, Y. (2020). Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine, 8(5), 475–481.
Zhang, T., Wang, Q., Leng, Z., Yang, Y., Yang, J., Chen, F., Jia, M., Zhang, X., Qi, W., Xu, Y., Chen, S., Dai, P., Ma, L., Feng, L., & Yang, W. (2021). A Scenario-Based Evaluation of COVID-19-Related Essential Clinical Resource Demands in China. Engineering, 7(7), 948–957. https://doi.org/10.1016/j.eng.2021.03.020
Downloads
Published
Issue
Section
License
Copyright (c) 2022 JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit)
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
