Big data analytics in healthcare and treatment of patients and health management
Keywords:
big data, big data analytics, healthAbstract
The implementation of Big Data Analytics (BDA) in the healthcare industry will make it possible to employ newly developed technology in the management of health as well as the treatment of individual patients. The purpose of this study is to conduct an investigation into the many ways in which Big Data Analytics may be used to the medical field. The research is predicated on an in-depth review of the existing body of literature, in addition to the presentation of a selection of the results obtained through actual research on the application of Big Data Analytics in clinical settings. The direct research was carried out in Poland on a sample size of 217 medical institutions using a research questionnaire as the primary data collection tool. Direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business, and clinical area, and studies of the literature have shown that the use of Big Data Analytics can bring many benefits to medical facilities. According to the findings of the research, medical institutions are actively working on both structured data and unstructured data at the same time.
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References
Abouelmehdi K, Beni-Hessane A, Khalouf H. Big healthcare data: preserving security and privacy. J Big Data. 2018. https://doi.org/10.1186/s40537-017-0110-7.
Agrawal A, Choudhary A. Health services data: big data analytics for deriving predictive healthcare insights. Health Serv Eval. 2019. https://doi.org/10.1007/978-1-4899-7673-4_2-1.
Al Mayahi S, Al-Badi A, Tarhini A. Exploring the potential benefts of big data analytics in providing smart healthcare. In: Miraz MH, Excell P, Ware A, Ali M, Soomro S, editors. Emerging technologies in computing—frst international conference, iCETiC 2018, proceedings (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). Cham: Springer; 2018. p. 247–58. https://doi.org/10.1007/978-3-319- 95450-9_21.
Bainbridge M. Big data challenges for clinical and precision medicine. In: Househ M, Kushniruk A, Borycki E, editors. Big data, big challenges: a healthcare perspective: background, issues, solutions and research directions. Cham: Springer; 2019. p. 17–31.
Bartuś K, Batko K, Lorek P. Business intelligence systems: barriers during implementation. In: Jabłoński M, editor. Strategic performance management new concept and contemporary trends. New York: Nova Science Publishers; 2017. p. 299–327. ISBN: 978-1-53612-681-5.
Bartuś K, Batko K, Lorek P. Diagnoza wykorzystania big data w organizacjach-wybrane wyniki badań. Informatyka Ekonomiczna. 2017;3(45):9–20.
Bartuś K, Batko K, Lorek P. Wykorzystanie rozwiązań business intelligence, competitive intelligence i big data w przedsiębiorstwach województwa śląskiego. Przegląd Organizacji. 2018;2:33–9.
Batko K. Możliwości wykorzystania Big Data w ochronie zdrowia. Roczniki Kolegium Analiz Ekonomicznych. 2016;42:267–82.
Bi Z, Cochran D. Big data analytics with applications. J Manag Anal. 2014;1(4):249–65. https://doi.org/10.1080/23270 012.2014.992985.
Boerma T, Requejo J, Victora CG, Amouzou A, Asha G, Agyepong I, Borghi J. Countdown to 2030: tracking progress towards universal coverage for reproductive, maternal, newborn, and child health. Lancet. 2018;391(10129):1538–48.
Bollier D, Firestone CM. The promise and peril of big data. Washington, D.C: Aspen Institute, Communications and Society Program; 2010. p. 1–66.
Bose R. Competitive intelligence process and tools for intelligence analysis. Ind Manag Data Syst. 2008;108(4):510–28.
Carter P. Big data analytics: future architectures, skills and roadmaps for the CIO: in white paper, IDC sponsored by SAS. 2011. p. 1–16.
Castro EM, Van Regenmortel T, Vanhaecht K, Sermeus W, Van Hecke A. Patient empowerment, patient participation and patient-centeredness in hospital care: a concept analysis based on a literature review. Patient Educ Couns. 2016;99(12):1923–39.
Gandamayu, I. B. M., Antari, N. W. S., & Strisanti, I. A. S. (2022). The level of community compliance in implementing health protocols to prevent the spread of COVID-19. International Journal of Health & Medical Sciences, 5(2), 177-182. https://doi.org/10.21744/ijhms.v5n2.1897
Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2022). Post-pandemic health and its sustainability: Educational situation. International Journal of Health Sciences, 6(1), i-v. https://doi.org/10.53730/ijhs.v6n1.5949
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