Análise de big data aplicada a serviços de saúde: uma revisão de literatura

Autores

DOI:

https://doi.org/10.5585/exactaep.2021.17297

Palavras-chave:

Análise de big data, Serviços de saúde, Análise de saúde, Transferência de tecnologia.

Resumo

O objetivo deste estudo é compreender os conceitos e a evolução da análise de big data aplicada aos serviços de saúde, considerando as atividades que envolvem o diagnóstico, tratamento e manejo do paciente. A revisão da literatura, consultando as bases de dados Science Direct, Scopus e Web of Science e empregando as palavras-chave health analytics e big data analytics sem restrições de tempo, encontrou trabalhos que abordam, especificamente, o uso de big data analytics no contexto da saúde, representados por exemplos e análises relacionadas. O tempo e a tomada de decisão aparecem como ações desenvolvidas tanto pela equipe de tecnologia da informação quanto pela equipe clínica, podendo considerar variáveis como custo, tempo, decisão e desempenho da estrutura funcional como os principais determinantes alinhados à estratégia corporativa. Este trabalho espera fomentar pesquisas sobre aspectos da saúde pública, além de considerar a preocupação com a sobrevivência das pessoas afetadas.

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Biografia do Autor

Myller Augusto Santos Gomes, Federal University of technology-Paraná - State university of mindwest

Ph.d student - Post-Graduate Program in Production Engineering (PPGEP), Federal University of Technology – Paraná (UTFPR), Campus Ponta Grossa, Paraná, Brazil. S/N Monteiro Lobato Av., Jardim Carvalho, Ponta Grossa, Postal code: 84016-210

Vander Luiz da Silva, Federal university of technology-Paraná - UTFPR

Msc. in Production Engineering, Federal University of Technology of Paraná. Undergraduate Degree in Production Engineering, State University of Paraná, Brazil.

João Luiz Kovaleski, Federal university of technology-Paraná - UTFPR

PhD in Industrial Instrumentation, University of Grenoble I. MSc in Industrial Informatics, Federal University of Technology of Paraná. MSc in Electronic Systems, Institut Polithnique de Grenoble. Undergraduate Degree in Electronic Industrial Engineering, Federal University of Technology of Paraná. Undergraduate Degree in Industrial Automation, University of Grenoble I, France.

Regina Negri Pagani, Federal university of technology-Paraná - UTFPR

PhD in Production Engineering, Federal University of Technology of Paraná, and Sorbonne Universités. MSc in Production Engineering, Federal University of Technology of Paraná. Specialist in Industrial Management, Federal University of Technology of Paraná. Undergraduate Degree in Business Administration, State University of Maringá, Brazil.

Referências

Alharthi, H. (2018). Healthcare predictive analytics: An overview with a focus on Saudi Arabia. Journal of infection and public health, 11(6), 749-756. https://doi.org/10.1016/j.jiph.2018.02.005.

Alkobaisi, S., Bae, W. D., Horak, M., Narayanappa, S., Lee, J., AbuKhousa, E., ... & Bae, D. J. (2019). Predictive and exposome analytics: A case study of asthma exacerbation management. Journal of Ambient Intelligence and Smart Environments, (Preprint), 1-26. https://doi.org/10.3233/AIS-190540

Abusharekh, A., Stewart, S. A., Hashemian, N., & Abidi, S. S. R. (2015, June). H-DRIVE: A Big Health Data Analytics Platform for Evidence-Informed Decision Making. In 2015 IEEE International Congress on Big Data (pp. 416-423). IEEE. https://doi.org/10.1109/BigDataCongress.2015.68

Balaji, S., Patil, M., & McGregor, C. (2017, June). A cloud based big data based online health analytics for rural nicus and picus in india: Opportunities and challenges. In 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 385-390). IEEE. https://doi.org/10.1109/CBMS.2017.112

Batarseh, F. A., & Latif, E. A. (2016). Assessing the quality of service using big data analytics: with application to healthcare. Big Data Research, 4, 13-24. https://doi.org/10.1016/j.bdr.2015.10.001

Balthazar, P., Harri, P., Prater, A., & Safdar, N. M. (2018). Protecting your patients’ interests in the era of big data, artificial intelligence, and predictive analytics. Journal of the American College of Radiology, 15(3), 580-586. https://doi.org/10.1016/j.jacr.2017.11.035

Blandford, A. (2019). HCI for health and wellbeing: Challenges and opportunities. International Journal of Human-Computer Studies, 131, 41-51. https://doi.org/10.1016/j.ijhcs.2019.06.007.

Cano, I., Tenyi, A., Vela, E., Miralles, F., & Roca, J. (2017). Perspectives on big data applications of health information. Current Opinion in Systems Biology, 3, 36-42. https://doi.org/10.1016/j.coisb.2017.04.012

Emani, C. K., Cullot, N., & Nicolle, C. (2015). Understandable big data: a survey. Computer science review, 17, 70-81. https://doi.org/10.1016/j.cosrev.2015.05.002

Galetsi, P., Katsaliaki, K., & Kumar, S. (2020). Big data analytics in health sector: Theoretical framework, techniques and prospects. International Journal of Information Management, 50, 206-216. https://doi.org/10.1016/j.ijinfomgt.2019.05.003

Galetsi, P., Katsaliaki, K., & Kumar, S. (2019). Values, challenges and future directions of big data analytics in healthcare: A systematic review. Social Science & Medicine, 241, 112533. https://doi.org/10.1016/j.socscimed.2019.112533

Galetsi, P., & Katsaliaki., & Kumar. (2019). Big Data Analytics in Health: an overview and bibliometric study of research activity. Health Information & Libraries Journal. https://doi.org/10.1111/hir.12286

Galetsi, P., & Katsaliaki., & Kumar. (2019). A review of the literature on big data analytics in healthcare. Journal of the Operational Research Society, 1-19. https://doi.org/10.1080/01605682.2019.1630328

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Gonzalez-Alonso, P., Vilar, R., & Lupiáñez-Villanueva, F. (2017, June). Meeting Technology and Methodology into Health Big Data Analytics Scenarios. In 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 284-285). IEEE. https://doi.org/10.1109/CBMS.2017.71

Harerimana, G., Jang, B., Kim, J. W., & Park, H. K. (2018). Health big data analytics: A technology survey. IEEE Access, 6, 65661-65678. https://doi.org/10.1109/ACCESS.2018.2878254

Kakhki, M. D., Singh, R., & Loyd, K. W. (2015). Developing Health Analytics Design Artifact for Improved Patient Activation: An On-going Case Study. In New Contributions in Information Systems and Technologies (pp. 733-739). Springer, Cham. https://doi.org/ 10.1007/978-3-319-16486-1_72

Kan, H., Nagar, S., Patel, J., Wallace, D. J., Molta, C., & Chang, D. J. (2016). Longitudinal treatment patterns and associated outcomes in patients with newly diagnosed systemic lupus erythematosus. Clinical Therapeutics, 38(3), 610-624. https://doi.org/10.1016/j.clinthera.2016.01.016

kBioAssist, S. (2017). CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health. Informatics Empowers Healthcare Transformation, 238, 19. http://dx.doi.org/10.3233/978-1-61499-781-8-19

Khennou, F., Khamlichi, Y. I., & Chaoui, N. E. H. (2018). Improving the Use of Big Data Analytics within Electronic Health Records: A Case Study based OpenEHR. Procedia Computer Science, 127, 60-68. https://doi.org/10.1016/j.procs.2018.01.098

Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation

McGregor, C., & Majola, P. X. (2019, June). Opportunities for a Cloud Based Health Analytics as a Service for Eastern Cape Initiation Schools in South Africa. In 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS) (pp. 531-534). IEEE. https://doi.org/10.1109/CBMS.2019.00108

Moutselos, K., Kyriazis, D., & Maglogiannis, I. (2018, July). A Web Based Modular Environment for Assisting Health Policy Making Utilizing Big Data Analytics. In 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-5). IEEE. https://doi.org/10.1109/IISA.2018.8633625

Moutselos, K., Kyriazis, D., Diamantopoulou, V., & Maglogiannis, I. (2018, December). Trustworthy data processing for health analytics tasks. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 3774-3779). IEEE. https://doi.org/10.1109/BigData.2018.8622449

Nambiar, R., Bhardwaj, R., Sethi, A., & Vargheese, R. (2013, October). A look at challenges and opportunities of big data analytics in healthcare. In 2013 IEEE international conference on Big Data (pp. 17-22). IEEE. https://doi.org/10.1109/BigData.2013.6691753

Nguyen, T., Larsen, M., O’Dea, B., Nguyen, H., Nguyen, D. T., Yearwood, J., ... & Christensen, H. (2018). Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2018.01.014

Oussous, A., Benjelloun, F. Z., Lahcen, A. 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

Pagani, R. N., Kovaleski, J. L., & Resende, L. M. (2015). Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, 105(3), 2109-2135. https://doi.org/10.1007/s11192-015-1744-x

Pashazadeh, A., & Navimipour, N. J. (2018). Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review. Journal of biomedical informatics, 82, 47-62. https://doi.org/10.1016/j.jbi.2018.03.014

Poornima, S., & Pushpalatha, M. (2020). A survey on various applications of prescriptive analytics. International Journal of Intelligent Networks, 1, 76-84. https://doi.org/10.1016/j.ijin.2020.07.001

Sabra, S., Malik, K. M., & Alobaidi, M. (2018). Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives. Computers in biology and medicine, 94, 1-10. https://doi.org/10.1016/j.compbiomed.2017.12.026

Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management, 54(5), 758-790. https://doi.org/10.1016/j.ipm.2018.01.010

Stephanie, L., & Sharma, R. S. (2020). Digital health eco-systems: An epochal review of practice-oriented research. International Journal of Information Management, 53, 102032. https://doi.org/10.1016/j.ijinfomgt.2019.10.017

Shafqat, S., Kishwer, S., Rasool, R. U., Qadir, J., Amjad, T., & Ahmad, H. F. (2020). Big data analytics enhanced healthcare systems: a review. The Journal of Supercomputing, 76(3), 1754-1799. https://doi.org/10.1007/s11227-017-2222-4

Suresh, S. (2016). Big data and predictive analytics: applications in the care of children. Pediatric Clinics, 63(2), 357-366. https://doi.org/10.1016/j.pcl.2015.12.007

Xu, Z. (2019). An empirical study of patients' privacy concerns for health informatics as a service. Technological Forecasting and Social Change, 143, 297-306. https://doi.org/10.1016/j.techfore.2019.01.018

Zhou, Y., Zhao, L., Zhou, N., Zhao, Y., Marino, S., Wang, T., ... & Dinov, I. D. (2019). Predictive Big Data Analytics using the UK Biobank Data. Scientific reports, 9(1), 6012. https://doi.org/10.1038/s41598-019-41634-y

Wlodarczak, P., Soar, P., & Ally, M. (2015). Behavioural health analytics using mobile phones. EAI Endorsed Trans. Scalable Information Systems, 2(5), e6. https://doi.org/10.4108/sis.2.5.e6

Wlodarczak, P., Soar, J., & Ally, M. (2015, May). Reality mining in eHealth. In International Conference on Health Information Science (pp. 1-6). Springer, Cham. https://doi.org/ 10.1007/978-3-319-19156-0_1

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Publicado

30.06.2022

Como Citar

Gomes, M. A. S., Silva, V. L. da, Kovaleski, J. L., & Pagani, R. N. (2022). Análise de big data aplicada a serviços de saúde: uma revisão de literatura. Exacta, 20(3), 647–665. https://doi.org/10.5585/exactaep.2021.17297

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