Big Data in the Brazilian public health sector

concept, characteristics, benefits, and challenges

Autores

Palavras-chave:

Big Data, public health sector, e-government Brazil

Resumo

Big Data (BD) has arisen as a strategic way to manage and process varied and elevated volume of information that is published these days. In this context, the purpose of this article is to investigate the adoption of Big Data (BD) for the management of financial resources related to Brazilian public health services and activities, in the perception of the actors involved. Using the multimethod approach, we propose a concept of BD in the public health sector; characterize the adoption of BD in an organization within this sector with attributes such as volume, velocity, variety, value, veracity, variability and visualization; and finally, identify categories and variables in the literature which are related to the challenges and benefits that are present and absent in this phenomenon, as well as new variables related to managerial benefits: sustainability and leadership support. The results offer an opportunity to understand the use of BD in the Brazilian public health sector, especially for the efficient management of the resources used to create collective value in public health.

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Biografia do Autor

Dayse Karenine de Oliveira Carneiro, MSc., University of Brasília

Possui graduação em Administração pela Universidade Potiguar (2000) e em Ciências Econômicas pela Universidade Federal do Rio Grande do Norte (2006). Mestrado Profissional em Administração Pública pela Universidade de Brasília (2016). Doutorado em andamento em Administração pela Universidade de Brasília.Tem experiência em gestão de serviços, com ênfase em administração da qualidade, planejamento estratégico, balanced scorecard, gerenciamento de projetos, orçamento e finanças públicas, compras governamentais e inovação no setor público.

Gislayne da Silva Goulart, Prof., Federal University of Mato Grosso do Sul

Professora da Universidade Federal de Mato Grosso do Sul (UFMS). Doutora em Administração pela Universidade de Brasília (UnB). Mestra e Bacharel em Administração pela UFMS.

Rafael Barreiros Porto, Prof., University of Brasília

Rafael Barreiros Porto. Concluiu seu doutorado em Ciências do Comportamento pela Universidade de Brasília e é pós doutor em Economia Comportamental aplicada ao Marketing pela Cardiff Business School (UK). É professor Associado no Departamento de Administração da Universidade de Brasília e do Programa de Pós-Graduação em Administração da UnB (PPGA/UnB). É atual coordenador do Programa de Pós-Graduação em Administração da UnB. Foi ex coordenador da linha de pesquisa Estratégia, Marketing e Inovação neste programa e membro de comitê de avaliação de Programas de Pós-Graduação em Administração, Contabilidade e Turismo, Qualis Periódico e Qualis Livro da Capes. Atualmente é editor chefe da Revista Contabilidade, Gestão e Governança (Qualis B1) e líder do grupo Experimenta - pesquisa em desempenho de estratégia e marketing. Concluiu 4 orientações de doutorado e 14 de mestrado acadêmico. Atua na área de Administração, com ênfase em marketing e estratégia. Temas de interesse: economia comportamental aplicada ao marketing e suas consequências empresariais; modelagem econométrica e experimental em marketing; dinâmica do desempenho financeiro empresarial; influência das atividades de marketing na compra e escolha de marcas; branding & brand equity.

Referências

Abu-Shanab, E. A. (2020). E-government contribution to better performance by public sector. In:

Open Government: Concepts, Methodologies, Tools, and Applications. IGI Global, 2020. p. 1-17. DOI:

4018/978-1-5225-9860-2.ch001

Akhavan-Hejazi, H.; Mohsenian-Rad, H. (2018). Power systems big data analytics: An assessment of

paradigm shift barriers and prospects. Energy Reports, v. 4, p. 91-100. DOI: https://doi.org/10.1016/j.

egyr.2017.11.002

Archenaa, J.; Anita, E. M. (2015). A survey of Big Data analytics in healthcare and government.

Procedia Computer Science, v. 50, p. 408-413. DOI: https://doi.org/10.1016/j.procs.2015.04.02 1https://

doi.org/10.1016/j.procs.2015.04.021

Arnold-Schmitt, L. E.; Triska, R. (2014). Informação na área da saúde em tempos de comunicação

móvel, Big Data e computação cognitiva. Razón y palabra, v. 18, n. 88.

Bardin, L. (2011). Análise de conteúdo. 3 reimp. Lisboa: Edições, 70.

Blazquez, D.; Domench, J. (2018). Big Data sources and methods for social and economic analyses.

Technological Forecasting and Social Change, v. 130, p. 99-113. DOI: https://doi.org/10.1016/j.

techfore.2017.07.027.

Brasil. (1988). Constituição da República Federativa do Brasil. Brasil. Disponível em:

planalto.gov.br/ccivil_03/Constituicao/Constituicao.htm>. Acesso em: 8 abr. 2021.

Brasil. (2018). Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados. Disponível em:

<>. Acesso em: 11 abr. 2021.

Bryman, A. (2003). Quantity and quality in social research. Routledge.

Chandra, Y.; Shang, L. (2017). An RQDA-based constructivist methodology for qualitative research.

Qualitative Market Research, v. 20. n. 1, p. 90-112. DOI: https://doi.org/10.1108/QMR-02-2016-0014

Chen, C. L. P.; Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques, and technologies:

A survey on Big Data. Information Sciences, v. 275, p. 314-347. DOI: 10.1016/j.ins.2014.01.015

Chen, M.; Mao, S.; Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, v. 19, n. 2, p.

–209. DOI: 10.1007/s11036-013-0489-0

Criado, J. I.; Gil-Garcia, J. R. (2019). Creating public value through smart technologies and strategies.

International Journal of Public Sector Management. DOI: https://doi.org/10.1108/IJPSM-07-2019-0178

Dash, S. et al. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big

Data, v. 6, n. 54. DOI: https://doi.org/10.1186/s40537-019-0217-0

De Vries, H.; Bekkers, V.; Tummers, L. (2016). Innovation in the public sector: A systematic review

and future research agenda. Public administration, v. 94, n. 1, p. 146-166. DOI: https://doi.org/10.1111/

padm.12209

Dias, G. P. (2019). Fifteen years of e-government research in Ibero-America: A bibliometric

analysis. Government Information Quarterly, v. 36, n. 3, p. 400-411. DOI: https://doi.org/10.1016/j.

giq.2019.05.008

Fredriksson, C. (2017). et al. Big data in the public sector: A systematic literature review. Scandinavian

Journal of Public Administration, v. 21, n. 3, p. 39-62.

Fundo Nacional de Saúde. (s.d.). Arquivos de Repasse Anual Fundo a Fundo: Repasse FAF com

População – 2002 a 2018. Disponível em: < https://portalfns.saude.gov.br/publicacoes/. Acesso em: 11

abr. 2019.

Giacalone, M.; Cusatelli, C.; Santarcangelo, V. (2018). Big Data Compliance for Innovative

Clinical Models. Big Data Research. DOI: https://doi.org/10.1016/j.BDr.2018.02.001

Gil-Garcia, J. R.; Flores-Zúñiga, M. Á. (2020). Towards a comprehensive understanding of digital

government success: Integrating implementation and adoption factors. Government Information

Quarterly, v. 37, n. 4, p. 101518. DOI: https://doi.org/10.1016/j.giq.2020.101518

Goulart, G. da S.; Viana, M. M. & Lucchese-Cheung, T. (2021). Consumer perception towards

familiar and innovative foods: the case of a Brazilian product. British Food Journal, v. 123, n. 1, p. 125-

DOI: https://doi.org/10.1108/BFJ-02-2020-0160

Günther, W. A. et al. (2017). Debating Big Data: a literature review on realizing value from Big

Data. Journal of Strategic Information Systems, v. 26, p. 191–209. DOI: http://dx.doi.org/10.1016/j.

jsis.2017.07.003

Hashem, I. A. T. et al. (2015). The rise of “Big Data” on cloud computing: review and open research

issues. Inf. Syst., v. 47, p. 98-115. DOI: https://doi.org/10.1016/j.is.2014.07.006

Hadi, M. S. et al. (2018). Big Data analytics for wireless and wired network design: A survey. Computer

Networks, v. 132, p. 180-199. DOI: https://doi.org/10.1016/j.comnet.2018.01.016

IQBAL, M. et al. (2018). A study of big data for business growth in SMEs: Opportunities & challenges. In:

International Conference on Computing, Mathematics and Engineering Technologies (iCoMET).

IEEE. p. 1-7. DOI: 10.1109/ICOMET.2018.8346368.

Janssen, M. & Van Den Hoven, J. (2015). Big and Open Linked Data (BOLD) in government: A

challenge to transparency and privacy?. DOI: https://doi.org/10.1016/j.giq.2015.11.007

Justo, a.m., & camargo, b.v. (2014). Estudos qualitativos e o uso de softwares para análises lexicais. In

Caderno de artigos: X SIAT & II Serpro (p. 37–54). UNIGRANRIO.DOI: <https://lageres.wordpress.

com/>

Kim, G-H, Trimi, S. & Chung, J-H. (2014). Big-Data Applications in the Government Sector.

Communications of the ACM, v. 57, n. 3, p. 78-85. DOI: 10.1145/2500873

Kulkarni, A. J. et al. (Ed.). (2020). Big Data Analytics in Healthcare. Springer, Cham. DOI: https://doi.

org/10.1007/978-3-030-31672-3

Kumar, Y. et al. (2020). Big Data analytics and its benefits in healthcare. In KULKARNI, A. J. et al. Big

Data Analytics in Healthcare. Springer, Cham, p. 3-21. DOI: https://doi.org/10.1007/978-3-030-31672-

_1

Löfgren, Karl, Webster, C. William R. (2020). The value of Big Data in government: The case of

‘smart cities’. Big Data & Society, v. 7, n. 1, p. 2053951720912775.

Marchand, P.& Ratinaud, P. (2012). L'analyse de similitude appliqueé aux corpus textueles: les

primaires socialistes pour l'election présidentielle française. In: Actes des 11eme Journées internationales

d’Analyse statistique des Données Textuelles. JADT, p. 687-699. Presented at the 11eme Journées

internationales d’Analyse statistique des Données Textuelles, Liège, Belgique.

Matheus, R., Janssen, M.& Janowski, T. (2021). Design principles for creating digital transparency in

government. Government Information Quarterly, v. 38, n. 1, p. 101550. DOI: https://doi.org/10.1016/j.

giq.2020.101550

Mcafee, A., Brynjolfsson, E. (2012). Big Data: the management revolution. Harvard Business

Review, 1 ed.

Mehta, N.; Pandit, A. (2018). Concurrence of Big Data analytics and healthcare: a systematic

review. International Journal of Medical Informatics, v. 114, p. 57-65. DOI: https://doi.org/10.1016/j.

ijmedinf.2018.03.013

Morabito, V. (2015). Big data and analytics for government innovation. In: Big data and analytics.

Springer, Cham, p. 23-45. DOI: https://doi.org/10.1007/978-3-319-10665-6–2

Paim, J. S. (2018). Sistema Único de Saúde (SUS) aos 30 anos. Ciência & Saúde Coletiva, v. 23, n. 6, p.

-1728. DOI: http://dx.doi.org/10.1590/1413-81232018236.09172018

ratinaud, p. (2014). IRAMUTEQ - Interface de R pour les Analyses Multidimensionnelles de Textes et

de Questionnaires (Versão 0.7 alpha 2). [Computer software]. DOI: http://www.iramuteq.org

Rumbold, J. M. & Pierscionek, B. K. (2017). What Are Data? A Categorization of the Data Sensitivity

Spectrum. Big Data Research. DOI: https://doi.org/10.1016/j.BDr.2017.11.001

Saggi, M. K. & Jain, S. (2018). A survey towards an integration of Big Data analytics to big insights for

value-creation. Information Processing & Management. DOI: https://doi.org/10.1016/j.ipm.2018.01.010

Seddon, J. J. & Currie, W. L. (2017). A model for unpacking Big Data analytics in high frequency trading.

Journal of Business Research, v. 70, p. 300–307. DOI: https://doi.org/10.1016/j.jbusres.2016.08.003

Shastri, A. & Deshpande, M. (2020)A Review of Big Data and Its Applications in Healthcare and

Public Sector. In KULKARNI, A. J. et al. Big Data Analytics in Healthcare. Springer, Cham, p. 55-66.

DOI: https://doi.org/10.1007/978-3-030-31672-3–4

Sheng, J., Amankwah-Amoah, J. & Wang, X. (2017). A multidisciplinary perspective of Big Data in

management research. International Journal of Production Economics, v. 191, p. 97–112. DOI: https://

doi.org/10.1016/j.ijpe.2017.06.006

Sivarajah, U. et al. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of

Business Research, v. 70, p. 263–286. DOI: https://doi.org/10.1016/j.jbusres.2016.08.001

Santos, V., Salvador, P., Gomes, A., Rodrigues, C., Tavares, F., Alves, K. & Bezerril, M. (2017).

Visualização de IRAMUTEQ nas pesquisas qualitativas brasileiras da área da saúde: Scoping review.

CIAIQ2017 - Investigación Cualitativa en Salud, 2. Https://Proceedings.Ciaiq.Org/Index.Php/

Ciaiq2017/Article/View/1230/1191

Twizeyimana, J. D. & Andersson, A. (2019). The public value of E-Government–A literature

review. Government Information Quarterly, v. 36, n. 2, p. 167-178. DOI: https://doi.org/10.1016/j.

giq.2019.01.001

Vasarhelyi, M. A., Kogan, A. & Tuttle, B. M. (2015). Big Data in accounting: An overview. Accounting

Horizons, v. 29, n. 2, p. 381–396. DOI: https://doi.org/10.2308/acch-51071

Wamba, S. F. et al. (2015). How ‘Big Data’ can make big impact: Findings from a systematic review

and a longitudinal case study. International Journal of Production Economics, v. 165, p. 234-246. DOI:

https://doi.org/10.1016/j.ijpe.2014.12.031

Wang, Y., Kung, L. & Byrd, T. A. (2018). Big Data analytics: Understanding its capabilities and potential

benefits for healthcare organizations. Technological Forecasting and Social Change, v. 126, p. 3-13. DOI:

https://doi.org/10.1016/j.techfore.2015.12.019

White, M. (2012). Digital workplaces: vision and reality. Business Information Review, v. 29, n. 4, p.

-214. DOI: https://doi.org/10.1177/0266382112470412

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Publicado

2022-12-27

Como Citar

Carneiro, D. K. de O., Goulart, G. da S., & Porto, R. B. (2022). Big Data in the Brazilian public health sector: concept, characteristics, benefits, and challenges. Revista Do Serviço Público, 73(4), 594-621. Recuperado de https://revista.enap.gov.br/index.php/RSP/article/view/4612

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