Big data na saúde: um estudo sobre a pluralidade de conceitos e caminhos para a aplicabilidade dessa tecnologia no setor de saúde pública

ADAM, Hammaad et al. Mitigating the impact of biased artificial intelligence in emergency decision-making. Communications Medicine, Londres, v. 2, n. 1, p. 149, 2022. DOI: https://doi.org/10.1038/s43856-022-00214-4. Disponível em: https://www.nature.com/articles/s43856-022-00214-4. Acesso em: 22 jul. 2026. AGRAWAL, Raag; PRABAKARAN, Sudhakaran. Big data in digital healthcare: lessons learnt and recommendations for general practice. Heredity, London, v. 124, p. 525-534, 2020. DOI: https://doi.org/10.1038/s41437-020-0303-2. Disponível em: https://www.nature.com/articles/s41437-020-0303-2. Acesso em: 15 jul. 2026. ANDREASEN, Martin M.; CHRISTENSEN, Jens H. E.; RUDEBUSCH, Glenn D. Term structure analysis with big data: one-step estimation using bond prices. Journal of Econometrics, Amsterdam, v. 212, n. 1, p. 26-46, 2019. DOI: https://doi.org/10.1016/j.jeconom.2019.04.019. Disponível em: https://www.sciencedirect.com/science/article/pii/S0304407619300740. Acesso em: 7 jul. 2026. BAINBRIDGE, Michael. Big data challenges for clinical and precision medicine. In: HOUSEH, Mowafa; KUSHNIRUK, Andre W.; BORYCKI, Elizabeth M. Big data, big challenges: a healthcare perspective. Cham: Springer Verlag, 2019. p. 17-31. DOI: https://doi.org/10.1007/978-3-030-06109-8_2. Disponível em: https://philpapers.org/rec/BAIBDC. Acesso em: 15 jul. 2026. BASILI, Victor R. Software modeling and measurement: the Goal/Question/Metric paradigm. College Park: University of Maryland, 1992. (Technical Report CS-TR-2956). Disponível em: https://www.cs.umd.edu/~basili/publications/technical/T78.pdf. Acesso em: 23 jul. 2026. BATKO, Kornelia; ŚLĘZAK, Andrzej. The use of big data analytics in healthcare. Journal of Big Data, Heidelberg, v. 9, n. 3, p. 1-24, 2022. DOI: https://doi.org/10.1186/s40537-021-00553-4. Disponível em: https://link.springer.com/article/10.1186/s40537-021-00553-4. Acesso em: 15 jul. 2026. BORISSOVA, Vladia. Cultural heritage digitization and related intellectual property issues. Journal of Cultural Heritage, Thousand Oaks, v. 34, p. 145-150, 2018. DOI: https://doi.org/10.1016/j.culher.2018.04.023. Disponível em: https://aata.getty.edu/primo-explore/fulldisplay/GETTY_AATA9934107255501551/AATA. Acesso em: 7 jul. 2026. CARTER, Thomas et al. Building a big data research program at a small university. The Journal of Computing Sciences in Colleges, Evansville, v. 28, n. 2, p. 95-102, 2011. Disponível em: https://dl.acm.org/doi/abs/10.5555/2382887.2382903. Acesso em: 6 jul. 2026. D’AGOSTINO, Marcelo et al. Salud pública y la interdependencia digital: evolución tecnológica, sostenibilidad tecnológica y la revolución del usuario. Revista Panamericana de Salud Pública, Washington, DC, v. 45, e156, 2021. DOI: https://doi.org/10.26633/RPSP.2021.156. Disponível em: https://iris.paho.org/items/99df8913-d222-4987-8acd-bee239c8c808. Acesso em: 3 jul. 2026. DAVENPORT, Thomas; DYCHÉ, Jill. Big data in big companies. [Portland]: International Institute for Analytics, 2013. Disponível em: https://www.iqpc.com/media/7863/11710.pdf. Acesso em: 15 jul. 2026. GRIVA, Anastasia et al. Artificial intelligence and analytics in practice. Journal of Decision Systems, Thousand Oaks, v. 32, n. 3, p. 535-541, 2023. DOI: https://doi.org/10.1080/12460125.2022.2122218. Disponível em: https://www.tandfonline.com/doi/full/10.1080/12460125.2022.2122218. Acesso em: 3 jul. 2026. GUPTA, Deepak; RANI, Rinkle. A study of big data evolution and research challenges. Journal of Information Science, Thousand Oaks, v. 45, n. 3, p. 322-340, 2018. DOI: https://doi.org/10.1177/016555151878988. Disponível em: https://journals.sagepub.com/doi/10.1177/0165551518789880. Acesso em: 3 jul. 2026. HRIPCSAK, George et al. Drawing reproducible conclusions from observational clinical data with OHDSI. Yearbook of Medical Informatics, v. 30, n. 1, p. 283-289, 2021. DOI: 10.1055/s-0041-1726481. HOSSAIN, Md. Mahbub et al. Digital interventions for people living with non-communicable diseases in India: a systematic review of intervention studies and recommendations for future research and development. Digital Health, Thousand Oaks, v. 5, 983243, 2019. DOI: https://doi.org/10.1177/2055207619896153. Disponível em: https://journals.sagepub.com/doi/10.1177/2055207619896153. Acesso em: 3 jul. 2026. JINDAL, Anish et al. Providing healthcare-as-a-service using fuzzy rule based big data analytics in cloud computing. IEEE Journal of Biomedical and Health Informatics, New York, v. 22, n. 5, p. 1605-1618, 2018. DOI: https://doi.org/10.1109/jbhi.2018.2799198. Disponível em: https://ieeexplore.ieee.org/document/8272340. Acesso em: 7 jul. 2026. KARPATHAKIS, Kassandra et al. An evaluation service for digital public health interventions: user-centered design approach. Journal of Medical Internet Research, Toronto, v. 23, n. 9, 2021. DOI: https://doi.org/10.2196/28356. Disponível em: https://www.jmir.org/2021/9/e28356. Acesso em: 3 jul. 2026. KHANRA, Sayantan et al. Big data analytics in healthcare: a systematic literature review. Enterprise Information Systems, Thousand Oaks, v. 14, n. 7, p. 878-912, 2020. DOI: https://doi.org/10.1080/17517575.2020.1812005. Disponível em: https://www.tandfonline.com/doi/full/10.1080/17517575.2020.1812005. Acesso em: 3 jul. 2026. KITCHENHAM, Barbara; CHARTERS, Stuart. Guidelines for performing systematic literature reviews in software engineering. Version 2.3. Keele: Keele University, 2007. (EBSE Technical Report). Disponível em: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf. Acesso em: 3 jul. 2026. LANEY, Douglas (ed). The importance of “big data”: a definition. Stamford: Gartner, 2012. LASSO CARDONA, Luis Adrian; FRANCO OCAMPO, Diego Fernando; ESTRADA ESPONDA, Royer David. Aplicaciones de la datificación y big data en América Latina entre el 2015 y 2019. Revista Logos Ciencia & Tecnología, Bogotá, v. 14, n. 2, p. 125-143, 2022. DOI: https://doi.org/10.22335/rlct.v14i2.1594. Disponível em: https://revistalogos.policia.edu.co:8443/index.php/rlct/article/view/1594. Acesso em: 3 jul. 2026. LAUSCH, Angela et al. Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sensing, Basel, v. 10, n. 7, 1120, 2018. DOI: https://doi.org/10.3390/rs10071120. Disponível em: https://www.mdpi.com/2072-4292/10/7/1120. Acesso em: 6 jul. 2026. LOTFI, Chaimaa et al. A tool for study on impact of big data technologies on firm performance. In: RAJAKUMAR, G. et al. (ed.). Intelligent Communication Technologies and Virtual Mobile Networks. Singapore: Springer, 2023. v. 131, p. 501-515. DOI: https://doi.org/10.1007/978-981-19-1844-5_40. Disponível em: https://link.springer.com/chapter/10.1007/978-981-19-1844-5_40. Acesso em: 15 jul. 2026. LV, Zhihan; QIAO, Liang. Analysis of healthcare big data. Future Generation Computer Systems, Amsterdam, v. 109, p. 103-110, 2020. DOI: https://doi.org/10.1016/j.future.2020.03.039. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0167739X20304829. Acesso em: 6 jul. 2026. MAURO, Andrea de; GRECO, Marco; GRIMALDI, Michele. Beyond data: a review of the Big Data definition. Proceedings of the 4th International Conference on Integrated Information, v. 1644, n. 1, p. 97-104, 2015. Trabalho apresentado ao International Conference on Integrated Information, 2014, Madrid. DOI: https://doi.org/10.13140/2.1.2341.5048. Disponível em: https://www.dhi.ac.uk/san/waysofbeing/data/data-crone-demauro-2015.pdf. Acesso em: 23 jul. 2026. MAYER-SCHÖNBERGER, Viktor; CUKIER, Kenneth. Big data: a revolution that will transform how we live, work, and think. London: John Murray, 2013. MICHAEL, Mike; LUPTON, Deborah. Toward a manifesto for the “public understanding of big data”. Public Understanding of Science, Bristol, v. 25, n. 1, p. 104-116, 2016. DOI: https://doi.org/10.1177/0963662515609005. Disponível em: https://journals.sagepub.com/doi/10.1177/0963662515609005. Acesso em: 3 jul. 2026. OHLHORST, Frank. J. Big data analytics: turning big data into big money. Hoboken: John Wiley & Sons, 2012. (Wiley & SAS business). PAIGE, Ellie et al. A versatile big data health system for Australia: driving improvements in cardiovascular health. Heart, Lung and Circulation, Chatswood, v. 30, n. 10, p. 1467-1476, 2021. DOI: https://doi.org/10.1016/j.hlc.2021.04.023. Disponível em: https://www.heartlungcirc.org/article/S1443-9506(21)00517-5/abstract. Acesso em: 15 jul. 2026. PETERSEN, Kai; VAKKALANKA, Sairam; KUZNIARZ, Ludwik. Guidelines for conducting systematic mapping studies in software engineering: an update. Information and Software Technology, Amsterdam, v. 64, p. 1-18, 2015. DOI: https://doi.org/10.1016/j.infsof.2015.03.007. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S0950584915000646. Acesso em: 3 jul. 2026. RESNYANSKY, Lucy. Conceptual frameworks for social and cultural big data analytics: answering the epistemological challenge. Big Data & Society, Thousand Oaks, v. 6, n. 1, p. 1-12, 2019. DOI: https://doi.org/10.1177/20539517188238. Disponível em: https://journals.sagepub.com/doi/10.1177/2053951718823815. Acesso em: 7 jul. 2026. RICARTE, Élmano. A expansão do processo de digitalização durante a pandemia de covid-19. Finisterra, Lisboa, v. 55, n. 115, p. 53-60, 2020. DOI: https://doi.org/10.18055/Finis20350. Disponível em: https://revistas.rcaap.pt/finisterra/article/view/20350. Acesso em: 7 jul. 2026. SACKETT, David L. et al. Evidence-based medicine: how to practice and teach EBM. 2. ed. Edinburgh: Churchill Livingstone, 2000. SAUNDERS, Gabrielle H. et al. Application of big data to support evidence-based public health policy decision-making for hearing. Ear and Hearing, Baltimore, MD, v. 41, n. 5, p. 1057-1063, 2020. DOI: https://doi.org/10.1097/AUD.0000000000000850. Disponível em: https://journals.lww.com/ear-hearing/fulltext/2020/09000/application_of_big_data_to_support_evidence_based.1.aspx. Acesso em: 3 jul. 2026. SEGATA, Jean; RIFIOTIS, Theophilos. Digitalização e dataficação da vida. Civitas, Porto Alegre, v. 21, n. 2, p. 186-192, 2021. DOI: https://doi.org/10.15448/1984-7289.2021.2. Disponível em: https://revistaseletronicas.pucrs.br/civitas/issue/view/1497. Acesso em: 15 jul. 2026. SENTHILKUMAR, S. A. et al. Big data in healthcare management: a review of literature. American Journal of Theoretical and Applied Business, London, v. 4, n. 2, p. 57-69, 2018. DOI: https://doi.org/10.11648/j.ajtab.20180402.14. Disponível em: https://www.sciencepublishinggroup.com/article/10.11648/j.ajtab.20180402.14. Acesso em: 15 jul. 2026. SILVA, Sandro Luís Freire de Castro. A coexistência de lógicas institucionais e os sistemas emergentes no contexto de sistemas de informação em saúde. 2021. 210 f. Tese (Doutorado em Informática) – Universidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, 2021. Disponível em: https://ppgi.uniriotec.br/wp-content/uploads/sites/41/2022/06/Tese-Sandro-Luis-Freire-VFINAL-REVISA%CC%83O-FINAL-2-Assinada.pdf. Acesso em: 3 jul. 2026. SILVENNOINEN, Piia. Professional master’s degree students’ perceptions on the changes digitalisation imposes on counselling in the social and health care sector. International Journal of Environmental Research and Public Health, Basel, v. 17, n. 17, p. 6243, 2020. DOI: https://doi.org/10.3390/ijerph17176243. Disponível em: https://www.mdpi.com/1660-4601/17/17/6243. Acesso em: 15 jul. 2026. SINGH, Ravi Pratap et al. Cloud computing in solving problems of covid-19 pandemic. Journal of Industrial Integration and Management, Singapure, v. 6, n. 2, p. 209-219, 2021. DOI: https://doi.org/10.1142/S2424862221500044. Disponível em: https://www.worldscientific.com/doi/abs/10.1142/S2424862221500044. Acesso em: 15 jul. 2026. TAHMASSEBI, Amirhessan et al. Deep learning in medical imaging: fMRI big data analysis via convolutional neural networks. In: PRACTICE AND EXPERIENCE ON ADVANCED RESEARCH COMPUTING: SEAMLESS CREATIVITY, 18., Pittsburgh, 22-26 Jul 2018. Proceedings […] New York: Association for Computing Machinery, 2018. p. 1-4. DOI: https://doi.org/10.1145/3219104.3229250. Disponível em: https://dl.acm.org/doi/10.1145/3219104.3229250. Acesso em: 7 jul. 2026. TOPOL, Eric J. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, New York, v. 25, p. 44-56, 2019. DOI: https://doi.org/10.1038/s41591-018-0300-7. Disponível em: https://www.nature.com/articles/s41591-018-0300-7. Acesso em: 15 jul. 2026. WANG, Sophia Y. PERSHING, Suzann; LEE, Aaron Y. Big data requirements for Artificial Intelligence. Current Opinion in Ophthalmology, Philadelphia, v. 31, n. 5, p. 318-323, 2020. DOI: https://doi.org/10.1097/icu.0000000000000676. Disponível em: https://journals.lww.com/co-ophthalmology/fulltext/2020/09000/big_data_requirements_for_artificial_intelligence.4.aspx. Acesso em: 15 jul. 2026. WERUTSKY, Gustavo et al. Perspectives on emerging technologies, personalised medicine, and clinical research for cancer control in Latin America and the Caribbean. The Lancet Oncology, London, v. 22, n. 11, p. E488-E500, 2021. DOI: 10.1016/S1470-2045(21)00523-4. Disponível em: https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(21)00523-4/abstract. Acesso em: 15 jul. 2026. WESSON, Paul et al. Risks and opportunities to ensure equity in the application of big data research in public health. Annual Review of Public Health, Palo Alto, v. 43, p. 59-78, 2022. DOI: https://doi.org/10.1146/annurev-publhealth-051920-110928. Disponível em: https://www.annualreviews.org/content/journals/10.1146/annurev-publhealth-051920-110928. Acesso em: 15 jul. 2026. WONG, Brian Li Han et al. The dawn of digital public health in Europe: implications for public health policy and practice. The Lancet Regional Health – Europe, Oxford, v. 14, 100316, 2022. DOI: https://doi.org/10.1016/j.lanepe.2022.100316. Disponível em: https://www.thelancet.com/journals/lanepe/article/PIIS2666-7762(22)00009-6/fulltext. Acesso em: 3 jun. 2026. WU, Jun et al. The challenge of healthcare big data to China’s commercial health insurance industry: evaluation and recommendations. BMC Health Services Research, London, v. 22, 1189, 2022. DOI: https://doi.org/10.1186/s12913-022-08574-2. Disponível em: https://link.springer.com/article/10.1186/s12913-022-08574-2. Acesso em: 23 jul. 2026. WU, Xiaobo; LIANG, Liping; CHEN Siyuan. How big data alters value creation: through the lens of big data competency. Management Decision, Leeds, v. 60, n. 3, p. 707-734, 2022. DOI: https://doi.org/10.1108/MD-09-2021-1199. Disponível em: https://www.emerald.com/md/article/60/3/707/452928/How-big-data-alters-value-creation-through-the. Acesso em: 6 jul. 2026. YUNITA, Ariana; SANTOSO, Harry B.; HASIBUAN, Zainal. A. “Everything is data”: towards one big data ecosystem using multiple sources of data on higher education in Indonesia. Journal of Big Data, London, v. 9, 91, 2022. DOI: https://doi.org/10.1186/s40537-022-00639-7. Disponível em: https://link.springer.com/article/10.1186/s40537-022-00639-7. Acesso em: 3 jul. 2026. ZHANG, Yang; HUANG, Tao; BOMPARD, Ettore Francesco. Big data analytics in smart grids: a review. Energy Informatics, London, v. 1, 8, 2018. DOI: https://doi.org/10.1186/s42162-018-0007-5. Disponível em: https://link.springer.com/article/10.1186/s42162-018-0007-5. Acesso em: 6 jul. 2026.