Data analysis in the healthcare context: a smart cities perspective

Autores

DOI:

https://doi.org/10.5585/exactaep.2021.20591

Palavras-chave:

Data Analysis, Big Data, Big Data Analysis, Healthcare, Health, Smart Cities, Technologies, Citizens, Machine Learning, Electronic Health.

Resumo

Cities are characterized as smart when they prioritize and develop ways to link technology, infrastructure, knowledge, and policies to improve the quality of life of citizens. In addition, technological application alone is not capable of making a city smart, people must be able to adapt and interact with technologies, as well as it is essential that the large volume of data generated by different devices, in real-time, called Big Data, are analyzed and interpreted, transforming them into interpretable information. In this context, this study aims to identify data analysis in the context of healthcare, as one of the domains of Smart Cities. For this, a bibliographic review was carried out, using the Methodi Ordinatio methodology, resulting in a portfolio of articles with scientific relevance, which was the source of data collection and analysis. Thus, the results obtained demonstrate that the most studied technologies in this context seek to analyze data with Big Data Analytics techniques, encompassing Artificial Intelligence and Machine Learning, which analyze data generated by "devices" in which Electronic Health Records are collected, and "sensors" often associated with the Internet of Things. However, some challenges were found, highlighting the need for data security and privacy, with Blockchain technology being mentioned several times as a possible solution, thus, by combining digital technologies and data analysis techniques, an approximation is obtained. real of smart city concept.

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Biografia do Autor

Fabiane Florencio de Souza, UTFPR

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

Alana Corsi, Federal University of Technology - Paraná (UTFPR) / Câmpus Ponta Grossa (PR)

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

Clayton Pereira de Sá, Federal University of Technology-Paraná (UTFPR) / Câmpus Ponta Grossa (PR)

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

Regina Negri Pagani, Federal University of Technology - Paraná (UTFPR) / Câmpus Ponta Grossa (PR)

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.

João Luiz Kovaleski, Federal University of Technology - Paraná (UTFPR) / Câmpus Ponta Grossa (PR)

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.

Referências

Aceto G, Persico V, Pescapé A. Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0. Journal of Industrial Information Integration, 18, 100129, 2020. https://doi.org/10.1016/J.JII.2020.100129

Aggarwal S, Kumar N, Alhussein M, Muhammad G. Blockchain-Based UAV Path Planning for Healthcare 4.0: Current Challenges and the Way Ahead. IEEE Network, 35(1), 20–29, 2021. https://doi.org/10.1109/MNET.011.2000069

Al-Jaroodi J, Mohamed N, AbuKhousa E. Health 4.0: On the Way to Realizing the Healthcare of the Future. IEEE Access, 2020. https://doi.org/10.1109/ACCESS.2020.3038858

Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H. Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Information Systems: 13(3), 329–351, 2018. https://doi.org/10.1080/17517575.2018.1557256

Bibri S, Krogstie J. Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183-212, 2017. https://doi.org/10.1016/j.scs.2017.02.016

Capdevila I, Zarlenga M. Smart City or Smart Citizens? The Barcelona Case. SSRN Electronic Journal, 2015. https://doi.org/10.2139/SSRN.2585682

Caragliu A, Del Bo C, Nijkamp P. Smart Cities in Europe. Routledge, 185-207, 2013.

Cavallini ME, Bisson MP. (2002). Farmácia hospitalar: um enfoque em sistemas de saúde. Manole.

Chen C, Loh E-W, Kuo KN, Tam K-W. The Times they Are a-Changin’ – Healthcare 4.0 Is Coming! Journal of Medical Systems, 44(2), 1–4, 2019. https://doi.org/10.1007/S10916-019-1513-0

Chen H, Chiang RHL, Storey VC. Business intelligence and analytics: From big data to big impact. MIS Quarterly: Management Information Systems, 36(4), 1165–1188, 2012. https://doi.org/10.2307/41703503

Chen M, Mao S, Liu Y. Big Data: A Survey. Mobile Networks and Applications, 19(2), 171–209, 2014. https://doi.org/10.1007/S11036-013-0489-0

Dashtban M, Li W. Predicting non-attendance in hospital outpatient appointments using deep learning approach. Health Systems, 1-22, 2021 https://doi.org/10.1080/20476965.2021.1924085

Davenport TH, Harris JG. Competição analítica: vencendo através da nova ciência. Alta Books, 2020.

Deloitte (2020). 2020 global health care outlook: Laying a foundation for the future. [online] Available at: https://www2.deloitte.com/content/dam/Deloitte/br/Documents/life-sciences-health-care/Deloitte-2020-global-health-care-outlook.pdf [Accessed 27 May 2021].

Fox P, Hendler J. Changing the Equation on Scientific Data Visualization. Science, 331(6018), 705–708, 2011. https://doi.org/10.1126/SCIENCE.1197654

Gomes MAS, Silva VL, Kovaleski JL, Pagani RN. Big data analytics applied to health services: a literature review. Exacta, 0(0), 2021. https://doi.org/10.5585/EXACTAEP.2021.17297

Gupta R, Tanwar S, Tyagi S, Kumar N. Tactile internet and its applications in 5G era: A comprehensive review. International Journal of Communication Systems, 32(14), e3981, 2019. https://doi.org/10.1002/DAC.3981

Hathaliya JJ, Tanwar S, Tyagi S, Kumar N. Securing electronics healthcare records in Healthcare 4.0 : A biometric-based approach. Computers & Electrical Engineering, 76, 398–410, 2019. https://doi.org/10.1016/J.COMPELECENG.2019.04.017

Herland M, Khoshgoftaar TM, Wald R. A review of data mining using big data in health informatics. Journal Of Big Data, 1(1), 1–35, 2014. https://doi.org/10.1186/2196-1115-1-2

Huang H, Yao X, Krisp J, Jiang B. Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions. Computers, Environment And Urban Systems, 90, 101712, 2021. https://doi.org/10.1016/j.compenvurbsys.2021.101712

Hu H, Wen Y, Chua TS, Li X. Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652–687, 2014. https://doi.org/10.1109/ACCESS.2014.2332453

Hung SY, Nakayama M, Chen CC, Tsai FL. Physician perceptions of electronic medical records: The impact of system service quality, and generation/experience gaps. International Journal of Healthcare Technology and Management, 17(4), 229–254, 2019. https://doi.org/10.1504/IJHTM.2019.104936

Jaleel A, Mahmood T, Hassan MA, Bano G, Khurshid SK. Towards Medical Data Interoperability through Collaboration of Healthcare Devices. IEEE Access, 8, 132302–132319, 2020. https://doi.org/10.1109/ACCESS.2020.3009783

Jayaraman PP, Forkan ARM, Morshed A, Haghighi PD, Kang Y-B. Healthcare 4.0: A review of frontiers in digital health. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), e1350, 2020. https://doi.org/10.1002/WIDM.1350

Jee K, Kim GH. Potentiality of big data in the medical sector: focus on how to reshape the healthcare system. Healthcare Informatics Research, 19(2), 79–85, 2013. https://doi.org/10.4258/HIR.2013.19.2.79

Kambatla K, Kollias G, Kumar V, Grama A. Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573, 2014. https://doi.org/10.1016/J.JPDC.2014.01.003

Karimi Y, Haghi Kashani M, Akbari M, Mahdipour E. Leveraging big data in smart cities: A systematic review. Concurrency And Computation: Practice And Experience, 33(21), 2021. https://doi.org/10.1002/cpe.6379

Kauffmann E, Peral J, Gil D, Ferrández A, Sellers R, Mora H. A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management, 90, 523–537, 2020. https://doi.org/10.1016/J.INDMARMAN.2019.08.003

König P. Citizen-centered data governance in the smart city: From ethics to accountability. Sustainable Cities And Society, 75, 103308, 2021. https://doi.org/10.1016/j.scs.2021.103308

Koumaditis K, Hussain T. Personal healthcare records research: past, present and new dimensions. International Journal of Healthcare Technology and Management, 17(1), 1–28, 2018. https://doi.org/10.1504/IJHTM.2018.091821

Kumar A, Krishnamurthi R, Nayyar A, Sharma K, Grover V, Hossain E. A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes. IEEE Access, 8, 118433–118471, 2020. https://doi.org/10.1109/ACCESS.2020.3004790

Li X, Fong PSW, Dai S, Li Y. Towards sustainable Smart Cities: An empirical comparative assessment and development pattern optimization in China. Journal of Cleaner Production, 215, 730-743, 2019.

Luongo J, Rocha RM, Miranda TVM, Hervás MJW, Silva RMA. Gestão de qualidade em saúde. São Paulo: Rideel, 239-65, 2011.

Manogaran G, Lopez D. Spatial cumulative sum algorithm with big data analytics for climate change detection. Computers & Electrical Engineering, 65, 207–221, 2018. https://doi.org/10.1016/J.COMPELECENG.2017.04.006

McCarthy S, Fitzgerald C, Sahm L, Bradley C, Walsh EK. Patient-held health IT adoption across the primary-secondary care interface: a Normalisation Process Theory perspective. Health Systems, 1-13, 2020. https://doi.org/10.1080/20476965.2020.1822146

Minelli M, Chambers M, Dhiraj A. Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. John Wiley & Sons, 578, 2013.

Mozachi N, Souza VHSD. O hospital: manual do ambiente hospitalar. Curitiba: Manual Real, 2005.

Mohamed A, Najafabadi MK, Wah YB, Zaman EAK, Maskat R. The state of the art and taxonomy of big data analytics: view from new big data framework. Artificial Intelligence Review, 53(2), 989–1037, 2019. https://doi.org/10.1007/S10462-019-09685-9

Murdoch T, Detsky A. The Inevitable Application of Big Data to Health Care. JAMA, 309(13), 1351, 2013.

Neirotti P, De Marco A, Cagliano AC, Mangano G, Scorrano F. Current trends in Smart City initiatives: Some stylised facts. Cities, 38, 25–36, 2014. https://doi.org/10.1016/J.CITIES.2013.12.010

Pagani RN, Kovaleski JL, Resende LM. TICs na composição da Methodi Ordinatio: construção de portfólio bibliográfico sobre Modelos de Transferência de Tecnologia. Ciência Da Informação, 24(2), 161–187, 2018. https://doi.org/10.18225/CI.INF..V47I1.1886

Pérez L, Salvachúa J. An Approach to Build e-Health IoT Reactive Multi-Services Based on Technologies around Cloud Computing for Elderly Care in Smart City Homes. Applied Sciences, 11(11), 5172, 2021. https://doi.org/10.3390/app11115172

Qiu H, Qiu M, Liu M, Memmi G. Secure Health Data Sharing for Medical Cyber-Physical Systems for the Healthcare 4.0. IEEE Journal of Biomedical and Health Informatics, 24(9), 2499–2505, 2020. https://doi.org/10.1109/JBHI.2020.2973467

Sanders NR. How to Use Big Data to Drive Your Supply Chain. California Management Review, 58(3), 26-48, 2016. https://doi.org/10.1525/CMR.2016.58.3.26

Sharma D, Singh GA, Bajaj R. Evolution from ancient medication to human-centered Healthcare 4.0: A review on health care recommender systems. International Journal of Communication Systems, e4058, 2019. https://doi.org/10.1002/DAC.4058

Sun Y, Yan H, Lu C, Bie R, Zhou Z. Constructing the Web of Events from Raw Data in the Web of Things. Mobile Information Systems, 10(1), 105–125, 2014. https://doi.org/10.1155/2014/517486

Tortorella GL, Fogliatto FS, Espôsto KF, Vergara AMC, Vassolo R, Mendoza DT, Narayanamurthy G. Effects of contingencies on healthcare 4.0 technologies adoption and barriers in emerging economies. Technological Forecasting and Social Change, 156, 2020. https://doi.org/10.1016/J.TECHFORE.2020.120048

Tortorella GL, Saurin TA, Fogliatto FS, Rosa VM, Tonetto LM, Magrabi F. Impacts of Healthcare 4.0 digital technologies on the resilience of hospitals. Technological Forecasting and Social Change, 166, 120666, 2021. https://doi.org/10.1016/J.TECHFORE.2021.120666

UNESCO (United Nations Educational, Scientific and Cultural Organization). From green economies to green societies: UNESCO’s commitment to sustainable development, 2011. Disponível em: http://unesdoc.unesco.org/images/0021/002133/213311e.pdf. Acesso em: 03 Nov. 2021.

Wasim A, Varalakshmi M, Sudeepthi J. Streaming Big Data Analytics- Current Status, Challenges and Connection of unbounded data Processing platforms. International Journal of Innovative Technology and Exploring Engineering, 8(9S2), 698-700, 2019.

Wu W. Determinants of citizen-generated data in a smart city: Analysis of 311 system user behavior. Sustainable Cities And Society, 59, 102167, 2020. https://doi.org/10.1016/j.scs.2020.102167

Wu X, Zhu X, Wu GQ, Ding W. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107, 2014. https://doi.org/10.1109/TKDE.2013.109

Yang G, Pang Z, Jamal Deen M, Dong M, Zhang YT, Lovell N, Rahmani AM. Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies. IEEE Journal of Biomedical and Health Informatics, 24(9), 2535–2549, 2020. https://doi.org/10.1109/JBHI.2020.2990529

Zeng D, Tim Y, Yu J, Liu W. Actualizing big data analytics for smart cities: A cascading affordance study. International Journal Of Information Management, 54, 102156, 2020. https://doi.org/10.1016/j.ijinfomgt.2020.102156

Zhang X, Yang LT, Liu C, Chen J. A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud. IEEE Transactions on Parallel and Distributed Systems, 25(2), 363–373, 2014. https://doi.org/10.1109/TPDS.2013.48

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Publicado

22.09.2023

Como Citar

Souza, F. F. de, Corsi, A., de Sá, C. P., Pagani, R. N., & Kovaleski, J. L. (2023). Data analysis in the healthcare context: a smart cities perspective. Exacta, 21(3), 827–850. https://doi.org/10.5585/exactaep.2021.20591

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