Data analysis in the healthcare context: a smart cities perspective




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


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.


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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.