Abstract
Nowadays, any health-related issue is always a very sensitive issue in the society as it interferes directly in the people well-being. In this sense, in order to improve the quality of health services, a good quality management of complaints is essential. Due to the volume of complaints, there is a need to explore Data Science models in order to automate internal quality complaints processes. Thus, the main objective of this article is to improve the quality of the health claims analysis process, as well as the knowledge analysis at the level of information systems applied to referred health. In this article, it is observable the development of data treatment in two stages: loading the data to an auxiliary database and processing them through the Extract, Transform and Load (ETL) process. With the data warehouse created, the Online Analytical Processing (OLAP) cube was developed that was later interconnected in Power BI enabling the creation and analysis of dashboards. The various models studied showed somehow a poor quality of the data that supports them. In this sense, with the application of the filters, it was possible to obtain a more detailed temporal perception, as the height of the year in which there is more affluence of registered complaints. Thus, we can find in this study the main analysis of paper complaints and online complaints. For paper complaints, a total of 234 records of the selected period is well-known for the “Unknown” valence affluence with 72.67% of the registrations. With regard to online complaints, a total of 42 records of the selected period is notorious for the following incidence: Typification “Other subjects” with 19.05% of registrations; State “Inserted” with 90.48% of registrations; Ignorance “Unknown” with 95.24% of registrations; Typology “Complaint” with 69.05% of registrations.
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Acknowledgement
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013
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Correia, C., Portela, F., Santos, M.F., Silva, Á. (2018). Data Science Analysis of HealthCare Complaints. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-319-77700-9_18
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DOI: https://doi.org/10.1007/978-3-319-77700-9_18
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