Advertisement

Data Science Analysis of HealthCare Complaints

  • Carlos Correia
  • Filipe Portela
  • Manuel Filipe Santos
  • Álvaro Silva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 747)

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.

Keywords

Data Science Knowledge discovery in database Health information systems Business Intelligence Quality of healthcare complaints 

Notes

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

References

  1. 1.
    Simões, J.: As parcerias público-privadas no sector da saúde em Portugal. Revista Portuguesa de Saúde Pública 4, 79–90 (2004)Google Scholar
  2. 2.
  3. 3.
    Anabela, L.: Entre o direito a reclamar e o direito à saúde. Serviço social em gabinetes do cidadão, do SNS (2014)Google Scholar
  4. 4.
    Moreira, V.: Regulação dos serviços da saúde - público (2011)Google Scholar
  5. 5.
    Almeida, L.: A criação da Entidade Reguladora da Saúde em Portugal | Comunicação Por conta e Risco (2010). https://porcontaerisco.wordpress.com/2010/12/03/a-criacao-da-entidade-reguladora-da-saude-em-portugal/#comments. Accessed 18 Oct 2016
  6. 6.
    Berner, E.S.: Clinical decision support systems: theory and practice. Springer, New York (1999)CrossRefGoogle Scholar
  7. 7.
    Vasconcelos Parra, R.: Reclamações no setor público da saúde (2014)Google Scholar
  8. 8.
    Portela, F., Gago, P., Santos, M.F., Machado, J., Abelha, A., Silva, Á., Rua, F.: Implementing a pervasive real-time intelligent system for tracking critical events with intensive care patients. Int. J. Healthc. Inf. Syst. Inform. 8(4), 1–16 (2013)CrossRefGoogle Scholar
  9. 9.
    Oliveira, A., Portela, F., Santos, M.F., Neves, J.: Towards an intelligent system for monitoring health complaints, pp. 639–649. Springer, Cham (2016)Google Scholar
  10. 10.
    Oliveira, A., Portela, F., Machado, J., Abelha, A., Neves, J.M., Vaz, S., Silva, A., Santos, M.F.: Towards an ontology for health complaints management. In: KMIS 2015 - International Conference on Knowledge Management and Information Sharing, Lisbon, Portugal, pp. 174–181. SciTePress (2015). ISBN978-989-758-158-8Google Scholar
  11. 11.
    Negash, S.: Business intelligence. Commun. Assoc. Inf. 13(15), 177–195 (2004)Google Scholar
  12. 12.
    Berson, A., Smith, S.J.: Data Warehousing, Data Mining, and OLAP. McGraw-Hill, New York (1997)Google Scholar
  13. 13.
    Guarda, T., Augusto, M.F., Barrionuevo, O., Pinto, F.M.: Internet of things in pervasive healthcare systems. In: Machado, J., Abelha, A., Santos, M., Portela, F. (eds.) Next-Generation Mobile and Pervasive Healthcare Solutions, pp. 22–31. IGI Global, Hershey (2018).  https://doi.org/10.4018/978-1-5225-2851-7.ch002 CrossRefGoogle Scholar
  14. 14.
    Pereira, A., Portela, F., Santos, M.F., Abelha, A., Machado, J.: Pervasive business intelligence: a new trend in critical healthcare. In: Procedia Computer Science - ICTH 2016 - International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, vol. 98, pp. 362–367. Elsevier (2016). ISSN1877-0509Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Carlos Correia
    • 1
  • Filipe Portela
    • 1
  • Manuel Filipe Santos
    • 1
  • Álvaro Silva
    • 2
  1. 1.Algoritmi Research CenterUniversity of MinhoGuimarãesPortugal
  2. 2.ERS - Entidade Reguladora da SaúdePortoPortugal

Personalised recommendations