Análise de Conformidade na Área de Saúde com o Suporte da Mineração de Processos
Resumo
Os processos da área de saúde são complexos e necessitam de certo nível de cooperação interdisciplinar entre os mais diversos especialistas e setores. Além dessa complexidade, no Brasil são notórios os problemas enfrentados pela saúde pública e privada, tanto do pronto de vista estrutural, como organizacional e financeiro, o que reflete na sua baixa avaliação sobre qualidade e atendimento. O objetivo deste trabalho é propor a adaptação das técnicas de análise de conformidade da mineração de processos para a área de saúde, de modo que tais técnicas possam auxiliar na descoberta e melhoria do fluxo de atividades e, consequentemente, gerar um efeito positivo sobre a área de saúde no Brasil. Para este fim, foi realizado um estudo de caso no hospital Erasto Gaertner, em Curitiba – PR, que é referência nacional no tratamento de câncer.
Palavras-chave:
Mineração de processos, mapeamento de processos, regras de negócio, análise de conformidade, área de saúde
Referências
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Beckel, C., Sadamori, L., Staake, T., and Santini, S. Revealing household characteristics from smart meter data. Energy 78:397–410, 2014.
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Castellani A.P., Bui, N., Casari, P., Rossi, M., Shelby, Z., and Zorzi, M. Architecture and protocols for the internet of things: A case study. In Proc. IEEE PerCom, pages 678–683, 2010.
Chen, D., Barker, S., Subbaswamy, A., Irwin, D., and Shenoy, P. Non-intrusive occupancy monitoring using smart meters, in: Proc. BuildSys’13, ACM, Rome, Italy, 2013.
Chunhua, S., Bao, F., Zhou, J., Takagi, T., and Sakurai, K. A new scheme for distributed density estimation based privacy-preserving clustering. Availability, Reliability and Security, 2008. ARES 08. Third International Conference on. IEEE, 2008.
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Irwin, D., Wu, A., Barker, S., Mishra, A., Shenoy, P., and Albrecht, J. Exploiting Home Automation Protocols for Load Monitoring in Smart Buildings. In BuildSys, 2011.
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Kelly, J., and Knottenbelt, W. UK-DALE: A dataset recording UK domestic appliance-level electricity demand and whole-house demand. CoRR, abs/1404.0284, 2014.
Kleiminger, W., Staake, T., and Santini, S. Occupancy detection from electricity consumption data. Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. ACM, 2013.
Kolter, J.Z., and Johnson, M.J. REDD: A public data set for energy disaggregation research. In Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, volume 25, pages 59–62. Citeseer, 2011.
McLoughlin, F., Duffy, A. and Conlon, M. A clustering approach to domestic electricity load profile characterization using smart metering data. Applied Energy, vol. 141, pp. 190-199, 2015.
Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., Tonello, A.M. GREEND: An energy consumption dataset of households in Italy and Austria. In Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference on, pages 511–516. IEEE, 2014.
Oliveira, S.R.M., and Zaiane, O.R. Privacy preserving clustering by data transformation. Journal of Information and Data Management, 1(1):37, 2010.
Paetz, A.-G., D¨utschke, E., and Fichtner, W. Smart Homes as a means to sustainable energy consumption: a study of consumer perceptions. Journal of Consumer Policy, 35(1):23–41, 2012.
Pang-Ning, T., Steinbach, M., and Kumar, V. Introduction to data mining. Vol. 1. Boston: Pearson Addison Wesley, 2006.
Peng, H., Zhang, X., Chen, H., Wu, Y., Zeng, J., and Li, D. Enable privacy preservation for k-NN query in two-tiered wireless sensor networks. Communications (ICC), 2015 IEEE International Conference on. IEEE, 2015.
Want, R., Schilit, B.N., and Jenson, S. Enabling the Internet of Things. Computer, (1):28–35, 2015.
Weber, R.H. Internet of things–new security and privacy challenges. Computer Law & Security Review, 26(1):23–30, 2010.
Witten, I.H., Frank, E., and Hall, M.A. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
Zhang, H., Yu, N., and Hu, H. The optimal noise distribution for privacy preserving in mobile aggregation applications. International Journal of Distributed Sensor Networks, 2014.
Armel, K.C., Gupta, A., Shrimali, G., and Albert, A. Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy, vol. 52, no. 0, pp. 213 – 234, 2013.
Axelrod, C.W. Enforcing security, safety and privacy for the Internet of Things. Systems, Applications and Technology Conference (LISAT), 2015 IEEE Long Island. IEEE, 2015.
Barker, S., Mishra, A., Irwin, D., Cecchet, E., Shenoy, P., and Albrecht, J. Smart*: An Open Data Set and Tools for Enabling Research in Sustainable Homes. Proceedings of the 2012 Workshop on Data Mining Applications in Sustainability, Beijing, China, August 2012.
Beckel, C., Kleiminger, W., Cicchetti, R., Staake, T., Santini, S. The ECO data set and the performance of non-intrusive load monitoring algorithms. Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. ACM, 2014.
Beckel, C., Sadamori, L., Staake, T., and Santini, S. Revealing household characteristics from smart meter data. Energy 78:397–410, 2014.
Benzi, F., Anglani, N., Bassi, E., and Frosini, L. Electricity Smart Meters Interfacing the Households. IEEE Transactions on Industrial Electronics , 58(10):4487–4494, 2011.
Bertino, E., Lin, D., and Jiang, W. A survey of quantification of privacy preserving data mining algorithms. In Privacy-preserving data mining, pages 183–205. Springer, 2008.
Bonfigli, R., Squartini, S., Fagiani, M., and Piazza, F. Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview. Environment and Electrical Engineering (EEEIC), 2015 IEEE 15th International Conference on. IEEE, 2015.
Castellani A.P., Bui, N., Casari, P., Rossi, M., Shelby, Z., and Zorzi, M. Architecture and protocols for the internet of things: A case study. In Proc. IEEE PerCom, pages 678–683, 2010.
Chen, D., Barker, S., Subbaswamy, A., Irwin, D., and Shenoy, P. Non-intrusive occupancy monitoring using smart meters, in: Proc. BuildSys’13, ACM, Rome, Italy, 2013.
Chunhua, S., Bao, F., Zhou, J., Takagi, T., and Sakurai, K. A new scheme for distributed density estimation based privacy-preserving clustering. Availability, Reliability and Security, 2008. ARES 08. Third International Conference on. IEEE, 2008.
Dong, C., Kalra, S., Irwin, D., Shenoy, P., and Albrecht, J. Preventing Occupancy Detection From Smart Meters. IEEE Transactions on Smart Grid, 6(5):2426–2434, 2015.
Gadakari, T., Mushatat, S., and Newman, R. Intelligent buildings: Key to achieving total sustainability in the built environment. Journal of Engineering, Project, and Production Management, 4(1):2–16, 2014.
Gokulnath, C., Pryan, M.K., Balan, E.V., Rama, P.K.P., and Jeyanthi, R. Preservation of privacy in data mining by using PCA based perturbation technique. Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2015 International Conference on. IEEE, 2015.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I.H. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18, 2009.
Hink, R.C.B., Beaver, J.M., Buckner, M.A., Morris, T., Adhikari, U., and Pan, S. Machine learning for power system disturbance and cyber-attack discrimination. In Resilient Control Systems (ISRCS), 2014 7th International Symposium on, pages 1–8. IEEE, 2014.
Irwin, D., Wu, A., Barker, S., Mishra, A., Shenoy, P., and Albrecht, J. Exploiting Home Automation Protocols for Load Monitoring in Smart Buildings. In BuildSys, 2011.
Jalla, H.R., and Girija, P.N. An efficient algorithm for privacy preserving data mining using hybrid transformation. International Journal of Data Mining & Knowledge Management Process, 4(4):45, 2014.
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., and Wu, A.Y. The analysis of a simple k-means clustering algorithm. ACM Symposium on Computational Geometry. ACM Press, pages 100–109, 2000.
Kaur, R., and Bansal, M. Transformation approach for boolean attributes in privacy preserving data mining. Next Generation Computing Technologies (NGCT), 2015 1st International Conference on. IEEE, 2015.
Kelly, J., and Knottenbelt, W. Metadata for energy disaggregation. In Proc. CDS. IEEE, 2014.
Kelly, J., and Knottenbelt, W. UK-DALE: A dataset recording UK domestic appliance-level electricity demand and whole-house demand. CoRR, abs/1404.0284, 2014.
Kleiminger, W., Staake, T., and Santini, S. Occupancy detection from electricity consumption data. Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings. ACM, 2013.
Kolter, J.Z., and Johnson, M.J. REDD: A public data set for energy disaggregation research. In Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, volume 25, pages 59–62. Citeseer, 2011.
McLoughlin, F., Duffy, A. and Conlon, M. A clustering approach to domestic electricity load profile characterization using smart metering data. Applied Energy, vol. 141, pp. 190-199, 2015.
Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., Tonello, A.M. GREEND: An energy consumption dataset of households in Italy and Austria. In Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference on, pages 511–516. IEEE, 2014.
Oliveira, S.R.M., and Zaiane, O.R. Privacy preserving clustering by data transformation. Journal of Information and Data Management, 1(1):37, 2010.
Paetz, A.-G., D¨utschke, E., and Fichtner, W. Smart Homes as a means to sustainable energy consumption: a study of consumer perceptions. Journal of Consumer Policy, 35(1):23–41, 2012.
Pang-Ning, T., Steinbach, M., and Kumar, V. Introduction to data mining. Vol. 1. Boston: Pearson Addison Wesley, 2006.
Peng, H., Zhang, X., Chen, H., Wu, Y., Zeng, J., and Li, D. Enable privacy preservation for k-NN query in two-tiered wireless sensor networks. Communications (ICC), 2015 IEEE International Conference on. IEEE, 2015.
Want, R., Schilit, B.N., and Jenson, S. Enabling the Internet of Things. Computer, (1):28–35, 2015.
Weber, R.H. Internet of things–new security and privacy challenges. Computer Law & Security Review, 26(1):23–30, 2010.
Witten, I.H., Frank, E., and Hall, M.A. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
Zhang, H., Yu, N., and Hu, H. The optimal noise distribution for privacy preserving in mobile aggregation applications. International Journal of Distributed Sensor Networks, 2014.
Publicado
17/05/2016
Como Citar
RIZ, Gustavo; SANTOS, Eduardo Alves Portela; LOURES, Eduardo de Freitas Rocha.
Análise de Conformidade na Área de Saúde com o Suporte da Mineração de Processos. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 12. , 2016, Florianópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 052-059.
DOI: https://doi.org/10.5753/sbsi.2016.5945.
