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Analysis the patients’ careflows using process mining

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  • Abdel-Hamed Mohamed Rashed
  • Noha E El-Attar
  • Diaa Salama Abdelminaam
  • Mohamed Abdelfatah

Abstract

Recently, The Egyptian health sector whether it is public or private; utilizes emerging technologies such as data mining, business intelligence, Internet of Things (IoT), among many others to enhance the service and to deal with increasing costs and growing pressures. However, process mining has not yet been used in the Egyptian organizations, whereas the process mining can enable the domain experts in many fields to achieve a realistic view of the problems that are currently happening in the undertaken field, and thus solve it. This paper presents application of the process mining techniques in the healthcare field to obtain meaningful insights about its careflows, e.g., to discover typical paths followed by certain patient groups. Also, to analyze careflows that have a high degree of dynamic and complexity. The proposed methodology starts by the preprocess step on the event logs to eliminate outliers and clean the event log. And then apply a set of the popular discovery miner algorithms to discover the process model. Then careflows processes are analyzed from three main perspectives: the control-flow perspective, the performance perspective and, the organizational perspective. That contributes with many insights for the domain experts to improve the existing careflows. Through evaluating the simplicity metric of extracted models; the paper suggested a method to quantify the simplicity metric. The paper used a dataset from a cardiac surgery unit in an Egyptian hospital. The results of the applied process mining techniques provide the hospital managers a real analysis and insights to make the patient journey easier.

Suggested Citation

  • Abdel-Hamed Mohamed Rashed & Noha E El-Attar & Diaa Salama Abdelminaam & Mohamed Abdelfatah, 2023. "Analysis the patients’ careflows using process mining," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-32, February.
  • Handle: RePEc:plo:pone00:0281836
    DOI: 10.1371/journal.pone.0281836
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    References listed on IDEAS

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    1. Catarina Moreira & Emmanuel Haven & Sandro Sozzo & Andreas Wichert, 2018. "Process mining with real world financial loan applications: Improving inference on incomplete event logs," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-31, December.
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