Applying PM4ILP to Identify Loosely or Unstructured Parts in the COVID-19 Patient Treatment Process

Abstract

This paper presents the application of Process Mining for Identifying Loosely processes (PM4ILP), designed to facilitate the discovery, modeling and improvement of unstructured and/or loosely processes, to the process of treating COVID-19 patients. By following the phases of the cycle and using process mining techniques, we analyzed the behavior of the COVID-19 process, with the aim of improving its effectiveness and efficiency. This was done by collecting real data and applying the phases of the cycle to facilitate the identification of the type of the process and represent improvements on its structure. Our results demonstrate the adaptability and effectiveness of the PM4ILP approach in identifying loosely/unstructured processes and optimizing their quality specifically in critical domains such as healthcare. In addition, we were able to highlight the benefits of PM4ILP, including its ability to facilitate the discovery phase, and the continuous improvement of a process.