  Process mining can help acquire insightful knowledge and heighten the
system's performance. In this study, we surveyed the trajectories of 1050
sepsis patients in a regional hospital in the Netherlands from the registration
to the discharge phase. Based on this real-world case study, the event log
comprises events and activities related to the emergency ward, admission to
hospital wards, and discharge enriched with data from lab experiments and
triage checklists. At first, we aim to discover this process through Heuristics
Miner (HM) and Inductive Miner (IM) methods. Then, we analyze a systematic
process model based on organizational information and knowledge. Besides, we
address conformance checking given medical guidelines for these patients and
monitor the related flows on the systematic process model. The results show
that HM and IM are inadequate in identifying the relevant process. However,
using a systematic process model based on expert knowledge and organizational
information resulted in an average fitness of 97.8%, a simplicity of 77.7%, and
a generalization of 80.2%. The analyses demonstrate that process mining can
shed light on the patient flow in the hospital and inspect the day-to-day
clinical performance versus medical guidelines. Also, the process models
obtained by the HM and IM methods cannot provide a concrete comprehension of
the process structure for stakeholders compared to the systematic process
model. The implications of our findings include the potential for process
mining to improve the quality of healthcare services, optimize resource
allocation, and reduce costs. Our study also highlights the importance of
considering expert knowledge and organizational information in developing
effective process models.
