An approach to fuzzy process mining to reduce patient waiting time in a hospital

Healthcare sector is constantly discovering new process and advanced method to develop operational ability. This research study delves into a new method to discover possible productivity improvements in healthcare sector by detecting exactly how they are approved in the earlier processes and then studying enhanced techniques of implementation by considering the factors like time, cost and resource utilization. To achieve competitive advantage, healthcare centers crack and outline their processes. The objective of the process mining is able to propose effective process models by applying on a dataset, for each model which easily identifies the deviation from the actual process by the support of analytical instrument ProM Lite also to understand the deviations and bottleneck we will analyses process model. In this paper, computed tomography (CT) tests are considered as the baseline scenario wherein effective process models are generated and checked for the efficiency using fuzzy process mining. The fitness of the process model can be understood by applying event log with Petri-Net called conformance checking and enhancement of the model by understanding the deviations and time intervals and propose an best fit model for the future implementation in a hospital for the best and effective utilization of the physical resources and try to decrease the waiting time of the patient.