A Finite-State Machine Approach to Study Patients Dropout From Medical Examinations

Abstract

With the burden of COVID-19, serious lacks in health facilities have emerged and novel solutions have been proposed to redesign the organizational model of healthcare systems. In particular, reduction in hospital occupancies has been recommended in order to avoid overcrowding of the facility and prevent patients from leaving without being seen (LWBS) by medical doctors. In this context, the use of simulation models could be an effective tool to test what-if scenarios and build more robust and resilient models of healthcare processes. In this work, we take advantage of the power discrete event systems theory to design a finite-state machine for monitoring the patient flow during a generic care process and to study the LWBS phenomenon by assessing the dropout rate as a function of both the healthcare service performance and the crowding of the department. The patient's behavior and process dynamics are tested under different simulated scenarios. The proposed methodological approach could provide a generalized framework for the design of novel healthcare organizational models as well as real-time systems for monitoring crucial healthcare performance indicators, such as overcrowding and LWBS rate.