Application of process mining in the management of inpatient analysis

Abstract

The classification of the care pathway is able to define the clinical evolution of patients during hospitalization. It can also find the departments that are more stressed in terms of number of events or patients with complex clinical pictures. By analysing these aspects, it is possible to suggest improvements to the healthcare setting, making the health service provided more efficient. By applying the techniques of Process Mining it is possible to model the evolution of the complexity of inpatient management. In this paper, we present an application of some techniques of Process Mining starting from the data collected at Policlinico Universitario Campus Bio-Medico di Roma composed of anonymized records of patients in the period between 01/01/2016 and 31/12/2017. The patients and thus their hospitalization are described through a patient status grid, according to their level of autonomy, cognitive stability and clinical stability. This measure is used as an indirect measure of the complexity of inpatient management. The data were made compliant with the typical structure of an event log, and then the complexity of care of patients admitted to the facility was modelled, analyzing even the most stressed departments. The proposed approach suggests important information for the healthcare setting, ensuring an improvement of the services provided.