Expectations from a Process Mining Dashboard in Operating Rooms with Analytic Hierarchy Process

The wide-spread adoption of real-time location system is boosting the development of software applications to track persons and assets on real-time and perform analytics. Among the vast amount of data analysis techniques, process mining allows to conform work-flows with heterogeneous multivariate data, enhancing the model understandability and usefulness in clinical environments. However, such applications still find entrance barriers in the clinical context. In this paper we have identified the preferred features of a process mining based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. Work-flows are inferred and enhanced using process discovery on location data of patients undergoing an intervention, drawing nodes (states in the process) and transitions across the entire process. Analytic Hierarchy Process has been applied to quantify the prioritization of the features contained in the process mining dashboard (filtering data, enhancement, node selection, statistics, etc.), distinguishing on the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (N=10) was classified into three groups: Technical, Clinical and Managerial staff according to their responsibilities. Results show different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms.