# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 1 Introduction  
The Emergency Room (ER) has become one of the most significant first-contact points with the healthcare system [1]. ER must provide the required services to screen, examine and provide care to patients in the most effective way. This has led to increased efforts to improve the service levels, to reduce overcrowding and to provide prompt and efficient care [2]. With these efforts comes a series of questions about what is the best way to make this happen, especially about how to improve ER processes. Based on the knowledge and use of historical information related to process execution within ER, it is possible to provide answers to a number of questions frequently posed by experts in the field. Examples of frequently-posed questions in the ER field include: What exactly generates the bottlenecks that lead to increased waiting times? What do cases in which patients have to endure long waiting times have in common? Are patients being attended to in line with established protocols? Why are there delays in the hospitalization of patients? In the past, multiple techniques have been used inorder to provide answers to such questions, as well as to obtain further knowledge about ER processes, such as business process redesign [3], evidence-based medicine [4; 5] and lean [6], among others. Our approach centers on the use of process mining as the main component for responding to the questions posed by the experts. Process mining is a relatively young research discipline that focuses on extracting knowledge from data generated and stored in the databases of (corporate) information systems; in this case, the Hospital Information Systems (HIS). In turn, process execution data are extracted as event logs. An event log can be viewed as a set of traces (also known as cases, or in the emergency room, episodes), each containing all of the activities executed for a particular process instance. Process mining has been applied to Healthcare (HC) in the past, giving rise to a number of significant advantages [7]. For example, it helps to identify and to understand which process is followed in a specific medical procedure (e.g., during surgery [8; 9], cardiovascular disease management [10] or during the treatment of cancer patients [11]); it helps to clarify the social relationships between the actors involved in the process (e.g., task delegation or collaboration patterns [12]); and it enables experts to verify levels of compliance with internal or external guidelines [13]. However, since process mining is an emerging discipline, there are still a number of limitations to its application, including the limited implementation of HIS that are process aware and that record event logs, as well as the difficulties involved in data extraction, the limited interpretation of data to respond to questions frequently posed by experts, the lack of methods for responding to the questions and the high dependence of experts on the process mining discipline, among others [7]. Previously, data from ER have been used to conduct three case studies that involve the application of process mining [14; 15; 16]. The case studies have provided information regarding the flow of executed activities (e.g., triage and examinations carried out), the relationships between available resources and the identification of opportunities to reduce waiting times for treatment. In the first case study, undertaken in Portugal [14], a methodology was proposed, through the use of clustering techniques, to help generate simple process models. The models provide insight into the control flow of healthcare processes, their performance and their adherence to institutional guidelines. The methodology provides a series of steps, which should be followed. However, the methodology is not specific for ER, it depends on clustering techniques, and it fails to provide solutions to ER data management. Furthermore, its implementation relates to a suite that is specific to the needs of the particular hospital in which the study was conducted.