# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 1 Introduction
The second case was an exploratory study undertaken using data from ER processes from four hospitals in Australia [15]. It identified the process from each respective hospital and subsequently compared the results, highlighting the areas in which processes were executed differently. As part of this comparative study, the steps followed for the creation of the event logs used were described. However, it failed to generate either a detailed method or one specific for ER that might be reused in other medical centers. Both cases illustrate the problems in dealing with complex and interlaced process models, called spaghetti models, and the need to work closely with experts from both the medical and process mining fields. The third case [16] refers to a study into the process of pediatric patients with asthma attended to in ER, using a visualization tool developed in a hospital in the United States. This case study identified the process model followed for attending to patients. However, it failed to specify a formal method with which to conduct the study, and it does not detail how the method might be replicated in other areas of ER. As a result of the literature review conducted [7] and previously undertaken case studies, the need to resolve four important requirements has been identified. First, a methodology based on process mining that provides answers to questions frequently posed by ER experts is needed. An ER expert is a professional that works in ER and has knowledge about how ER processes are performed; some roles that are usually included in this group are physicians, nurses, technicians and administrative resources, among others. Previous case studies have merely defined mechanisms for obtaining simple process models, leaving the inclusion of ER experts in the field as work to be conducted in the future. Second, data reference models that represent the data from ER processes are required in order to ensure that data are stored in a structured manner. Such models need to be process-aware and able to facilitate the creation of event logs. Third, there is a need to reduce the spaghetti effect when discovering ER process models, through the use of a methodology that is driven by specific ER questions. Finally, the fourth is the need to establish methods to apply process mining and data analysis techniques in flexible environments, such as the emergency room. To fulfill these four requirements a methodology is proposed. This methodology uses both process mining and data analysis techniques, to provide answers to the questions frequently posed by experts, using the data stored in a process-oriented data reference model. The methodology will provide the guide, the data reference model will provide the data structures, and the frequently posed questions will help reduce the spaghetti effect. ER processes are intrinsically flexible, since they must adapt to the particular characteristics of each patient. This flexible nature is evidenced through the presence of typical and atypical behavior in the ER [17]. However, there have been attempts to establish certain guidelines on how to treat patients, for example by creating guidelines to address specific diagnoses [18]. Our methodology deals with the flexible nature of the ER processes by identifying Frequently-Posed Questions (FPQs) that will guide the different stages of the methodology: which data must be extracted, the data model to be used, the building of the event log and the analysis to be performed. Moreover, episode filtering will help to reduce the event logs to only include the behavior that is desired to be studied for each FPQ. The structure of the article is as follows: Section 2 defines the questions frequently posed by experts in ER. Section 3 describes the proposed methodology and the proposed data reference models. Section 4 describes a case study in which the methodology has been put into practice. Section 5 provides a discussion of the results obtained, and the article culminates with the conclusions and future work of the authors in Section 6.