# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 4 Results
### Case Study  
The case study relates to ER processes within a university hospital in Santiago, Chile. The data collected correspond to July 2014. Initially, several questions were posed according to the specific needs of an ER expert who works as a member of the ER team. For example: What activities are carried out, and what processes are followed in providing attention to ER patients diagnosed with appendicitis? How long do the activities carried out last in attending to ER patients diagnosed with bronchitis? What process is executed for treating patients who are triaged red? Are there certain diagnoses that are always triaged yellow and last more than ten hours? What are the activities carried out; what are the processes followed; and how long do the activities last in terms of providing attention to ER patients diagnosed with pneumonia? What are the most commonly-requested inter-consultations in cases of a long stay, and what are the main diagnoses? The aim of the case study was to demonstrate the usefulness and applicability of the data reference model, the methodology and the use of process mining techniques to provide answers specifically to the following question: What activities are carried out, and what process is followed in providing attention to ER patients diagnosed with appendicitis? The decision to answer this question is because the episodes of appendicitis should normally follow a standard process, so we are interested in verifying, through the use of the proposed tools and methodology, whether that is effective in practice. Further research will include additional and more complex questions. The stages defined in Section 3.2 were applied over a period of three months, during which data cleansing was the most time-consuming activity. The following is a description of the tasks undertaken. **Stage 1. ** Data extraction:  
In order to answer the question, the tasks in the first stage of data extraction were executed. Data were extracted from HIS Alert ADWPhase I, which is the HIS used in the ER of the hospital in question. Subsequently, every data type was extracted by means of specific reports in CSV (comma separated value) format from the database (problems, vital signs, allergies, referrals, transportation, responsibility transfer, diagnosis, professional activities, medications, final discharge and triage). Each report comprerehends detailed information about each activity or event considered in the analysis, including: a unique episode ID, the activity name, the resource who performed the activity, a timestamp with a high degree of accuracy and, optionally, a series of attributes about the activity or event. These attributes help to better understand how each episode was performed; for example, the dosage and effects of a drug applied to a patient in an internal medication event for a given episode. Demographic information about patients was not included among these attributes, since it was not required for the analysis. Standardization tasks were performed on date formats, including: checking and establishing the desired format (e.g., dd/mm/yyyy or mm/dd/yyyy) and the spacer (e.g., - or /). In addition, simple activity columns (e.g., recording vital signs) and compound activity columns (e.g., professional task: medical doctor) may be defined and generated to improve the analysis. It is advisable to perform the data extraction with the help of the HIS owners, to make sure no data are left aside and the correct values are being extracted. Besides, it is important to validate the timestamps to check for any inconsistencies. Details, such as the date format or the spacer, can be seen as insignificant at the beginning, but they are relevant when the event log is created and uploaded into the process mining tools. **Stage 2. ** Event log creation:  
During the second stage of the methodology, an event log was extracted taking into account the specific question we want to answer. In our case, the event log was created including all ER episodes during July 2014.