# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 3 Methods
### Process discovery:  
Process discovery is aimed at discovering a process model based on an event log, in which the resulting model includes the activities and paths taken in different cases. Given the flexible nature of ER processes, in which two episodes are never the same, when dealing with questions related to control-flow analysis, we recommend the use of models with more flexible semantics, such as dependency graphs or fuzzy models, as well as the following discovery algorithms, heuristic miner [33] and fuzzy miner [34]. Alternatively, some questions may require models with a more formal semantic (e.g., Petri nets or process trees) and their associated algorithms (e.g., genetic miner [35] or inductive \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline
**Activity** & **Description** & **Guidelines** \\ \hline
5.1: Identifying the appropriate tool & Select appropriate tools that include the methods and algorithms to execute the desired analysis. & - Identify the available tools, including licensing issues, input and output capacities. - Identify the process mining methods each tool provides. - Each type of analysis may provide different types of data, information, models and results, so it is important to study the desired methods and algorithms to have a clear knowledge of the resulting outputs. & - Understand what is the meaning of each event or activity present in each episode. \\
5.2: Process Discovery & Aimed at discovering a process model based on an event log. & - Have knowledge about the applied algorithm, to understand the correct meaning of its inputs and outputs. - Create process models at different levels of granularity. - Consider analyzing different episode stages (sub-processes) independently. \\ \hline
5.3: Conformance Analysis & Aimed at verifying conformance between a given ideal model and the actual execution as contained in the event log. & - Conformance techniques are complex, so first have a high level of understanding of how the techniques work; this way, the results will be understood and explained accurately when answering the FPO. Carefully match the name of events in the ideal model and the event log. & - Canfor example be used. \\
5.4: Performance Analysis & Aimed at analyzing data regarding activity durations and waiting times between activities. & - It is vital to have the highest level of granularity when executing this analysis. This way, more exact results will be obtained. \\ \hline
5.5 Organizational Analysis & Focuses on the resources perspective and how people interact during the execution of process activities. & - According to the FPO, the level of analysis should be defined. It could be at the level of resources, at the level of roles or even at the level of teams or work groups. \\ \hline
5.6: Analysis regarding each type of question & According to the type of FPO, specific techniques may be applied. & - It is not necessary to executed all of the analysis in the same tool. Different tools may be combined to obtain better models or a deeper analysis. \\ \hline
5.7: Data analysis and process mining cycle and and the results. & In order to obtain the necessary results to certain questions, a continuous iteration is required for refining the data and the results. & - Several iterations may be done in order to get the exact answer. New iterations may include filtering or modifying the event log, adding new data, applying new filters or incorporating new methods. \\ \hline \end{tabular}
\end{table} 
Table 5: Guidelines for the process mining stage. miner [36]) in order to verify conformance. Disco focuses on non-formal semantic models, while ProM includes models and algorithms with more formal semantics. **Activity 5.3. **: Conformance analysis:  
Conformance analysis is aimed at verifying conformance between a given ideal model and the actual execution that is contained in the event log. It is significant because it is able to detect whether the process is being run as expected by the model.