# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 3 Methods
### Process discovery:
It is also possible to check whether or not there is compliance with internal or external guidelines. The authors recommend using algorithms based on conformance alignments in cases where optimal results are desired [37]. If only an exploratory conformance is desired, it is possible to choose conformance based on replay [38]. The algorithms are implemented in tools, such as ProM or CoBeFra. **Activity 5.4. **: Performance analysis:  
Performance analysis is an analysis conducted from the time perspective, which takes into account the data regarding activity durations and waiting times between activities. This type of analysis is able to identify bottlenecks, activities that take longer than expected, excessive waiting times or slow synchronizations. The authors recommend the use of algorithms based on token replay over the model, which are able to obtain performance statistics and annotated Petri net models. The algorithms are implemented in tools, such as ProM. **Activity 5.5. **: Organizational analysis:  
Finally, organizational analysis focuses on the resources perspective and how people behave during the execution of process activities. For example, it identifies who performs each task and how resources interact during a case execution. The authors recommend the use of the organizational metrics implemented in ProM (e.g., working together or handover of work) in order to obtain organizational models. **Activity 5.6. **: Analysis regarding each type of question:  
Section 2 provides a classification of FPQ. For each of them, one or several types of techniques can be applied. For example, some FPQs may need to use discovery techniques to obtain process models, while others may need to use conformance checking techniques to verify conformance. Table 6 provides a general guide of what analysis techniques should be used for each FPQ. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Question** & **Analysis** \\ \hline General Discovery Questions & Heuristics miner algorithm [33], genetic miner algorithm [35] and inductive miner algorithm [36]. \\ \hline General Conformance Questions & Conformance checking and replay [39]. \\ \hline General Performance Questions & Performance analysis technique [38]. \\ \hline General Organizational Questions & Organizational metrics, such as handover of work, doing similar tasks, working together and subcontracting [40]. \\ \hline Episode Triage Category Questions & Classify and divide the episodes according to the triage categories. Discover a process model for each of the categories and execute the analysis according to the required characteristics of the episode. \\ \hline Episode Duration Category Questions & Apply clustering techniques to classify the episodes according to their duration characteristics (time attributes), and afterwards, apply discovery or performance analysis techniques. \\ \hline Episode Discharge Destination & Classify and divide the episodes according to the episode discharge destination categories. Discover a process model for each of the categories, and execute the analysis according to the required characteristics of the episode. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis guide for each type of question. **Activity 5.7**. : Data analysis and process mining cycle:  
Data analysis and process mining analysis stages are introduced as a cycle, since, in order to obtain the necessary results for responding to certain questions, a continuous iteration is required for refining the data and the results until the desired answers are obtained. For example, process mining discovery techniques are used at the beginning of an analysis to create a process model with a complete event log that includes all activities. These activities have characteristics (e.g., triage color or diagnosis), which help to undertake statistical analyses of the data and filter the event log, as required. The new event log includes only the desired episodes, and during the process mining stage, it helps to create a new model.