# Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining
## 3 Methods
### New Methodology
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Activity** & **Description** & **Guidelines** \\ \hline  & & - & Make sure you have permissions and access granted to them directly or through the data owner. \\
1.1: Identify available data in HIS and build the data model & Have access to the correct data from the direct sources, them being HIS or legacy systems. & - & Identify if data are missing in the data sources, and check it it is feasible to execute the analysis (e.g., timestamps are the minimum required data). \\  & & - & The available data model should contain as many dimensions and attributes of the data reference model as possible. \\  & & - & It should have as much detail granularity as possible. \\ \hline
1.2: Ensure availability of a timestamp for each event event & Check that for each event or activity included in the data model, a correct timestamp is included. & - & If different levels of accuracy are present, the highest one present in all of the data is recommended, just to have the same level across all of the examined data. \\ \hline
1.3: Name events & In case any activity or event does not have an appropriate name, one should be assigned to it. & - & Use meaningful names for the ER experts, such as “record vital signs”. \\ \hline
1.4: Create specific-fields & Create specific-fields based on the required needs. & - & It is advisable to group activities into subprocesses (e.g., group the triage activities in one subprocess, when our focus is the rest of the process). \\  & & - & It might also be useful to split activities into sub-activities (e.g., the professional activities could be split according to the role, for example professional activities, such as “physician professional activities” or “nurse professional activities”. \\ \hline
1.5: Verify data quality & Further general issues have been identified from the literature review that must be tackled when generating an event log for process mining purposes in healthcare. & - & Check lack of data, incorrect data or the inaccuracy and irrelevance of data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Guidelines for the data extraction stage. HIS, Hospital Information System. Figure 3: Proposed methodology. the presence of a timestamp, the granularity of the timestamp must also be checked, since some timestamps have a high degree of accuracy (e.g., with a precision up to seconds), whereas others have a low level of accuracy (e.g., with a precision up to hours). As a result, it is necessary to decide the desired level in order to conduct the analysis. Ideally, the timestamp with the highest level of accuracy will be used. If different levels of accuracy are present, the highest one present in all of the data is recommended, just to have the same level across all of the examined data. If some data do not have a timestamp, they cannot be used for the analysis. Check that for each event or activity included in the data model, a correct timestamp is included. This will allow the use of the event or activity in the desired analysis. Name events:  
The second challenge is to decide the explicit name used to identify each of the activities included in the event log. In the reference models, data structures are identified for each of the activities undertaken. However, at the precise moment of creating the event log, it is necessary to define a particular name for each of them, in case they do not have one already established. For example, if we decide to include the events outlined in the vital signs data structure, each event must have a particular name, such as \"record vital signs\" or \"taking vital signs\". In case any activity or event does not have an appropriate name, one should be assigned to it. The name should be establish according to the knowledge of the domain and taking into consideration the ER expert. Create specific-fields:  
Create specific fields based on the required needs. According to the available data and their level of granularity, we can build specific fields to help us through the analysis.