This paper presents PALIA-ER, a web-based tool for questiondriven process mining in Emergency Room. PALIA-ER uses Palia discovery algorithm and includes model simplification and filtering features specially domain-specific for ER. Most PALIA-ER functionalities can be easily applied to other interdisciplinary contexts such as other healthcare units, education, or logistics.
understood by researchers not familiar with the BPM discipline (e.g., healthcare professionals), 2) process models should be able to represent in an intuitive way complex behavior typical of such interdisciplinary contexts, 3) event data and process models should be able to be filtered and aggregated to represent the process at the desired level of detail, 4) case statistics should be available in order to lead the process analysis interactively, and finally, 5) it should include domain-specific filter possibilities (e.g., in the ER case, triage, discharge destination).
This paper presents PALIA-ER, a tool for Question-driven Process Mining analysis in ER. PALIA-ER addresses all the aforementioned challenges, and provides the healthcare expert a tool with a complete set of features to analyze their own ER processes. Notice that, although PALIA-ER is ER-specific, most of the concepts and features presented in this work are easily adaptable to other interdisciplinary domains.
This article is organized as follows: Section 2 presents PALIA-ER and its main features. Two illustrative case examples using the tool are illustrated in Section 3. Finally, Section 4 concludes the article. 2
In this article we present PALIA-ER, a web-based process mining tool designed for question-driven process mining analysis of the ER domain. Figure 1 shows an overview of the tool main elements: the main center panel of the tool is
b. Activity Naming
c. Statistics
used to display the discovered process models and the process statistics; the side
bar on the left contains the menus to set the filters, model simplifications, and
discovery parameters; finally the panel at the bottom displays information about
the analyzed process. PALIA-ER has several features specially designed for ER.
In particular, the main ones are:
B Interdisciplinary Process Models and Discovery: Process Mining tools for
interdisciplinary research require process models to be easy to interpret by
actors of all disciplines. PALIA-ER uses the Palia algorithm [
B Model Simplification: PALIA-ER includes features to easily merge or remove activities in order to provide high or low level overviews of the same process, depending on the question being answered. Figure 2b partially illustrates this feature, where providing an existing alias to an activity or unchecking it collapses or hides such activity.
B ER Domain Specific Filters: PALIA-ER includes case filters based on
transversal case properties, common in other process mining tools. For example, filter
by date or filter by duration, among other examples. However, the tool also
provides easy access to ER domain specific filters. For example, filter by
triage color (i.e., the color-based priority of the ER episode assigned
during the triage phase), or filter by last discharge (i.e., the type of discharge
of the last visit, including ambulatory discharge or hospitalize discharge for
example). Other examples include filters by the general characteristics of the
patient, such as age or gender. For the sake of space, we refer the reader to
[
B Statistics: PALIA-ER provides detailed and aggregated statistics about the cases, allowing the final user to go back and forward on an interactive analysis of the process, filtering such cases not relevant for answering the current question. Figure 2c partially illustrates this feature.
To demonstrate the use of PALIA ER, a screencast with a walk-through of the tool can be found at http://pmuc.ing.puc.cl/tools/paliaer/. 3
In this section we illustrate the applicability and potential impact of the tool,
by applying it to answer two frequently asked questions by experts in the ER,
following our methodology [
Q1: What is the followed process by all the ER episodes? : The first question relates to the general view of the ER unit. In order to analyze the high-level view, the grouping feature shown in Figure 2b is used to group all the activities in 5 main ER stages (Triage and Diagnostic, Examination, Referral, Treatment and Discharge). Then, the relations between the ER stages and the start and end points of the process are analyzed. The resulting process model can be seen in Figure 3. A more detailed analysis concludes that significant interactions are been held between the Triage and Diagnostics, Examinations and Treatment stages, followed by the Discharge stage and finally, with less interaction, the Referral stage.
Q2: What is the followed process for patients over 70 years? : Regarding the analysis necessary to answer Q2: First, the data was filtered according to the age of the patient, selecting only patients with 70 years or more. The discovered process model can be seen in Figure 4. When we analyze the statistics of these episodes (cf. Figure 4), we discover one particular characteristic: hospitalization is the highest destination after the patient has been treated in the ER. A more detailed analysis concludes that in these cases, hospitalization is higher than the typical episodes studied in Q1. 4
This paper presented PALIA-ER, a tool for a question-driven process mining for the ER domain. Two conclusions arise: First, the tool can help reduce the dependency on the process mining expert from the clinicians looking for solutions to analyze their processes. And second, even though the PALIA-ER solution is ER-specific, it could potentially be applied to other interdisciplinary domains.