# A Goal-Driven Evaluation Method Based On Process Mining for Healthcare Processes
## 3 Related Works  
PM has the potential to offer many advantages for healthcare professionals. Particularly in healthcare, where most processes are complex, variable, dynamic, and multi-disciplinary in nature, the application of this technique is growing yet challenging. In this research, first, a descriptive analysis of the PM studies on healthcare [4] was undertaken by applying systematic mapping [29]. A total of 172 relevant papers were analyzed with respect to various aspects including research and contribution type, application context and healthcare specialty, process modeling type and notation, PM activities, PM techniques, and demographic and bibliometric analysis. The results of mapping showed that, despite the challenges related to healthcare data and technique, this field is rapidly growing and open for further research and practice. The systematic mapping study also showed that PM community in the healthcare domain is lacking a comprehensive methodology that describes how to accomplish PM activities and techniques in practice. In data mining, efforts have been made to establish methodologies to support organizations in their data mining projects [30]. The aim of these methodologies is to guide the planning and execution of such projects in order to save time and cost. The commonly used methodologies include CRISP-DM [31], SEMMA [32], and KDD [33], which are very high-level and provide only little guidance support. Furthermore, none of the available methodologies have been tailored as specific to PM projects [34]. The main difference between data mining and PM methodologies concerns the analysis stage, in which PM considers the whole process in terms of events with the main objectives of process discovery, conformance checking and process enhancement, whereas data mining projects analyze data statistics to determine unsuspected correlations. During the literature review, four generic PM methodologies were identified: Process Diagnostic Method (PDM) [35], which has also been extended to the healthcare domain [9]; the L* life-cycle model [8]; PM2: Process Mining Project Methodology [34]; and Business Process Comparison Methodology [36]. PDM was designed to obtain a broad overview of processes; however, its scope is limited and it does not utilize domain knowledge. The L* life-cycle model covers different distributed aspects of PM techniques, and is designed for structured processes. PM2 guides the execution of PM projects by defining and answering questions iteratively to improve process performance. Business process comparison methodology focuses only on the analysis of multiple processes by comparing processes or process variants. In the related literature, 12 healthcare specific studies were found to have proposed processes to guide the use of PM techniques for process enhancement. Of these, four [37; 38; 39; 40] present their proposals without having them validated, and therefore do not include empirical evidence for applicability or usefulness. Question-driven methodology [41] focuses on the application of PM projects to provide answers to questions about emergency room processes. Some studies [42; 43; 44] suggest combining PM and simulation to discover and evaluate as-is and to-be processes, respectively. [PERSON] et al. [45] focus on clustering patient traces and process recommendations and [PERSON] et al. [46] emphasize deriving frequent healthcare flows and clinical data enrichment. [PERSON] et al. [19] address the gap between daily practice and clinical guidelines based on declarative process models, and [PERSON] et al. [47]provide filtering options and algorithms for process discovery, conformance, and enhancement by implementing PALIA ILS tool suite. Table 1 presents the relation of methodologies with PM features that need to be addressed for analyzing healthcare processes. Based on the findings and challenges identified in previous work [4], the current research extends PM activity types as PM features by comparing and collecting the steps of generic PM methodologies.