Improving structural medical process comparison by exploiting domain knowledge and mined information
Introduction
Process model comparison and similar process retrieval is a key issue to be addressed in many real-world situations. For example, when two companies are merged, process engineers need to compare processes originating from the two companies, in order to analyze their possible overlaps, and to identify areas for consolidation. Moreover, large companies build over time huge process model repositories, which serve as a knowledge base for their ongoing process management/enhancement efforts. Before adding a new process model to the repository, process engineers have to check that a similar model does not already exist, in order to prevent duplication. Particularly interesting is the case of medical process model comparison, where similarity quantification can also be exploited in a conformance checking perspective. Indeed, the process model actually implemented at a given healthcare organization can be compared to the existing reference clinical guideline, to check conformance, and/or to understand the level of adaptation to local constraints that may have been required. As a matter of fact, the existence of local resource constraints may lead to differences between the models implemented at different hospitals, even when referring to the treatment of the same disease (and to the same guideline). A quantification of these differences (and maybe a ranking of the hospitals derived from it) can be exploited for several purposes, like, e.g., administrative purposes, performance evaluation and public funding distribution. The actual medical process models are not always explicitly available at the healthcare organization. However, a database of process execution traces (also called the “event log”) can often be reconstructed starting from data that hospitals collect through their information systems (in the best case by means of workflow technology).
In this case, process mining techniques [1] can be exploited, to extract process models from event log data. Stemming from these considerations, in this work we present a framework, which allows the user to:
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extract the actual process model from the available medical process execution traces, through process mining techniques;
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perform medical process model comparison, to fulfill the objectives described above.
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exploiting domain knowledge;
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exploiting process mining outputs and statistical temporal information.
Section snippets
Methods
In this section, we will first introduce process ming and the ProM tool; then we will provide the technical details of our metric.
Results
We have applied our framework, which is implemented in Java3, to stroke management processes. A stroke is the rapidly developing loss of brain function(s) due to disturbance in the blood supply to the brain. This can be due to ischemia (lack of glucose and oxygen
Comparison to related works
Graph representation and retrieval is a very active research area, which is giving birth to different methodological approaches and software tools. Graph databases, like, e.g., HypergraphDB [16] and DEX [17], are gaining popularity, for working in emerging linked data such as social network data and biological data. However, in this section we will focus on contributions that are more closely related to comparison and retrieval in process/workflow management research. As clearly stated in [9],
Discussion on limitations and future research directions
Despite the novelty of our approach, discussed in Section 4, and despite the encouraging experimental results presented in Section 3, we wish to point out some limitations of the current version of our work, that will guide us in the choice of future research directions. Namely, the following issues still need to be managed:
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in distance calculation, we currently simplify the control flow information of the mined models, by simply considering sequence, and ignoring AND/OR splits and joins. In the
Conclusions
This work showed that process mining and process comparison can be applied successfully to clinical data to gain a better understanding of medical processes. It is interesting to analyze the differences, to establish whether they concern only the scheduling of the various tasks or also the tasks themselves. In this way, not only may different practices that are used to treat similar patients be discovered, but also unexpected behavior may be highlighted. Experimental results have shown the
Acknowledgments
This research is partially supported by the GINSENG Project, Compagnia di San Paolo. We would like to thank Dr. I. Canavero for the independent evaluation of process distance.
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2020, Artificial Intelligence in MedicineCitation Excerpt :In this respect, Mans et al. [58] compare stroke treatment in two different processes. The same context, stroke treatment, is also considered by Montani et al. [59] to demonstrate a general comparison technique for clinical processes. Partington et al. [60] consider patients presenting themselves with acute coronary syndrome symptoms at the emergency department of four Australian hospitals.
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2018, Journal of Biomedical InformaticsCitation Excerpt :Section 4 shows the experimental results on a clinical data set with 48,024 CVD patients, and finally, Section 5 concludes the work. Process mining is a research discipline that focuses on providing evidence-based analysis for effective business process management [8,25,26]. Shifting to clinical settings, applications that employ process mining techniques to routinely collected clinical data can enable healthcare stakeholders to empirically investigate treatment behaviors as they are delivered by different health providers [7].
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On behalf of the Stroke Unit Network (SUN) collaborating centers.