Integrated Declarative Process and Decision Discovery of the Emergency Care Process
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Integrated Declarative Process and Decision Discovery of the Emergency Care Process Steven Mertens 1
&
Frederik Gailly 1 & Diederik Van Sassenbroeck 2 & Geert Poels 1
Accepted: 11 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Deviations and variations are the norm rather than the exception in medical diagnosis and treatment processes. Physicians must leverage their knowledge and experience to choose an appropriate variation for each patient. However, this knowledge and experience is often tacit. Process modeling offers a way to convert tacit to explicit knowledge. Many process mining techniques have been developed due to the difficulty of doing this manually, yet, they often neglect the decisions themselves, and these proposed techniques are just one piece of a comprehensive process discovery method. In this paper, we use the Action Design Research methodology to develop a method for process and decision discovery of medical diagnosis and treatment processes. The method was iteratively improved and validated by applying it to a practical setting, which was the emergency medicine department of a hospital. An analysis of the resulting model shows that previously tacit knowledge was successfully made explicit. Keywords Process discovery . Process mining . Decision mining . Knowledge extraction . Healthcare . Knowledge-intensive
1 Introduction Within the Information Systems domain it is generally accepted that a process-oriented approach centered around process modeling results in efficiency gains, efficacy gains and/or cost reduction (Dijkman et al. 2016). Typical examples are applications in sectors such as manufacturing (Hertz et al. 2001), sales (Kim and Suh 2011) and software development (Krishnan et al. 1999). However, the healthcare sector is one the exceptions (Lenz and Reichert 2007; Palvia et al. 2012). This is surprising because some of the main concerns trending
* Steven Mertens [email protected] Frederik Gailly [email protected] Diederik Van Sassenbroeck [email protected] Geert Poels [email protected] 1
Faculty of Economics and Business Administration, Ghent University, Tweekerkenstraat 2, 9000 Ghent, Belgium
2
Department of Emergency Medicine, AZ Maria Middelares, Buitenring Sint-Denijs 30, 9000 Ghent, Belgium
in eHealth are very similar to those of these other sectors, namely, cost reduction, efficiency and patient orientation (Payton et al. 2011). Healthcare processes can be subdivided in two groups of processes: medical diagnosis/treatment processes and organizational/administrative processes (Lenz and Reichert 2007). The slow adoption of process modeling in healthcare is primarily manifested for the medical diagnosis and treatment processes. These processes typically represent the extreme end of the complexity spectrum for processes, hypercomplexity (Klein and Young 2015), and can be characterized as dynamic, multi-disciplinary, loosely framed, human-centric and knowledge-intensive processes (Mertens et al. 2017; R
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