Process mining is a promising approach that turns event logs into valuable insights about processes. One domain amenable to process mining is healthcare, where an enormous amount of data is generated by care processes, but where realistic care models are seldom available. In this paper, we perform a systematised literature review to assess the status of process mining, particularly in healthcare. We first provide an overview of process mining in general, and in healthcare in particular. Based on 2371 research publications related to process mining, obtained by querying six relevant search engines in May 2016, we found that the trend of publications in this domain has been growing over the past decade, especially in healthcare. Among the eleven existing literature reviews on process mining selected for further analysis, only two are systematised, and only three relate to healthcare. This paper contributes a systematised review in healthcare that is much needed to fill this void. Important challenges specific to healthcare are identified, and threats to the validity of the results are also discussed.Keywords: Process mining, healthcare, care processes, clinical pathways, literature review IntroductionThe number of processes whose event logs are being recorded is highly increasing. Process mining is a promising approach that can use these logs and turn them into valuable insights about processes. In particular, process mining plays an important role as a bridge between traditional model-based process analysis (e.g., simulation) and data analysis techniques (e.g., data mining). This leads into a huge demand for data scientists who are not only able to analyse big data, but also to relate them to real operational processes. According to van der Aalst (2011), process mining techniques are classified into three categories: i) discovery, where a model is being created using the event logs; ii) conformance, where the data generated from the model is compared with the actual data in event logs to compare the model with reality; and iii) enhancement, where the desired data is used to improve or/and extend an existing process model.One of the domains amenable to process mining is healthcare. In this domain, an enormous amount of data is being generated by care processes, but care models that reflect the reality are seldom available. On the other hand, healthcare expenditure is consistently rising (independently of outcomes and countries) and on average it amounts to 10% of the gross domestic product (GDP) of countries across the world (The World Bank, 2015). It is not surprising to see that the demand for high quality care at low cost is increasing, especially with our aging society. Consequently, healthcare service providers are highly motivated to use their data to improve the quality and performance of their care processes and lower their costs. Process mining is an approach that promises to support the analysis and understanding of such processes. 2Process mining is a broad area of literature that received eno...
Process Mining is an approach that uses event logs of systems or processes and turns them into valuable insights. The main characteristic of process mining techniques is that they focus on and exploit "real behavior" of a large number of stakeholders of a system or of a process. On the other hand, requirements engineering is concerned with requirements elicitation and analysis not only in terms of software specifications but also in terms of activities carried out within an organizational and social context. Furthermore, involving a large number of users/stakeholders has always been a challenge with traditional requirements engineering methods. Although both requirements engineering and process mining have gained increasing research attention, the synergy between these two domains is yet to be exploited. Such a synergy can help both domains benefit from their capabilities and mitigate their own challenges. The ability of process mining to exploit huge data logs can help requirements engineers cope with the above challenge. This paper aims to highlight how requirements engineering can benefit from process mining's components such as execution logs, process discovery and conformance techniques for requirements elicitation, prioritization and validation.
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