Evaluating the Effectiveness of Interactive Process Discovery in Healthcare: A Case Study

Benevento, Elisabetta; Dixit, Prabhakar M.; Sani, Mohammadreza Fani; Aloini, Davide; Aalst, Wil M. P. van der

doi:10.1007/978-3-030-37453-2_41

Cited by 16 publications

(13 citation statements)

References 23 publications

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“…An automated discovery task makes use of a process discovery algorithm which takes as input the event log and outputs a process model in terms of a process modeling language, without using any a priori knowledge. An interactive discovery task, in addition, takes advantage of a domain expert to discover the process model, exploiting his domain knowledge along with the event log (Benevento, Dixit, et al, 2019). Table 1 provides a quick overview of the work divided according with the above classification.…”

Section: Literature Discussionmentioning

confidence: 99%

“…An interactive discovery task, in addition, takes advantage of a domain expert to discover the process model, exploiting his domain knowledge along with the event log (Benevento, Dixit, et al, 2019). Table 1 provides a quick overview of the work divided according with the above classification.…”

Section: Process Discoverymentioning

confidence: 99%

“…Xu et al, 2017;Lee & Rismanchian, 2018;Zhang & Chen, 2012; Automated with clustering (deToledo et al, 2019;Delias et al, 2014Delias et al, , 2015Huang et al, 2014;Kovalchuk et al, 2018;Lakshmanan et al, 2013;Lismont et al, 2016;Najjar et al, 2018;Pebesma et al, 2019;Prokofyeva & Zaytsev, 2020;Ronny et al, 2015;Lu et al, 2019;S. Yang, Tao, et al, 2018) Interactive(Benevento, Dixit, et al, 2019;Valero-Ramon, Fernandez-Llatas, Martinez-Millana, & Traver, 2020;Valero- Ramon, Fernandez-Llatas, Valdivieso, & Traver, 2020)…”

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confidence: 99%

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Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

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Process mining (PM) is a well-known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success.

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Section: Literature Discussionmentioning

confidence: 99%

Section: Process Discoverymentioning

confidence: 99%

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confidence: 99%

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Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

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“…A case study illustrating such situation is e.g. the work of Benevento et al [9]. While filtering out missing values is straightforward, various methodologies of event log filtering have been proposed in the past to solve the problem of incorrect event attributes: the filtering can take place thanks to a reference model, which can be given as process specification [31], or from information discovered from the frequent and well-formed traces of the same event log; for example extracting an automaton from the frequent traces [14], computing conditional probabilities of frequent sequences of activities [25], or discovering a probabilistic automaton [33].…”

Section: Event Data Uncertaintymentioning

confidence: 99%

Conformance Checking over Uncertain Event Data

Pegoraro,

Uysal,

van der Aalst

2020

Preprint

Self Cite

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Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper, we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. We define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained aligning an uncertain trace onto a regular process model.

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“…Several studies indicate that exploiting domain knowledge within (semi-) automated process discovery leads to better process models [6,4]. Recent work has proposed the tool ProDiGy [5], allowing the user to interact with automated process discovery.…”

Section: Introductionmentioning

confidence: 99%

Cortado---An Interactive Tool for Data-Driven Process Discovery and Modeling

Schuster,

van Zelst,

van der Aalst

2021

Preprint

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Process mining aims to diagnose and improve operational processes. Process mining techniques allow analyzing the event data generated and recorded during the execution of (business) processes to gain valuable insights. Process discovery is a key discipline in process mining that comprises the discovery of process models on the basis of the recorded event data. Most process discovery algorithms work in a fully automated fashion. Apart from adjusting their configuration parameters, conventional process discovery algorithms offer limited to no user interaction, i.e., we either edit the discovered process model by hand or change the algorithm's input by, for instance, filtering the event data. However, recent work indicates that the integration of domain knowledge in (semi-)automated process discovery algorithms often enhances the quality of the process models discovered. Therefore, this paper introduces Cortado, a novel process discovery tool that leverages domain knowledge while incrementally discovering a process model from given event data. Starting from an initial process model, Cortado enables the user to incrementally add new process behavior to the process model under construction in a visual and intuitive manner. As such, Cortado unifies the world of manual process modeling with that of automated process discovery.

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Evaluating the Effectiveness of Interactive Process Discovery in Healthcare: A Case Study

Cited by 16 publications

References 23 publications

Process mining applications in the healthcare domain: A comprehensive review

Process mining applications in the healthcare domain: A comprehensive review

Conformance Checking over Uncertain Event Data

Cortado---An Interactive Tool for Data-Driven Process Discovery and Modeling

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