Applicability of process mining in the exploration of healthcare sequences

Tóth, Krisztina; Machalik, Károly; Fogarassy, György; Vathy-Fogarassy, Ágnes

doi:10.1109/nc.2017.8263273

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…Lismont et al proposed by Lismont et al (2016), a PM framework which follows the CEDA approach reinforced with trace clustering as well, with the addition of an activity clustering task with the aim of renaming similar activities with same label so as to obtain more understandable process models. Tóth et al (2017) provided an overview of the difficulties of the application of PM in healthcare, gave recommendations for managing such problems, and suggested a CEDA‐based workflow to generate more precise process models.…”

Section: Literature Discussionmentioning

confidence: 99%

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%

Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

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“…Process mining is a collection of tools for analysing and exploiting data obtained by IT systems that support business processes. Process mining's primary objective is to automatically extract information from event-logs generated by IT systems [8], [13]. Information systems keep track of events in various formats, from plain text files to data embedded in massive database structures.…”

Section: Process Mining Background Knowledgementioning

confidence: 99%

“…Trace clustering allows the comprehension of process-models by reducing the "spaghettiness." Clustering enables an easier grasp of process models discovered by reducing "spaghettiness" [32]. Such segregation also assumes significance when dealing with vast volumes of data [27], based on the technique "divide and conquer" [33], [34].…”

Section: Trace Clusteringmentioning

confidence: 99%

Process Discovery Enhancement with Trace Clustering and Profiling

Faizan¹,

Zuhairi²,

Ismail³

2021

AETiC

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The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured process models that are hard to comprehend. Existing approaches are good at discovering and visualizing structured processes but often struggle with less structured ones. Surprisingly, process mining is most useful in domains where flexibility is desired. A good illustration is the "patient treatment" process in a hospital, where the ability to deviate from dealing with changing conditions is crucial. It is useful to have insights into actual operations. However, there is a significant amount of diversity, which contributes to complicated, difficult-to-understand models. Trace clustering is a method for decreasing the complexity of process models in this context while also increasing their comprehensibility and accuracy. This paper discusses process mining, event-logs, and presenting a clustering approach to pre-process event-logs, i.e., a homogeneous subset of the event-log is created. A process model is generated for each subset. These homogeneous subsets are then evaluated independently from each other, which significantly improving the quality of mining results in flexible environments. The presented approach improves the fitness and precision of a discovered model while reducing its complexity, resulting in well-structured and easily understandable process discovery results.

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“…Two papers [16,21] were not included in Table 6, since several hundred diseases and health problems were cited and classified using ICD-9. Of the remaining 36 case studies, ICD-10 was already used in 8 papers to code the diagnosis [12,14,21,22,33,34,38,40].…”

Section: Medical Diagnosismentioning

confidence: 99%

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Helm

Lin

Baumgartner

et al. 2020

IJERPH

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Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article.

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Applicability of process mining in the exploration of healthcare sequences

Cited by 10 publications

References 11 publications

Process mining applications in the healthcare domain: A comprehensive review

Process mining applications in the healthcare domain: A comprehensive review

Process Discovery Enhancement with Trace Clustering and Profiling

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

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