Process mining for clinical pathway: Literature review and future directions

Yang, Wei; Su, Qiang

doi:10.1109/icsssm.2014.6943412

Cited by 51 publications

(31 citation statements)

References 19 publications

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“…In these papers, the variants of the clinical pathway are analysed to check the conformance of observed behaviour in the event logs with the model. Considering the techniques adopted in their selected papers, Yang and Su (2014) concluded that there are still many technical challenges for the identification and analysis of the clinical path variants. They believe that the identification and analysis methods are not always relevant and that they pay little attention to the sources and impacts of the variants, leading to low detection efficiency.…”

Section: Healthcarementioning

confidence: 99%

“…In the first area, Yang and Su (2014) analysed the difficulties raised through applying process discovery techniques in healthcare processes, reported by 19 papers. The difficulties originated form the complex and unstructured nature of healthcare processes, noise, incompleteness, multiple occurrences of activities, and richness of process types and variants, which are some major challenges of mining clinical processes.…”

Section: Healthcarementioning

confidence: 99%

“…They show that many researchers focus on the first two kinds of process mining (process discovery and conformance), but very limited studies have focused on the continuous evaluation and improvement of actual clinical pathways. According to Yang and Su (2014), the evaluation and improvement are regarded as the redesign of the clinical pathways. Nevertheless, the roots that triggered the improvement are always neglected.…”

Section: Healthcarementioning

confidence: 99%

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Process mining in healthcare: a systematised literature review

Ghasemi

Amyot

2016

IJEH

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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...

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Section: Healthcarementioning

confidence: 99%

Section: Healthcarementioning

confidence: 99%

Section: Healthcarementioning

confidence: 99%

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Process mining in healthcare: a systematised literature review

Ghasemi

Amyot

2016

IJEH

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“…Once the user provided all input parameters, Multi-CAT will process the event log executing steps 3,5,6,7,8 and 9 described in Section 5.1. If during the execution of this activity any problem is identified (e.g. duplicated criteria, invalid attribute value) then Multi-CAT will provide a list of errors to the user.…”

Section: Activity B Data Processing and Analysismentioning

confidence: 99%

“…It allows for automatically identifying process models (process discovery), recognizing the adherence in the execution of a process (conformance checking), identifying performance metrics (performance checking), besides many other applications. Process mining was applied in several research studies in health care areas Yang and Su, 2014]. For example, Augusto et al [2016] presented an automatic way to simulate and evaluate CPs.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Criteria Process Mining Optimization Tool and Its Application in a Sepsis Clinical Pathway

NEIRA¹

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A survey on recommendation in process mining

Eili

Rezaeenour

2022

Concurrency and Computation

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Recommender systems have been widely applied in several domains to make informed decisions by recommending items that might be of interest. Considering recommendation during business process execution is also highly advantageous as the efficient suggestions about possible activities or resources can impact process performance. However, the deployment of the recommendation frameworks in process mining still needs more investigations to identify the current challenges to enable the practical application of research findings and ensure a large-scale adoption of this technique. Accordingly, a systematic review is conducted to provide a taxonomy of the published studies on process-aware recommender systems based on specified criteria, including the type and perspective of recommendation, a list of datasets and evaluation metrics used in the setting of PARS, implementation environments, and different algorithms used in PARS. In this regard, there are various insights extracted from this study: (i) Most studies in the business process analysis domain are of descriptive and predictive nature, (ii) recommendation in process mining is an emerging research area that is being evolving; the majority of proposals relate to 2015 and after that, and (iii) due to the lack of common evaluation protocol, datasets, and metrics, most studies are validated through experiments and prototyping, with less tendency to the practical implementation of a solution regarding real scenarios.

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Process mining for clinical pathway: Literature review and future directions

Cited by 51 publications

References 19 publications

Process mining in healthcare: a systematised literature review

Process mining in healthcare: a systematised literature review

A Multi-Criteria Process Mining Optimization Tool and Its Application in a Sepsis Clinical Pathway

A survey on recommendation in process mining

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