Process mining in healthcare: A literature review

Rojas, Eric; Muñoz-Gama, Jorge; Sepúlveda, Marcos; Capurro, Daniel

doi:10.1016/j.jbi.2016.04.007

Cited by 448 publications

(310 citation statements)

References 31 publications

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“…Those research questions represented possible healthcare analysis supported by process mining [14], [42]:…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, there is no medical subheading (MeSH) term for process mining. To be effective the search therefore used other keywords which appeared frequently in the Rojas et al review [14] and highly cited process mining literature such as [12]. An exploratory review was performed to verify relevant keywords.…”

Section: Research Methods a Search Processmentioning

confidence: 99%

“…A previous literature review was completed by Eric Rojas et al [14] on process mining in healthcare. This identified 66 papers with associated case studies which were analysed according to 11 main aspects.…”

Section: Process Mining In Oncologymentioning

confidence: 99%

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Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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Abstract-Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.

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“…Those research questions represented possible healthcare analysis supported by process mining [14], [42]:…”

Section: Resultsmentioning

confidence: 99%

Section: Research Methods a Search Processmentioning

confidence: 99%

See 1 more Smart Citation

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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“…Mining healthcare processes has been the focus of previous literature and a review of approaches is provided by Rojas et al (2016). Mans et al (2015) conclude that data mining techniques cannot be used for process discovery, conformance checking, and other forms of process analysis.…”

Section: Medical- Data-driven Clinical Pathway and Process Miningmentioning

confidence: 99%

Improving hospital layout planning through clinical pathway mining

Arnolds

Gärtner

2017

Ann Oper Res

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Clinical pathways (CPs) are standardized, typically evidence-based health care processes. They define the set and sequence of procedures such as diagnostics, surgical and therapy activities applied to patients. This study examines the value of data-driven CP mining for strategic healthcare management. When assigning specialties to locations within hospitals-for new hospital buildings or reconstruction works-the future CPs should be known to effectively minimize distances traveled by patients. The challenge is to dovetail the prediction of uncertain CPs with hospital layout planning. We approach this problem in three stages: In the first stage, we extend a machine learning algorithm based on probabilistic finite state automata (PFSA) to learn significant CPs from data captured in hospital information systems. In that stage, each significant CP is associated with a transition probability. A unique feature of our approach is that we can generalize the data and include those CPs which have not been observed in the data but which are likely to be followed by future patients according to the pathway probabilities obtained from the PFSA. At the same time, rare and non-significant CPs are filtered out. In the second stage, we present a mathematical model that allows us to perform hospital layout planning decisions based on the CPs, their probabilities and expert knowledge. In the third stage, we evaluate our approach based on different performance measures. Our case study results based on real-world hospital data reveal that using our CP mining approach, distances traveled by patients can be reduced substantially as compared to using a baseline method. In a second case study, when using our approach for reconstructing a hospital and incorporating expert knowledge into the planning, existing layouts can be improved. B Daniel Gartner

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“…Several domains have already recognised the crucial role of social media analysis in improving productivity and gaining competitive advantage. Information derived from social media has been utilised in health-care to support effective service delivery [2,3], in sport to engage with fans [4], in the entertainment industry to complement intuition and experience in business decisions [5] and in politics to track election processes, promote wider engagement with supporters [6] and predict poll outcomes. However, alongside the benefits, the rapid increase in social media spam contents questions the credibility of research based on analysing this data.…”

Section: Introductionmentioning

confidence: 99%

Detection of spam-posting accounts on Twitter

2018

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Online Social Media platforms, such as Facebook and Twitter, enable all users, independently of their characteristics, to freely generate and consume huge amounts of data. While this data is being exploited by individuals and organisations to gain competitive advantage, a substantial amount of data is being generated by spam or fake users. One in every 200 social media messages and one in every 21 tweets is estimated to be spam. The rapid growth in the volume of global spam is expected to compromise research works that use social media data, thereby questioning data credibility. Motivated by the need to identify and filter out spam contents in social media data, this study presents a novel approach for distinguishing spam vs. non-spam social media posts and offers more insight into the behaviour of spam users on Twitter. The approach proposes an optimised set of features independent of historical tweets, which are only available for a short time on Twitter. We take into account features related to the users of Twitter, their accounts and their pairwise engagement with each other. We experimentally demonstrate the efficacy and robustness of our approach and compare it to a typical feature set for spam detection in the literature, achieving a significant improvement on performance. In contrast to prior research findings, we observe that an average automated spam account posted at least 12 tweets per day at well defined periods. Our method is suitable for real-time deployment in a social media data collection pipeline as an initial preprocessing strategy to improve the validity of research data.

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Process mining in healthcare: A literature review

Cited by 448 publications

References 31 publications

Process mining in oncology: A literature review

Process mining in oncology: A literature review

Improving hospital layout planning through clinical pathway mining

Detection of spam-posting accounts on Twitter

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