Supporting healthcare management decisions via robust clustering of event logs

Delias, Pavlos; Doumpos, Michael; Grigoroudis, Evangelos; Manolitzas, Panagiotis; Matsatsinis, Nikolaos F.

doi:10.1016/j.knosys.2015.04.012

Cited by 81 publications

(53 citation statements)

References 35 publications

(34 reference statements)

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“…Pavlos Delias et al, [20] supported decision-making for flexible environments with help of logical process models. A mining technique was proposed as a technique to cluster customer flow and thereby create effective summarization.…”

Section: Literature Reviewmentioning

confidence: 99%

Enriching Medicare Severity-Diagnosis Related Group (MS-DRG) Payments for better Service to inpatients using ANFIS

Chimwayi¹,

Haris²,

Caytiles³

et al. 2017

IJHIT

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Section: Literature Reviewmentioning

confidence: 99%

Enriching Medicare Severity-Diagnosis Related Group (MS-DRG) Payments for better Service to inpatients using ANFIS

Chimwayi¹,

Haris²,

Caytiles³

et al. 2017

IJHIT

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show abstract

“…Until today, the studies on patient-flow through process mining have mainly focused on medical and methodological aspects (e.g. Bose and Van der Aalst, 2011;Huang et al, 2012;De Weerdt et al, 2013;Mans et al 2013;Delias et al, 2015;Rovani et al, 2015).…”

Section: Patient-flow and Healthcare Processmentioning

confidence: 99%

“…Other researchers have focused their studies on proposing innovative methodologies and/or new algorithms (e.g. Huang et al, 2012;Rebuge and Ferreira, 2012;Delias et al, 2015).…”

Section: Process Mining In Healthcarementioning

confidence: 99%

Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining

Stefanini

Aloini

Dulmin

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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The knowledge of patient-flow is very important for healthcare organizations, because strongly connected to effectiveness and efficiency of resource allocation. Unfortunately, traditional approaches to process analysis are scarcely effective and low efficient: they are very time-consuming and they may not provide an accurate picture of healthcare processes. Process mining techniques help to overcome these problems. This paper proposes a methodology for building a DRG related patient-flow using process mining. Findings show that it is possible to discover the different sequences of activities associated with a DRG related process. Managerial implications concern both process identification, analysis and improvement. A case study, based on a real open data set, is reported

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“…Then they use the k-means algorithm to separate the control-flow behaviors. The same similarity criteria are also used in [10] to form a similarity matrix for traces. After applying a technique that relegates the most dissimilar traces, a spectral clustering method is applied to deliver clusters with more related behaviors.…”

Section: Related Workmentioning

confidence: 99%

Discovering Characteristics that Affect Process Control Flow

Delias

Grigori

Mouhoub

et al. 2015

Decision Support Systems IV – Information and Knowledge Management in Decision Processes

Self Cite

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Abstract. In flexible environments like healthcare and customer service, business processes are executed with high variability. Often, this is because cases' characteristics vary. However, it is difficult to correlate process flow with characteristics because characteristics may refer to different perspectives, their number can be real big or even because deep domain knowledge may be required to state hypotheses. The goal of this paper is to propose an effective exploratory tool for discovering the characteristics that are causing the process variation. To this end, we propose a process mining approach. First, we apply a clustering approach based on Latent Class Analysis to identify subtypes of related cases based on the case-wise process characteristics. Then, a process model is discovered for each cluster and through a model similarity step, we are able to recommend the characteristics that mostly diversify the flow. Finally, to validate our methodology, we applied it to both simulated and real datasets.

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Supporting healthcare management decisions via robust clustering of event logs

Cited by 81 publications

References 35 publications

Enriching Medicare Severity-Diagnosis Related Group (MS-DRG) Payments for better Service to inpatients using ANFIS

Enriching Medicare Severity-Diagnosis Related Group (MS-DRG) Payments for better Service to inpatients using ANFIS

Linking Diagnostic-Related Groups (DRGs) to their Processes by Process Mining

Discovering Characteristics that Affect Process Control Flow

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