Literature DB >> 27010685

A process mining-based investigation of adverse events in care processes.

Filip Caron1, Jan Vanthienen1, Kris Vanhaecht2, Erik Van Limbergen3, Jochen Deweerdt, Bart Baesens1.   

Abstract

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-world clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

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Year:  2014        PMID: 27010685     DOI: 10.1177/183335831404300103

Source DB:  PubMed          Journal:  Health Inf Manag        ISSN: 1833-3583            Impact factor:   3.185


  2 in total

1.  Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol.

Authors:  Ian Litchfield; Ciaron Hoye; David Shukla; Ruth Backman; Alice Turner; Mark Lee; Phil Weber
Journal:  BMJ Open       Date:  2018-12-04       Impact factor: 2.692

2.  Modified Needleman-Wunsch algorithm for clinical pathway clustering.

Authors:  Emma Aspland; Paul R Harper; Daniel Gartner; Philip Webb; Peter Barrett-Lee
Journal:  J Biomed Inform       Date:  2021-01-27       Impact factor: 6.317

  2 in total

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