Kurniati, AP, Rojas, E, Zucker, K et al. (3 more authors) (2021) Process Mining to Explore Variations in Endometrial Cancer Pathways from GP Referral to First Treatment. In: Mantas, J, Stoicu-Tivadar, L, Chronaki, C, Hasman, A, Weber, P, Gallos, P, Crișan-Vida, M, Zoulias, E and Chirila, OS, (eds.) Studies in Health Technology and Informatics. Studies in Health Technology and Informatics, 281 . IOS Press , Netherlands , pp. 769-773. ISBN 9781643681849
Abstract
The main challenge in the pathway analysis of cancer treatments is the complexity of the process. Process mining is one of the approaches that can be used to visualize and analyze these complex pathways. In this study, our purpose was to use process mining to explore variations in the treatment pathways of endometrial cancer. We extracted patient data from a hospital information system, created the process model, and analyzed the variations of the 62-day pathway from a General Practitioner referral to the first treatment in the hospital. We also analyzed the variations based on three different criteria: the type of the first treatment, the age at diagnosis, and the year of diagnosis. This approach should be of interest to others dealing with complex medical and healthcare processes.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). |
Keywords: | . Process mining, care pathway, endometrial cancer, 62-day wait |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Sep 2021 14:43 |
Last Modified: | 10 Sep 2021 14:43 |
Status: | Published |
Publisher: | IOS Press |
Series Name: | Studies in Health Technology and Informatics |
Identification Number: | 10.3233/shti210279 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177391 |
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Filename: Kurniati et al SHTI-281-SHTI210279 (1).pdf
Licence: CC-BY-NC-ND 4.0