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Privacy-Preserving Process Mining in Healthcare †

Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcar...

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Detalles Bibliográficos
Autores principales: Pika, Anastasiia, Wynn, Moe T., Budiono, Stephanus, ter Hofstede, Arthur H.M., van der Aalst, Wil M.P., Reijers, Hajo A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084661/
https://www.ncbi.nlm.nih.gov/pubmed/32131516
http://dx.doi.org/10.3390/ijerph17051612
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author Pika, Anastasiia
Wynn, Moe T.
Budiono, Stephanus
ter Hofstede, Arthur H.M.
van der Aalst, Wil M.P.
Reijers, Hajo A.
author_facet Pika, Anastasiia
Wynn, Moe T.
Budiono, Stephanus
ter Hofstede, Arthur H.M.
van der Aalst, Wil M.P.
Reijers, Hajo A.
author_sort Pika, Anastasiia
collection PubMed
description Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.
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spelling pubmed-70846612020-03-24 Privacy-Preserving Process Mining in Healthcare † Pika, Anastasiia Wynn, Moe T. Budiono, Stephanus ter Hofstede, Arthur H.M. van der Aalst, Wil M.P. Reijers, Hajo A. Int J Environ Res Public Health Article Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log. MDPI 2020-03-02 2020-03 /pmc/articles/PMC7084661/ /pubmed/32131516 http://dx.doi.org/10.3390/ijerph17051612 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pika, Anastasiia
Wynn, Moe T.
Budiono, Stephanus
ter Hofstede, Arthur H.M.
van der Aalst, Wil M.P.
Reijers, Hajo A.
Privacy-Preserving Process Mining in Healthcare †
title Privacy-Preserving Process Mining in Healthcare †
title_full Privacy-Preserving Process Mining in Healthcare †
title_fullStr Privacy-Preserving Process Mining in Healthcare †
title_full_unstemmed Privacy-Preserving Process Mining in Healthcare †
title_short Privacy-Preserving Process Mining in Healthcare †
title_sort privacy-preserving process mining in healthcare †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7084661/
https://www.ncbi.nlm.nih.gov/pubmed/32131516
http://dx.doi.org/10.3390/ijerph17051612
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