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Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

Privacy-Preserving Process Mining in Healthcare †

School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia
RWTH Aachen University, Process and Data Science Group, 52062 Aachen, Germany
Utrecht University, Department of Information and Computing Sciences, 3508 TC Utrecht, The Netherlands
Author to whom correspondence should be addressed.
Proceedings of the Second International Workshop on Process-Oriented Data Science for Healthcare, Vienna, Austria, 1–6 September 2019 “Towards Privacy-Preserving Process Mining in Healthcare”.
Int. J. Environ. Res. Public Health 2020, 17(5), 1612;
Received: 17 January 2020 / Revised: 24 February 2020 / Accepted: 26 February 2020 / Published: 2 March 2020
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))
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. View Full-Text
Keywords: process mining; healthcare process data; data privacy; anonymisation; privacy metadata process mining; healthcare process data; data privacy; anonymisation; privacy metadata
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MDPI and ACS Style

Pika, A.; Wynn, M.T.; Budiono, S.; ter Hofstede, A.H.M.; van der Aalst, W.M.P.; Reijers, H.A. Privacy-Preserving Process Mining in Healthcare. Int. J. Environ. Res. Public Health 2020, 17, 1612.

AMA Style

Pika A, Wynn MT, Budiono S, ter Hofstede AHM, van der Aalst WMP, Reijers HA. Privacy-Preserving Process Mining in Healthcare. International Journal of Environmental Research and Public Health. 2020; 17(5):1612.

Chicago/Turabian Style

Pika, Anastasiia, Moe T. Wynn, Stephanus Budiono, Arthur H.M. ter Hofstede, Wil M.P. van der Aalst, and Hajo A. Reijers. 2020. "Privacy-Preserving Process Mining in Healthcare" International Journal of Environmental Research and Public Health 17, no. 5: 1612.

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