Privacy-Preserving Process Mining in Healthcare

Anastasiia Pika, Moe T. Wynn, Stephanus Budiono, Arthur H.M. ter Hofstede, Wil M.P. van der Aalst and Hajo A. Reijers
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Anastasiia Pika: School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia
Moe T. Wynn: School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia
Stephanus Budiono: School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia
Arthur H.M. ter Hofstede: School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia
Wil M.P. van der Aalst: School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia
Hajo A. Reijers: School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia

IJERPH, 2020, vol. 17, issue 5, 1-28

Abstract: 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.

Keywords: process mining; healthcare process data; data privacy; anonymisation; privacy metadata (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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