Privacy-Preserving Process Mining in Healthcare

International Journal of Environmental Research and Public Health, vol. 17, pp. 1612

Abstract

Process mining has been successfully applied in the healthcare domain and has helped touncover various insights for improving healthcare processes. While the benefits of process miningare widely acknowledged, many people rightfully have concerns about irresponsible uses of personaldata. Healthcare information systems contain highly sensitive information and healthcare regulationsoften require protection of data privacy. The need to comply with strict privacy requirements mayresult in a decreased data utility for analysis. Until recently, data privacy issues did not get muchattention in the process mining community; however, several privacy-preserving data transformationtechniques have been proposed in the data mining community. Many similarities between datamining and process mining exist, but there are key differences that make privacy-preserving datamining techniques unsuitable to anonymise process data (without adaptations). In this article, weanalyse data privacy and utility requirements for healthcare process data and assess the suitabilityof privacy-preserving data transformation methods to anonymise healthcare data. We demonstratehow some of these anonymisation methods affect various process mining results using three publiclyavailable healthcare event logs. We describe a framework for privacy-preserving process mining thatcan support healthcare process mining analyses. We also advocate the recording of privacy metadatato capture information about privacy-preserving transformations performed on an event log.

Authors 6

1. Anastasiia Pika corresponding

   Queensland University of Technology

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia

2. Moe Thandar Wynn

   Queensland University of Technology

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia

3. Stephanus Budiono

   Queensland University of Technology

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia

4. Arthur H. M. ter Hofstede

   Queensland University of Technology

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia

5. Wil M. P. van der Aalst Aachen Process and Data Science Group

   RWTH Aachen University · Queensland University of Technology

   Affiliation as printed

   RWTH Aachen University, Process and Data Science Group, 52062 Aachen, Germany

   School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia

6. Hajo Alexander Reijers

   Queensland University of Technology · Utrecht University

   Affiliation as printed

   School of Information Systems, Queensland University of Technology, Brisbane 4000, QLD, Australia

   Utrecht University, Department of Information and Computing Sciences, 3508 TC Utrecht, The Netherlands