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.

Performance of business processes is affected by capabilities of employees who execute activities in these processes. Resource allocation approaches aim to identify an optimal allocation of resources to activities which supports the achievement of some process performance goals (e.g., in terms of time or cost). The majority of resource allocation approaches proposed in the Business Process Management community are process-centric (i.e., they aim to optimise process performance) and they neglect capability development needs of employees. In this article, we propose a novel approach for recommending unfamiliar process activities to employees which is based on the application of machine learning techniques to information extracted from process execution data. The goal of the approach is to assist organisations in their quest for capacity development by providing employees with opportunities to gain experience through the execution of new activities. The approach was implemented and evaluated by conducting experiments with real publicly available event logs. In the experiments, we compared the predictions provided by the approach with actual activity executions recorded in the logs. The experiments demonstrated the effectiveness of different approach configurations and showed that this machine-learning based approach significantly outperforms an existing algorithm proposed in earlier work.