55 research outputs found
Guided Interaction Exploration in Artifact-centric Process Models
Artifact-centric process models aim to describe complex processes as a
collection of interacting artifacts. Recent development in process mining allow
for the discovery of such models. However, the focus is often on the
representation of the individual artifacts rather than their interactions.
Based on event data we can automatically discover composite state machines
representing artifact-centric processes. Moreover, we provide ways of
visualizing and quantifying interactions among different artifacts. For
example, we are able to highlight strongly correlated behaviours in different
artifacts. The approach has been fully implemented as a ProM plug-in; the CSM
Miner provides an interactive artifact-centric process discovery tool focussing
on interactions. The approach has been evaluated using real life data sets,
including the personal loan and overdraft process of a Dutch financial
institution.Comment: 10 pages, 4 figures, to be published in proceedings of the 19th IEEE
Conference on Business Informatics, CBI 201
Blockchains for Business Process Management - Challenges and Opportunities
Blockchain technology promises a sizable potential for executing
inter-organizational business processes without requiring a central party
serving as a single point of trust (and failure). This paper analyzes its
impact on business process management (BPM). We structure the discussion using
two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle.
This paper provides research directions for investigating the application of
blockchain technology to BPM.Comment: Preprint for ACM TMI
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
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