From Insights to INTEL: Evaluating Process Mining Insights with Healthcare Professionals

As healthcare organisations are looking for ways to improve their processes, process mining techniques are increasingly being used. Current process mining methods do not offer support for translating process mining insights into actionable improvement ideas. By performing action research at two healthcare organisations, we introduce and illustrate the INTEL funnel, a novel three-staged method consisting of process familiarisation, domain explanation and improvement ideation. Our method complements existing process mining methods and constitutes the first attempt to open the black box regarding the path from process mining insights to actionable process improvement ideas. In this way, it can contribute to a more systematic uptake of process mining in healthcare practice

Using domain knowledge to enhance process mining results

\u3cp\u3eProcess discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.\u3c/p\u3

Using domain knowledge to enhance process mining results

Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining