Discover Context-Rich Local Process Models (Extended Abstract)

We introduce a new ProM plugin called Discover Context-Rich LPMs which mines a log for large local process models (LPMs) based on supported words. The main advantage of this plugin is that it produces much larger and much fewer LPMs than other tools. The plugin is packaged with an additional plugin called Generate HTML coverage report which calculates the coverage of LPMs along with several other quality measures. This extra plugin is useful to select and improve a set of LPMs.</p

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

Research Paper: Process Mining and Synthetic Health Data: Reflections and Lessons Learnt

Analysing the treatment pathways in real-world health data can provide valuable insight for clinicians and decision-makers. However, the procedures for acquiring real-world data for research can be restrictive, time-consuming and risks disclosing identifiable information. Synthetic data might enable representative analysis without direct access to sensitive data. In the first part of our paper, we propose an approach for grading synthetic data for process analysis based on its fidelity to relationships found in real-world data. In the second part, we apply our grading approach by assessing cancer patient pathways in a synthetic healthcare dataset (The Simulacrum provided by the English National Cancer Registration and Analysis Service) using process mining. Visualisations of the patient pathways within the synthetic data appear plausible, showing relationships between events confirmed in the underlying non-synthetic data. Data quality issues are also present within the synthetic data which reflect real-world problems and artefacts from the synthetic dataset’s creation. Process mining of synthetic data in healthcare is an emerging field with novel challenges. We conclude that researchers should be aware of the risks when extrapolating results produced from research on synthetic data to real-world scenarios and assess findings with analysts who are able to view the underlying data

Defining meaningful local process models

Current process discovery techniques are unable to produce high quality models that describe all observed behavior in semi-structured processes in a meaningful way. Local process model (LPM) discovery has been proposed to discover meaningful patterns in event logs from unstructured processes. In this paper, we explore the use of LPM discovery on event logs from semi-structured processes and find several drawbacks: It finds many small patterns but doesn't find patterns larger than 4-5 events, it produces too many models, and the discovered models describe some events from the log multiple times while leaving others unexplained. Despite these drawbacks, we observe that a set of LPMs taken together can yield interesting insights. From these observations we distill several requirements for meaningful sets of LPMs: We want (1) a limited set of models that (2) have high accuracy measures such as fitness and precision while (3) they together cover the whole event log and (4) do not cover parts of the log multiple times unnecessarily. We show that it is possible to manually construct sets of LPMs that satisfy all these requirements on the well-known BPIC12 event log. We then apply and evaluate the existing quality measures for individual LPMs. We propose to disregard support, confidence, and determinism as measures for meaningfulness of LPMs and we propose new ways to evaluate sets of LPMs based existing methods.</p

Defining meaningful local process models

Current process discovery techniques are unable to produce high quality models that describe all observed behavior in semi-structured processes in a meaningful way. Local process model (LPM) discovery has been proposed to discover meaningful patterns in event logs from unstructured processes. In this paper, we explore the use of LPM discovery on event logs from semi-structured processes and find several drawbacks: It finds many small patterns but doesn't find patterns larger than 4-5 events, it produces too many models, and the discovered models describe some events from the log multiple times while leaving others unexplained. Despite these drawbacks, we observe that a set of LPMs taken together can yield interesting insights. From these observations we distill several requirements for meaningful sets of LPMs: We want (1) a limited set of models that (2) have high accuracy measures such as fitness and precision while (3) they together cover the whole event log and (4) do not cover parts of the log multiple times unnecessarily. We show that it is possible to manually construct sets of LPMs that satisfy all these requirements on the well-known BPIC12 event log. We then apply and evaluate the existing quality measures for individual LPMs. We propose to disregard support, confidence, and determinism as measures for meaningfulness of LPMs and we propose new ways to evaluate sets of LPMs based existing methods