Resolution limits for process comparison from event data

This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transforms heterogeneous clinical tables into a process-mining-ready event log and applies discovery, declarative conformance checking, and outcome analysis. The reconstructed pathways highlight the monitoring backbone of inpatient care, variability at the Emergency department-admission interface, and outcome differences driven by age and exposure to intensive care units. These insights support triage standardization, capacity planning, and step-down coordination from intensive care units to lower-acuity wards, showing how process mining can inform evidence-based hospital governance.

Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent years, process mining has proved successful in the analysis and improvement of operational processes. More often than not, surprising findings are found when one does not consider the full process, making LPMs and their discovery highly valuable. However, similar to other pattern mining approaches, LPM discovery algorithms face the problems of model explosion and model repetition, i.e., the algorithms may create hundreds if not thousands of LPMs, and subsets of them are close in structure or behavior. Practically, no analyst would be able to comb through thousands of LPMs leading to using a sample of LPMs that are easily accessible. The current sentiment is that the top-scoring LPMs form the optimal sample to be presented. However, different applications should demand a different optimal sample. With this work, we show that if the goal of the mined LPMs is to understand a process, using the top-scoring LPMs as an optimal sample is a poor choice because of high repetition. We propose a framework for grouping LPMs and creating an optimal sample by taking one representative LPM for each group. We measure similarity between models via established process model similarity measures or by comparing the context in which an LPM appears. The context is formed using data attributes available in the underlying event logs. We demonstrate the usefulness of grouping on multiple event logs by comparing repetition and coverage between samples comprised of the top-scoring models and the representatives of discovered groups.

Medical world models commonly learn from multimodal recordings synchronized onto a fixed-rate grid. This preprocessing resamples each native stream onto a shared time axis. Each stream has an observation clock that governs when observations are emitted or updated. When this clock depends on the latent or acquisition state, it is endogenous. In such settings, synchronization may not be neutral and can erase task-relevant evidence before the model sees the data. We introduce a four-regime taxonomy that characterizes where the evidence needed to distinguish a target event or state survives. The relevant witness may remain in the sampled values, in grid-cell update patterns, in native timing, or only in an external acquisition channel. EndoClock operationalizes this taxonomy as a conservative pretraining audit. It reports the lowest witness-bearing representation supported by the available evidence, or unresolved when no regime can be established. We illustrate this failure in echocardiography, where B-mode video write-outs cease during pulsed-wave Doppler acquisition while the corresponding measurement events remain recorded only in an external acquisition log. This work is a preliminary failure alert and executable audit. Its practical message is to preserve the native observation process long enough to determine whether synchronization has erased information required by the intended task.