arXiv:2606. 00041v1 Announce Type: cross Abstract: This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset.
arXiv:2512. 03787v2 Announce Type: replace Abstract: Clinical pathways are specialized healthcare plans that model patient treatment procedures.
The paper examines how event data from hospital processes can obscure whether activities occur concurrently or sequentially. It shows that standard event‑log approaches, based on stochastic language, often fail to distinguish concurrency because any log can be explained by a model with no concurrent events. The authors argue that the key to resolving this ambiguity lies in the choice of what is recorded—such as precise start and end times or object‑centric ordering—rather than simply collecting more data.
The paper examines how event logs used in process mining can fail to reveal concurrent versus sequential activities, using a hospital example where blood tests and imaging may occur simultaneously or in alternating order. It demonstrates that standard stochastic language approaches only expose the assumptions of their discovery algorithms, often misrepresenting concurrency. The authors argue that the key to distinguishing concurrent behavior lies in the choice of recorded data—such as precise start and end times or object‑centric ordering—rather than simply increasing sample size.
arXiv:2510. 04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure.
arXiv:2609.07984v1 Announce Type: new Abstract: Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime prediction...
arXiv:2608. 08806v1 Announce Type: new Abstract: Objective.
AI Morbidity and Mortality (AI M&M) is a structured, blameless framework designed to review clinical AI failures. It combines standardized case intake, evidence preservation, investigator reconstruction, tool‑in‑loop attribution, and corrective‑action tracking, classifying each event across four linked dimensions: Trigger, Mechanism, Clinical Pathway, and Corrective Action. The authors demonstrate the framework with five outpatient medication and clinical decision‑support cases, achieving full agreement among reviewers on all classification axes.
arXiv:2607. 04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining.
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
The paper introduces a new approach to process model forecasting that uses causal nets instead of traditional directly-follows graphs, enabling explicit representation of concurrency. It forecasts time series of relation and binding counts, reconstructs future process models with AND/XOR semantics, and evaluates them using a protocol that handles partial traces for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance close to re‑mined models and outperform static discovery baselines, though filtering infrequent bindings improves metrics at the cost of losing concurrent behavior.
The paper introduces an evidence ladder for evaluating reinforcement learning (RL) in healthcare, outlining stages from problem formulation to lifecycle monitoring. It argues that success in historical data does not guarantee real‑world improvement and highlights assumptions and failure modes at each rung. The authors propose reporting practices to support cumulative evaluation and emphasize that RL should be tested as an intervention within a dynamic sociotechnical system.
arXiv:2606. 05463v1 Announce Type: new Abstract: Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts.