Re-ordered fuzzy conformance checking for uncertain clinical records

Sicui Zhang, Laura Genga, Lukas Dekker, Hongchao Nie, Xudong Lu (Corresponding author), Huilong Duan, Uzay Kaymak

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Modern hospitals implement clinical pathways to standardize patients’ treatments. Conformance checking techniques provide an automated tool to assess whether the actual executions of clinical processes comply with the corresponding clinical pathways. However, clinical processes are typically characterized by a high degree of uncertainty, both in their execution and recording. This paper focuses on uncertainty related to logging clinical processes. The logging of the activities executed during a clinical process in the hospital information system is often performed manually by the involved actors (e.g., the nurses). However, such logging can occur at a different time than the actual execution time, which hampers the reliability of the diagnostics provided by conformance checking techniques. To address this issue, we propose a novel conformance checking algorithm that leverages principles of fuzzy set theory to incorporate experts’ knowledge when generating conformance diagnostics. We exploit this knowledge to define a fuzzy tolerance in a time window, which is then used to assess the magnitude of timestamp violations of the recorded activities when evaluating the overall process execution compliance. Experiments conducted on a real-life case study in a Dutch hospital show that the proposed method obtains more accurate diagnostics than the state-of-the-art approaches. We also consider how our diagnostics can be used to stimulate discussion with domain experts on possible strategies to mitigate logging uncertainty in the clinical practice.

Original languageEnglish
Article number104566
Number of pages15
JournalJournal of Biomedical Informatics
Volume149
DOIs
Publication statusPublished - Jan 2024

Bibliographical note

Publisher Copyright:
© 2023

Funding

This research has received funding from the Brain Bridge Project sponsored by Philips Research .

FundersFunder number
Philips Research Americas

    Keywords

    • Clinical deviations
    • Conformance checking
    • Event-log re-ordering
    • Fuzzy sets
    • Process mining
    • Uncertainty
    • Reproducibility of Results
    • Algorithms
    • Hospitals
    • Humans
    • Fuzzy Logic
    • Hospital Information Systems

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