Multi-level process mining methodology for exploring disease-specific care processes.

Background: Public healthcare is a complex domain with many actors and highly variable protocols, which makes traditional process mining tools less effective and calls for specialized methods.Aim: The objective of the work was to develop a generally applicable process mining methodology to explore c...

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Published in:Journal of Biomedical Informatics Vol. 125
Main Authors: Vathy-Fogarassy, Ágnes, Vassányi, István, Kósa, István
Format: Journal Article
Published: Academic Press Inc. Jan2022
Online Access:View this record in EBSCOhost
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        15320464
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      dt: Jan2022
      vid: 125
      pid: 735
      pub: Academic Press Inc.
      place: Burlington, Massachusetts
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        154660939
        154660939
        NLM34954110
        10.1016/j.jbi.2021.103979
        NLM34954110
        154660939
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        atl: Multi-level process mining methodology for exploring disease-specific care processes.
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        au:
          Vathy-Fogarassy, Ágnes
          Vassányi, István
          Kósa, István
        affil: University of Pannonia, Department of Computer Science and Systems Technology 8200 Veszprém, Egyetem u. 10., Hungary
      sug:
        subj:
          Cardiology
          Health Care Delivery
          Scales
      ab: Background: Public healthcare is a complex domain with many actors and highly variable protocols, which makes traditional process mining tools less effective and calls for specialized methods.Aim: The objective of the work was to develop a generally applicable process mining methodology to explore care processes related to diseases.Methods: The proposed methodology called Process Mining Methodology for Exploring Disease-specific Care Processes (MEDCP) is based on a systematic, step-wise refinement of the raw event logs by using such a multi-level expert taxonomy of events that encapsulates the professional concepts of the analysis. A treatment process is defined according to domain-specific rules to identify the starting (index) and closing events. Concepts from various levels of the taxonomy support the final process definition for an analysis that can deliver meaningful conclusions for domain experts.Results: The applicability of the methodology was demonstrated on two case studies in the cardiological and oncological care domains, in the public health care system in Hungary over a period of ten years. Thanks to the multi-level taxonomy, these studies successfully identified the most important high-level event sequence patterns and some key anomalies in the national care system, such as the significantly different behavior of low-volume vs. high volume care providers in the oncology study or the geographically connected, homogeneous clusters of providers with similar care spectra in the cardiology study.Discussion: As the case studies showed, the proposed methodology can improve the efficiency of standard process mining methods, and deliver high level conclusions that are easy to interpret by domain experts. System-level insight into health care processes can serve as a basis for the optimisation and long-term planning of the whole care system.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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