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Modeling clinical activities based on multi-perspective declarative process mining with openEHR's characteristic.

Haifeng Xu1,2, Jianfei Pang1, Xi Yang2

  • 1Information Center, Academy of Military Medical Sciences, Beijing, China.

BMC Medical Informatics and Decision Making
|December 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for clinical process mining, enabling healthcare professionals to model complex medical activities effectively. The approach enhances model understandability and reusability, improving healthcare quality.

Keywords:
Clinical eventsDeclarative modelingMulti-perspectiveProcess miningopenEHR

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Area of Science:

  • Health Informatics
  • Process Mining
  • Clinical Data Analysis

Background:

  • Current healthcare process mining often overlooks data properties and temporal aspects, hindering comprehensive analysis.
  • Difficulty for medical experts in classifying event attributes for computational models poses a challenge.
  • Limited model sharing and reuse capabilities restrict the broader application of existing healthcare process models.

Purpose of the Study:

  • To develop a user-friendly, constraint-based method for multi-perspective declarative process mining in clinical settings.
  • To empower healthcare personnel to independently model clinical processes.
  • To address limitations in current process mining by incorporating data properties and temporal perspectives.

Main Methods:

  • A constraint-based method utilizing multi-perspective declarative process mining was developed.
  • Event attributes were classified into seven types, inspired by openEHR standards.
  • A Constrained Relationship Matrix was used to represent relationships between attribute types, alongside a conformance checking algorithm.

Main Results:

  • The method was validated using Electronic Medical Record (EMR) data from 358 patients.
  • The ischemic stroke treatment process was modeled and checked for conformance with clinical guidelines.
  • Conformance checking results were analyzed and confirmed by medical experts, demonstrating clinical relevance.

Conclusions:

  • The developed representation approach is understandable and expandable for modeling clinical activities.
  • The method facilitates the sharing and reuse of clinical process models across different medical facilities.
  • This approach enhances the quality of medical services through improved process modeling and analysis.