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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Updated: May 20, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Using ontologies to facilitate healthcare process mining and analysis.

Owen P Dwyer1, Lara Chammas1, Emanuel Sallinger1,2

  • 1University of Oxford, Oxford, UK.

Journal of Intelligent Information Systems
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

Ontology-based process mining in healthcare uses clinical terminology to analyze patient data, improving care standards and identifying improvement opportunities. This automated approach aids in extracting insights from complex health records.

Keywords:
Electronic health recordsOntologiesProcess mining

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Published on: February 23, 2019

Area of Science:

  • Health Informatics
  • Computational Biology
  • Clinical Data Analysis

Background:

  • Healthcare organizations generate vast amounts of patient care data.
  • Analyzing this data is crucial for identifying care standard deviations, improvement opportunities, and supporting new treatment development.
  • The complexity and volume of healthcare data necessitate automated analysis techniques like process mining.

Purpose of the Study:

  • To demonstrate how ontological information from clinical terminologies can enhance healthcare process mining.
  • To facilitate the extraction and analysis of clinical processes by connecting and aggregating clinical events.
  • To evaluate the effectiveness of ontology-based methods against expert-curated approaches.

Main Methods:

  • Utilized ontological information from clinical terminologies to classify diagnoses and treatments.
  • Applied process mining techniques to connect and aggregate clinical events based on ontological classifications.
  • Demonstrated the approach using patient cancer care data from a major hospital.
  • Compared results with benchmark datasets analyzed using expert-driven methods.

Main Results:

  • Ontology-based methods show potential in automating the extraction and analysis of healthcare processes.
  • The approach successfully connected and aggregated clinical events for process mining.
  • Comparison revealed both strengths and limitations of ontology-based extraction compared to expert-curated methods.
  • Identified specific areas where ontological information facilitates or hinders process analysis.

Conclusions:

  • Ontological information in clinical terminologies can significantly aid healthcare process mining.
  • Automated, ontology-driven analysis offers a scalable approach to understanding patient care pathways.
  • Further refinement is needed to address the shortcomings and maximize the potential of ontology-based healthcare process mining.
  • This method provides a valuable tool for quality improvement and the development of healthcare technologies.