An Ontology-Based Approach for Understanding Appendicectomy Processes and Associated Resources

Authors

Nadeesha Pathiraja Rathnayaka Hitige
Ting Song
Steven J Craig
Kimberley J Davis
Xubing Hao
Licong Cui
Ping Yu

Publication Date

12-24-2024

Journal

Healthcare

Abstract

Background: Traditional methods for analysing surgical processes often fall short in capturing the intricate interconnectedness between clinical procedures, their execution sequences, and associated resources such as hospital infrastructure, staff, and protocols.

Aim: This study addresses this gap by developing an ontology for appendicectomy, a computational model that comprehensively represents appendicectomy processes and their resource dependencies to support informed decision making and optimise appendicectomy healthcare delivery.

Methods: The ontology was developed using the NeON methodology, drawing knowledge from existing ontologies, scholarly literature, and de-identified patient data from local hospitals.

Results: The resulting ontology comprises 108 classes, including 11 top-level classes and 96 subclasses organised across five hierarchical levels. The 11 top-level classes include "clinical procedure", "appendicectomy-related organisational protocols", "disease", "start time", "end time", "duration", "appendicectomy outcomes", "hospital infrastructure", "hospital staff", "patient", and "patient demographics". Additionally, the ontology includes 77 object and data properties to define relationships and attributes. The ontology offers a semantic, computable framework for encoding appendicectomy-specific clinical procedures and their associated resources.

Conclusion: By systematically representing this knowledge, this study establishes a foundation for enhancing clinical decision making, improving data integration, and ultimately advancing patient care. Future research can leverage this ontology to optimise healthcare workflows and outcomes in appendicectomy management.

Keywords

ontology, appendicectomy, surgical process, resources, process mining

DOI

10.3390/healthcare13010010

PMID

39791617

PMCID

PMC11720549

PubMedCentral® Posted Date

12-24-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes