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Ontology-based specification, identification and analysis of perioperative risks
Alexandr Uciteli1, Juliane Neumann2, Kais Tahar3
1Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany. auciteli@imise.uni-leipzig.de.
Journal of Biomedical Semantics
|September 8, 2017
Summary
This study introduces an ontology and software system to identify and mitigate medical errors, particularly in perioperative settings. The system analyzes data to provide real-time alerts, enhancing patient safety and reducing adverse events in healthcare.
Area of Science:
- Medical Informatics
- Patient Safety
- Ontology Engineering
Background:
- Healthcare professionals face significant physical and mental strain, increasing the risk of medical errors.
- Adverse events in hospitals, often linked to organizational or procedural shortcomings, could be reduced with improved safety measures.
- Critical situations and errors are common in interdisciplinary collaboration and the use of complex technology, especially in perioperative care.
Purpose of the Study:
- To develop an ontology and an ontology-based software system for identifying risks in medical processes.
- To support the avoidance of errors, with a specific focus on the perioperative setting.
- To create a practical definition of risk for medical staff and software tools.
Main Methods:
- Developed a Risk Identification Ontology (RIO) based on a defined risk notion.
- Created an agent system for centralized collection and analysis of risk-relevant data throughout the perioperative process.
- Developed an ontology-based software module, Ontology-based Risk Detector (OntoRiDe), for risk identification.
Main Results:
- The agent system centralizes risk data from various sources for comprehensive analysis.
- Medical personnel receive context-sensitive hints and alerts for identified risks.
- The OntoRiDe module effectively identifies risks specified within the RIO.
Conclusions:
- Approximately 20 risks associated with cochlear implantation (CI) have been implemented and tested.
- Comprehensive testing confirmed the accuracy of data acquisition, risk identification, and analysis.
- The web-based visualization of results has been validated, demonstrating system efficacy.
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