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Towards a decision support system for pediatric emergency telephone triage
Aurélia Manns1, Alix Millet1, Florence Campeotto2
1Department of Medical Informatics, Hôpital Européen Georges Pompidou, Hôpital Necker Enfants Malades, APHP, Paris, France; Université Paris Cité, INSERM UMR1163, Imagine Institute, Clinical Bioinformatics Laboratory, Paris F-75006, France.
Insights
A new clinical decision support system (CDSS) was developed for pediatric telephone triage, achieving 77.1% accuracy. This tool aims to improve emergency department admissions and support healthcare professionals.
Area of Science:
- Medical Informatics
- Pediatric Emergency Medicine
- Artificial Intelligence in Healthcare
Background:
- Telephone triage is crucial for emergency department (ED) admissions but challenging in pediatrics due to nonspecific symptoms and parental reporting.
- Current clinical decision support systems (CDSSs) are not optimized for pediatric nuances, necessitating specialized tools.
- Developing a tailored CDSS for pediatric emergency telephone triage is essential to improve accuracy and quality of care.
Purpose of the Study:
- To develop and evaluate a novel CDSS specifically designed for pediatric emergency telephone triage.
- To enhance the accuracy and efficiency of triage decisions made over the phone for pediatric patients.
- To provide real-time, interpretable recommendations to healthcare professionals during pediatric triage.
Main Methods:
- Developed a three-layered CDSS featuring an ontology-oriented knowledge base (KB) with 303 concepts and 1780 axioms.
- Implemented a generic medical reasoning system mimicking clinical judgment in pediatric emergency triage.
- Assessed the CDSS performance using 96 fictitious clinical cases, comparing its recommendations against written guidelines and expert panels.
Main Results:
- The CDSS demonstrated 100% internal validity against written recommendations.
- Achieved 77.1% accuracy when compared to a panel of three pediatric emergency experts.
- Discrepancies with experts (22.9%) arose from undocumented expert information (11.5%) or differing interpretations of guidelines (10.4%).
Conclusions:
- The developed CDSS offers explainable recommendations, aiming to reduce cognitive load for healthcare professionals.
- Future work includes expanding the knowledge base and integrating data-driven approaches with the current knowledge-based system.
- Enhancing user interaction with patient-friendly language is a key area for future development.
Background:
Telephone triage could limit admissions to emergency departments. However, telephone triage is challenging in pediatrics due to nonspecific symptoms, reliance on parental description, and emotional distress. Clinical decision support systems (CDSSs) could improve the accuracy and quality of telephone triage. Despite proven benefits, current CDSSs are not well suited to the nuances of pediatrics. This study aims to develop a CDSS for pediatric emergency telephone triage.
Methods:
We developed a formal knowledge base (KB) for pediatric telephone triage inspired by the ontology model and implemented a generic medical reasoning system that mimics the clinical reasoning used in pediatric emergency triage. The CDSS is built in three layers (a knowledge layer, a Python-based decision layer, and a web interface layer) and provides real-time recommendations. We assessed its accuracy on 96 fictitious clinical cases.
Results:
The CDSS uses an ontology-oriented KB that includes 303 concepts and 1780 axioms and a generic algorithm that provides recommendations based on user input, exploring and updating decisions continuously. It demonstrated 100 % internal validity compared to written recommendations and 77.1 % accuracy compared to a trio of experts. The 22.9 % discrepancies were due to experts using additional elements not documented in the written recommendations (11.5 %) or experts making different decisions despite consistent rules in the textual recommendations (10.4 %), emphasizing the challenges of standardized guidelines in this narrow but complex field.
Discussion/Conclusion:
The CDSS provides explainable and interpretable recommendations designed to alleviate healthcare professionals' cognitive load so that they can focus on complex clinical situations. Future improvements involve enriching the KB, enhancing user interaction with patient-friendly language, and combining this knowledge-based approach with data-driven approaches.
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