| Objectives: Fast and accurate patient triage for the response process is a critical first step in emergency situations.
This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes
time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to
determine the triage level.
Methods: A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were
used to predict the triage level of patients according to the triage specialist’s opinions and Emergency Severity
Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The
data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was
converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and
predict the triage levels.
Results: Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of
the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support
systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the
traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of
the documentation also improved from 76.72% to 98.5%.
Conclusions: Designed system was effective in determining the triage level of patients and it proved helpful for
nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can
reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. |