Emergency patient analysis using the certainty factor method

Abstract

Emergency departments (EDs) serve as the frontline of hospital care, managing patients with a wide spectrum of medical urgency. Accurate and timely triage is essential to prioritize treatment and
prevent adverse outcomes. However, the growing volume of ED visits has placed increasing pressure on clinical staff, who must make rapid decisions often under uncertainty. Expert systems that
incorporate uncertainty-handling methods offer a promising solution to support clinical decision-making in such environments. This study analyzes a hospital emergency department dataset comprising 9,216 patient records, encompassing key variables such as patient age, gender, race, waiting time, department referral, inpatient admission status, and patient satisfaction score. The Certainty Factor (CF) method was applied to quantify the confidence level in determining whether a patient requires hospitalization, based on three clinically relevant indicators: elderly patient age group (age ≥ 60 years), waiting time exceeding 40 minutes, and referral to a specialist department. Each factor was assigned user and expert confidence weights, and the combined CF was computed using the standard sequential combination formula. Manual calculation using all three factors yielded a final CF value of 0.9322, corresponding to a diagnostic confidence level of 93.22%. This high confidence value
indicates that the simultaneous presence of these three clinical and operational indicators is a strong predictor of hospitalization need. Descriptive analysis further revealed that 50% of patients were
admitted, 25% belonged to the elderly group, and 39.9% experienced extended waiting times. The results demonstrate that the Certainty Factor method is effective in handling diagnostic uncertainty in ED triage scenarios and can serve as a reliable foundation for developing clinical decision support systems in hospital emergency settings.