Combining decision support methodologies to diagnose pneumonia.

Combining decision support methodologies that process information stored in different data formats can increase the performance of a computerized decision support system.

Abstract

OBJECTIVE: To evaluate the performance of a computerized decision support system that combines two different decision support methodologies (a Bayesian network and a natural language understanding system) for the diagnosis of patients with pneumonia. DESIGN: Evaluation study using data from a prospective, clinical study. PATIENTS: All patients 18 years and older who presented to the emergency department of a tertiary care setting and whose chest x-ray report was available during the encounter. METHODS: The computerized decision support system calculated a probability of pneumonia using information provided by the two systems. Outcome measures were the area under the receiver operating characteristic curve, sensitivity, specificity, predictive values, likelihood ratios, and test effectiveness. RESULTS: During the 3-month study period there were 742 patients (45 with pneumonia). The area under the receiver operating characteristic curve was 0.881 (95% CI: 0.822, 0.925) for the Bayesian network alone and 0.916 (95% CI: 0.869, 0.949) for the Bayesian network combined with the natural language understanding system (p=0.01). CONCLUSION: Combining decision support methodologies that process information stored in different data formats can increase the performance of a computerized decision support system.