Abstract#R##N#OBJECTIVE: To assess the ability of an integrated, real-time diagnostic system (Bayesian network) to identify patients with community-acquired pneumonia who are eligible for a computerized pneumonia guideline without requiring clinicians to enter additional data. DESIGN: Prospective validation study. PATIENTS: All patients 18 years and older who presented to the emergency department of a tertiary care hospital. METHODS: The diagnostic system computed a probability of pneumonia for every patient. The final diagnosis was established using ICD-9 discharge diagnoses. Outcome measures were sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, and test effectiveness. RESULTS: During the 9-week study period there were 4,361 patients (112 pneumonia patients). The area under the receiver operating characteristic curve was 0.930 (CI: 0.907, 0.948). At a fixed sensitivity of 95%, the specificity was 68.5%, the positive predictive value 7.3%, the negative predictive value 99.8%, the positive likelihood ratio 3.0, the negative likelihood ratio 0.08, and the test effectiveness 2.05. CONCLUSION: The diagnostic system was able to detect patients who are eligible for a pneumonia guideline. The detection of eligible patients can be applied to automatically initiate and evaluate computerized guidelines.
['Dominik Aronsky', 'Peter J. Haug']
Automatic identification of patients eligible for a pneumonia guideline.