title: Automatic identification of patients eligible for a pneumonia guideline.
abstract: 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.
  
  
  METHODS
  Prospective validation study.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Dept. of Medical Informatics, LDS Hospital, University of Utah, Salt Lake City, Utah, USA.
authors.forename: D
authors.initials: D
authors.lastname: Aronsky
authors.forename: P J
authors.initials: PJ
authors.lastname: Haug
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016013:Likelihood Functions; D011014:Pneumonia / Q000145:classification / Q000175:diagnosis*; D017410:Practice Guidelines as Topic; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index