Automatic identification of patients eligible for a pneumonia guideline
1Dept. of Medical Informatics, LDS Hospital, University of Utah, Salt Lake City, Utah, USA.
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.
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