An Algorithm to Assess Guideline Concordance of Antibiotic Choice in Community-Acquired Pneumonia

This study aimed to develop and evaluate an algorithm to reduce the chart review burden of improvement efforts by automatically labeling antibiotic selection as either guideline-concordant or -discordant based on electronic health record data for patients with community-acquired pneumonia (CAP).

We developed a 3-part algorithm using structured and unstructured data to assess adherence to an institutional CAP clinical practice guideline. The algorithm was applied to retrospective data for patients seen with CAP from 2017 to 2019 at a tertiary children's hospital. Performance metrics included positive predictive value (precision), sensitivity (recall), and F1 score (harmonized mean), with macro-weighted averages. Two physician reviewers independently assigned "actual" labels based on manual chart review.

Of 1345 patients with CAP, 893 were included in the training cohort and 452 in the validation cohort. Overall, the model correctly labeled 435 of 452 (96%) patients. Of the 286 patients who met guideline inclusion criteria, 193 (68%) were labeled as having received guideline-concordant antibiotics, 48 (17%) were labeled as likely in a scenario in which deviation from the clinical practice guideline was appropriate, and 45 (16%) were given the final label of "possibly discordant, needs review." The sensitivity was 0.96, the positive predictive value was 0.97, and the F1 was 0.96.

An automated algorithm that uses structured and unstructured electronic health record data can accurately assess the guideline concordance of antibiotic selection for CAP. This tool has the potential to improve the efficiency of improvement efforts by reducing the manual chart review needed for quality measurement.