Using Electronic Health Records to Identify Asthma-Related Acute Care Encounters.

Objective: Leveraging "big data" to improve care requires that clinical concepts be operationalized using available data. Electronic health record (EHR) data can be used to evaluate asthma care, but relying solely on diagnosis codes may misclassify asthma-related encounters. We created streamlined, feasible and transparent prototype algorithms for EHR data to classify emergency department (ED) encounters and hospitalizations as "asthma-related."

Methods: As part of an asthma program evaluation, expert clinicians conducted a multi-phase iterative chart review to evaluate 467 pediatric ED encounters and 136 hospitalizations with asthma diagnosis codes from calendar years 2017 and 2019, rating the likelihood that each encounter was actually asthma-related. Using this as a reference standard, we developed rule-based algorithms for EHR data to classify visits. Accuracy was evaluated using sensitivity, specificity, and positive and negative predictive values (PPV, NPV).

Results: Clinicians categorized 38% of ED encounters as "definitely" or "probably" asthma-related; 13% as "possibly" asthma-related; and 49% as "probably not" or "definitely not" related to asthma. Based on this reference standard, we created two rule-based algorithms to identify "definitely" or "probably" asthma-related encounters, one using text and non-text EHR fields and another using non-text fields only. Sensitivity, specificity, PPV, and NPV were >95% for the algorithm using text and non-text fields and >87% for the algorithm using only non-text fields compared to the reference standard. We created a two-rule algorithm to identify asthma-related hospitalizations using only non-text fields.

Conclusions: Diagnostic codes alone are insufficient to identify asthma-related visits, but EHR-based prototype algorithms that include additional methods of identification can predict clinician-identified visits with sufficient accuracy.