A Systematic Approach to Optimize Electronic Health Record Medication Alerts in a Health-System
Purpose: Limited literature evaluates a sustainable process for optimization of medication alerts when implementing a new electronic health record (EHR) technology with clinical decision support (CDS) capabilities. This study aimed to provide health-system enterprises with a systematic approach to optimize medication alerts with new EHR technology and evaluate the effect of strategic interventions to improve the effectiveness of medication related CDS.
Methods: An 81 week quasi-experimental study was conducted to evaluate the impact of interventions made to medication related CDS alerts by a multi-disciplinary committee. The primary endpoint was weekly modification and acknowledgement rates of medication alerts after drug-drug interaction reclassification. Secondary endpoints included weekly modification and acknowledgement rates of drug-drug interaction and duplicate therapy alerts, pharmacist and provider modification and acknowledgment rates in response to drug alerts, and monthly number of alerts per 100 medication orders. Data on alert and warning frequency, severity, and response type were analyzed before and after committee interventions to determine the impact of committee led interventions. Interrupted time series regression analysis was utilized to assess primary and secondary endpoints over the study time period.
Results: After reclassification of drug-drug interactions, a significant increase in weekly provider modification and acknowledgement rates occurred (2.06 ± 0.18%, p <0.001; 1.49 ± 0.25%, p<0.001). Total alerts per 100 medication orders significantly decreased after drug-drug interaction classification (Pre-intervention median: 88.4 vs Post-intervention 63.1, p=0.017).
Conclusion: Committee led interventions to drug-drug interactions facilitated an overall increase in both medication alert acknowledgement and modification rates, as well as an overall reduction in the total quantity of generated alerts.