Sunny B Bhakta 1,2 , A Carmine Colavecchia 1,2 , Linda Haines 1 , Divya Varkey 3 , Kevin W Garey 3 . Show Affiliations »
Abstract
PURPOSE: The effectiveness of a systematic, streamlined approach to optimize drug-drug interaction alerts in an electronic health record for a health system was studied. METHODS: An 81-week quasi-experimental study was conducted to evaluate interventions made to medication-related clinical decision-support (CDS) alerts. Medication-related CDS alerts were systematically reduced using a multi disciplinary healthcare committee. The primary endpoint was weekly overall, modification, and acknowledgement rates of medication alerts after drug-drug interaction reclassification. Secondary endpoints included sub analysis of types of medication alerts (drug-drug interaction and duplicate therapy alerts) and alert use by providers (pharmacist and prescribers). Data was analyzed using interrupted time series regression analysis. RESULTS: After implementation of the new alert system, total number of weekly inpatient alerts decreased from 68,900 (66,300-70,900) and 50,300 (48,600-53,600) in the postintervention period (p < 0.001). The perentage of alerts acknowledged weekly increased from 11.8% (IQR, 11.4-12.1%) in the preintervention period to 13.7% (IQR, 13.3-14.0%) in the postintervention period (p < 0.001). The percentage of alerts that were modified also increased from 5.0% (IQR, 4.9-5.3%) in the preintervention period to 7.3% (IQR, 7.0-7.6%) in the postintervention period (p < 0.001). Both increases were primarily seen with pharmacists versus other healthcare professionals (p < 0.001). CONCLUSION: A committee-led systematic approach to optimizing drug-drug interactions facilitated a significant decrease in the overall number of alerts and an increase in both medication alert acknowledgement and modification rates. © American Society of Health-System Pharmacists 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
PURPOSE: The effectiveness of a systematic, streamlined approach to optimize drug-drug interaction alerts in an electronic health record for a health system was studied. METHODS: An 81-week quasi-experimental study was conducted to evaluate interventions made to medication-related clinical decision-support (CDS) alerts. Medication-related CDS alerts were systematically reduced using a multi disciplinary healthcare committee. The primary endpoint was weekly overall, modification, and acknowledgement rates of medication alerts after drug-drug interaction reclassification. Secondary endpoints included sub analysis of types of medication alerts (drug-drug interaction and duplicate therapy alerts) and alert use by providers (pharmacist and prescribers). Data was analyzed using interrupted time series regression analysis. RESULTS: After implementation of the new alert system, total number of weekly inpatient alerts decreased from 68,900 (66,300-70,900) and 50,300 (48,600-53,600) in the postintervention period (p < 0.001). The perentage of alerts acknowledged weekly increased from 11.8% (IQR, 11.4-12.1%) in the preintervention period to 13.7% (IQR, 13.3-14.0%) in the postintervention period (p < 0.001). The percentage of alerts that were modified also increased from 5.0% (IQR, 4.9-5.3%) in the preintervention period to 7 .3% (IQR, 7.0-7.6%) in the postintervention period (p < 0.001). Both increases were primarily seen with pharmacists versus other healthcare professionals (p < 0.001). CONCLUSION: A committee-led systematic approach to optimizing drug-drug interactions facilitated a significant decrease in the overall number of alerts and an increase in both medication alert acknowledgement and modification rates. © American Society of Health-System Pharmacists 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Entities: Species
Keywords:
adverse events; automation; clinical pharmacy; pharmacovigilance
Year: 2019
PMID: 31361861 DOI: 10.1093/ajhp/zxz012
Source DB: PubMed Journal: Am J Health Syst Pharm ISSN: 1079-2082 Impact factor: 2.637