ABSTRACT
PURPOSE: An analysis to determine the frequency of medication administration timing variances for specific therapeutic classes of high-risk medications using data extracted from a health-system clinical data warehouse (CDW) is presented. METHODS: This multicenter retrospective, observational analysis of medication administration data from 14 hospitals over 1 year was conducted using a large enterprise health-system CDW. The primary objective was to assess medication administration timing variance for focused therapeutic classes using medication orders and electronic medication administration records data extracted from the electronic health record (EHR). Administration timing variance patterns between standard hospital staffing shifts, within therapeutic drug classes, and for as-needed (PRN) medications were also studied. To assess medication administration timing variance, calculated variables were created for time intervals of 30-59, 60-120, and greater than 120 minutes. Scheduled medications were assessed for delayed administration and PRN medications for early administration. RESULTS: A total of 5,690,770 medication administrations (3,418,275 scheduled and 2,272,495 PRN) were included in the normalized data set. Scheduled medications were frequently subject to delays of ≥60 minutes (15% of administrations, n = 275,257) when scheduled for administration between 9-10 AM and between 9-10 PM. By therapeutic drug class, scheduled administrations of insulins, heparin products, and platelet aggregation inhibitors were the most commonly delayed. For PRN medications, medications in the anticoagulant and antiplatelet agent class (most commonly heparin flushes and line-management preparations) were most likely to be administered early, defined as more than 60 minutes from the scheduled time of first administration. CONCLUSION: The findings of this study assist in understanding patterns of delayed medication administration. Medication class, time of day of scheduled administration, and frequency were factors that influenced medication administration timing variance.