Purpose: Missing medication requests (MMRs) are common in inpatient care and may represent both medication distribution-system vulnerabilities and uncertainty-driven communication. This quality improvement study characterized electronic health record (EHR)-generated MMRs to identify where requests originated, when they occurred, and why they were submitted, to inform actionable intervention targets. Methods: This IRB-exempt, retrospective quality improvement initiative reviewed MMRs submitted from inpatient nursing units to the inpatient pharmacy during a 30-day period (September 2020) at a 258-bed community teaching hospital. Messages were summarized by nursing unit, hour of day, and day of week, and categorized using a structured operational schema to identify dominant drivers and priorities. A limited post-implementation assessment tracked aggregate message volume to evaluate directional change after targeted workflow interventions. Results: A total of 13 915 MMRs were submitted during the study period (mean 464/day; approximately 20/hour; ~1 message every 3 minutes). Requests clustered in higher-acuity areas and peaked around nursing shift-change and medication administration rhythms (approximately 0800 and 2000). The most frequent categories were continuous infusion redispense (4189; 30.1%), request for dose despite initial dispense (1712; 12.3%), and inability to locate a cart-fill item (1488; 10.7%). Patterns including redundant messages and requests sent soon after dispensing suggested limited visibility into medication status and opportunities to improve transparency and workflow expectations. Following implementation of targeted workflow changes, aggregate message volume decreased by 14.7% (median 11 870; IQR, 11 726-12 045), representing a median reduction of 2045 messages (IQR, 1870-2189) from baseline. Conclusion: High-volume MMRs can serve as a practical diagnostic signal of medication-use system vulnerabilities and communication workarounds. Characterizing MMRs by location, timing, and request type can help health systems prioritize targeted interventions, including infusion standardization, automated dispensing cabinet optimization, workflow alignment to predictable demand peaks, and EHR-based tracking tools.
Achey et al. (Mon,) studied this question.