2026 Southeastern Residency Conference

Background: Onsite clinical pharmacists balance order verification with a range of concurrent responsibilities. These include consults, multidisciplinary rounds, admission and discharge medication reconciliation, and constant involvement with their respective patient care teams. Increasing demands in any one of these areas can limit availability for higher-value clinical activities. A centralized remote order verification (ROV) pharmacist model was created to alleviate these demands. The intention was to support safe and timely order verification, redistribute workload, and allow onsite clinical pharmacists to prioritize clinical responsibilities while maintaining operational efficiency.

Methods: An ROV pilot was first implemented at a single hospital for a 2-week period. The ROV pharmacist would be allowed to verify medication orders remotely, while multiple order types were excluded from the ROV scope. Exclusions included total parenteral nutrition (TPN) orders, pediatric orders (<18 years old), intensive care unit (ICU) orders, non-formulary medications, medications with a listed health-system defined criteria for use (CFU), and a predefined list of emergency department orders. The ROV pharmacist was also allowed to practice within the health-system’s established clinical scope which included interventions such as dose optimization, intravenous-to-oral, and therapeutic interchanges. This ROV model was later approved to expand via three additional 2-week pilots across seven sites. These pilots were also used as an opportunity to test the capacity of a single ROV pharmacist to support multiple campuses. One pilot involved having the ROV pharmacist cover three sites simultaneously, while the other two pilots involved covering two sites at once. A single ROV pharmacist covered all pilot periods. The general model was implemented at most participating sites. At two larger hospitals, the pilot was modified to align with the sites’ existing workflows, and these sites assigned campus designated verification queues to the ROV pharmacist.

Results: After the initial pilot supported the concept, the second pilot showed the ROV pharmacist verified 29.76% of all orders during the shift time, with a total of 7,562 orders verified. Time in the queue shifted more to the 5 to 10 minute and 10 to 15 minute ranges, but the overall dispensing turnaround time decreased by one minute and 21 seconds. The third pilot saw similar numbers with the ROV pharmacist verifying 32.76% of all orders, 7,072 orders in total, and a similar shift with time in the queue. The dispensing turnaround time remained steady increasing by 12 seconds.

Conclusions: The ROV model was able to effectively absorb a significant verification burden without having any operationally significant slowdown. Overall, the model supports capability of creating additional capacity for onsite pharmacist to focus on other clinical and time intensive tasks.