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Implementation of a passive bin-based perpetual medication inventory model within ambulatory clinics at an academic
Elly Glazier1, Jared Gabbert2, Grayson Peek3
1Inpatient Pharmacy Operations, Vanderbilt University Medical Center, Nashville, TN, USA.
Purpose:
Ambulatory clinics manage high-cost medications with little visibility into quantity or movement, leaving unrealized opportunities for inventory optimization. Automated dispensing cabinets, common in inpatient settings, address this issue but require significant capital investment, forcing clinics into complex workflows to balance demand for high-cost medications with minimizing waste. This study evaluated a passive bin-based inventory model that tracked clinic transactions in real time using light sensors to log product removal and replacement. The model uses artificial intelligence and various algorithms to recommend inventory optimizations based on transaction data and notably requires no electronic health record integration.
Methods:
This 10-week study included select medications at 2 ambulatory locations and assessed whether the utilization of a passive bin-based inventory model allowed a decrease in inventory on-hand valuation. Inventory valuation was assessed before implementation and following implementation of system recommendations at the conclusion of the pilot. The primary outcome was the change in inventory on-hand valuation. The accuracy of the system was validated via twice-weekly manual cycle count.
Results:
The assessed model recorded 3,454 dispenses during the study period. The average days on hand varied widely, and the total inventory valuation decreased by $34,000 of average wholesale price, although this cannot be extrapolated due to volume. The results of the modified MAS-NAS nursing satisfaction survey were mixed and not generalizable.
Conclusion:
In this prospective pre- vs postimplementation study, the utilization of a passive bin-based perpetual inventory model reduced inventory valuation but the difference was not statistically significant. While the model enhanced visibility in a challenging setting, further studies are necessary.
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