Assessing the Operational Efficiency of a Vial Filling Robot in an Outpatient or a Community Pharmacy
Author(s)
Eric P. Borrelli, PhD, PharmD, MBA1, Rohan Jamal, PharmD, MBA2, Mia Weiss, MPH2, Doina Dumitru, PharmD, MBA, FASHP3, Julia Lucaci, MS, PharmD2.
1Manager, HEOR, Becton, Dickinson and Company, San Diego, CA, USA, 2Becton, Dickinson and Company, Franklin Lakes, NJ, USA, 3Becton, Dickinson and Company, San Diego, CA, USA.
1Manager, HEOR, Becton, Dickinson and Company, San Diego, CA, USA, 2Becton, Dickinson and Company, Franklin Lakes, NJ, USA, 3Becton, Dickinson and Company, San Diego, CA, USA.
Presentation Documents
OBJECTIVES: Burnout among retail and outpatient pharmacy workers has been on the rise, driven by increasing prescription volumes, staffing shortages, and nonclinical activities such as manual medication vial-filling. Pharmacy automation, such as vial-filling robots, has been shown to enhance operational efficiency while reducing the time needed for manual tasks. This analysis aimed to evaluate the operational and financial impact of implementing a vial-filling robot on labor time in outpatient and community pharmacies.
METHODS: An operational model was created to assess the labor impact of implementing a vial-filling robot on staff labor time. The model was applied to three different scenarios: a pharmacy averaging filling 1,500, 3,500, and 6,000 prescriptions per week. It was assumed that 30% of prescription volume would be processed using the robot, and 90% of fills would be completed by technicians. Peer-reviewed literature and time study data were used to estimate the average labor time for manual and automated fills. Labor cost impacts were calculated using U.S. Bureau of Labor Statistics data.
RESULTS: Implementing a vial-filling robot led to weekly labor savings of 17 technician hours and 2 pharmacist hours for the 1,500-prescription scenario, 39 technician hours and 4 pharmacist hours for the 3,500-prescription scenario, and 68 technician hours and 8 pharmacist hours for the 6,000-prescription scenario. This equates to annual cost savings of $16,997 to $67,989 technician labor and $6,377 to $25,506 pharmacist labor, which can potentially be reallocated to non-dispensing, clinical or revenue-generating activities.
CONCLUSIONS: The implementation of vial-filling robots substantially reduces labor pressure on pharmacy staff, and allows reallocation of time to higher-value, and potentially more clinically focused tasks. This automation technology has the potential to improve operational efficiency across varying community and outpatient settings.
METHODS: An operational model was created to assess the labor impact of implementing a vial-filling robot on staff labor time. The model was applied to three different scenarios: a pharmacy averaging filling 1,500, 3,500, and 6,000 prescriptions per week. It was assumed that 30% of prescription volume would be processed using the robot, and 90% of fills would be completed by technicians. Peer-reviewed literature and time study data were used to estimate the average labor time for manual and automated fills. Labor cost impacts were calculated using U.S. Bureau of Labor Statistics data.
RESULTS: Implementing a vial-filling robot led to weekly labor savings of 17 technician hours and 2 pharmacist hours for the 1,500-prescription scenario, 39 technician hours and 4 pharmacist hours for the 3,500-prescription scenario, and 68 technician hours and 8 pharmacist hours for the 6,000-prescription scenario. This equates to annual cost savings of $16,997 to $67,989 technician labor and $6,377 to $25,506 pharmacist labor, which can potentially be reallocated to non-dispensing, clinical or revenue-generating activities.
CONCLUSIONS: The implementation of vial-filling robots substantially reduces labor pressure on pharmacy staff, and allows reallocation of time to higher-value, and potentially more clinically focused tasks. This automation technology has the potential to improve operational efficiency across varying community and outpatient settings.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
EE451
Topic
Economic Evaluation
Topic Subcategory
Budget Impact Analysis, Cost/Cost of Illness/Resource Use Studies, Value of Information
Disease
No Additional Disease & Conditions/Specialized Treatment Areas