(Management Case Study) Better Together: Leveraging Frontline Pharmacy Technician Insights and Data Analytics to Optimize Workflows

Purpose: Following some pharmacy technicians expressing frustration with a heavy delivery workload, several opportunities were identified to optimize operational workflows. This case presents a framework for redesigning central pharmacy and delivery workflows using a FOCUS-PDCA model incorporating frontline pharmacy technician insights as well as institutional data. In doing so, we were able to increase efficiency, distribute the work equitably, and allow pharmacy technicians to participate in more activities at the top of their license such as medication histories thus mitigating burn-out.

Methods: A core team consisting of two pharmacy technicians, the pharmacy director, and the informatics pharmacist met to identify what mattered most to the team when updating the workflow, create a process map of the current workflow, identify areas to increase efficiency, and define metrics to gauge success. Based on technician input, the team focused on timing batch deliveries to minimize interruptions, optimizing decentralized inventory, and reducing the number of restocks needed for the automated dispensing machines. Analyses were performed to identify times for technicians to deliver that: 1) minimized competition at the automated dispensing machines with nursing, 2) ensured adequate technician coverage in the main pharmacy, and 3) were offset from times of high order verification. Algorithms were developed to determine which items should be stocked decentrally and in what quantities based on institutional data. Inventory optimization metrics were set with pre-specified thresholds for success and evaluated using T-Tests. The technicians collaborated to develop task lists that distributed the workload equitably. The technician champions and director engaged the rest of the department through a combination of meetings and one-on-one conversations. They emphasized the “why” behind the changes prior to operationalization and the iterative process for change in the FOCUS-PDCA model. Following go-live, the core team developed strategies for continual monitoring and quality improvement based on the quantitative metrics as well as qualitative frontline staff feedback.

Results: Roles of each pharmacy technician in the new workflow were clearly defined. The percentage of medications dispensing from the main pharmacy decreased from 14.3% (17,918/125,601) to 11.3% (16,617/147,293) [p < 0.01]. This equated to an average of 4,180 dispenses less per month that needed to be filled patient specific. The issue to restock ratio for optimized automated dispensing cabinets increased from 10.5 to 13.5 [p < 0.01]; this equated to an average of 2,575 fewer restock events per month despite an overall increase in dispense volumes over the same time period. Due to the increased efficiency of the technician workflow, resources could be re-allocated to create a full-time medication history technician position. Frontline pharmacy technicians reported fewer conflicts with nursing, better break coverage, more resiliency when there were call-outs, and that the environment felt less stressed.

Conclusion: Combining the lived experience of workflow processes that frontline pharmacy technicians possess and data-analytics within the FOCUS-PDCA model resulted in powerful insights that lead to meaningful workflow updates that led to a more positive work environment. Leveraging frontline pharmacy technician champions throughout the process increased buy-in as the team navigates through various tests of change.