Creating pharmacy staffing-to-demand models: Predictive tools used at two institutions

The creation and implementation of data-driven staffing-to-demand models at two institutions are described. Predictive workload tools provide a guideline for pharmacy managers to adjust staffing needs based on hospital volume metrics. At Abbott Northwestern Hospital, management worked with the department's staff and labor management committee to clearly outline the productivity monitoring system and the process for reducing hours. Reference charts describing the process for reducing hours and a form to track the hours of involuntary reductions for each employee were created to further enhance communication, explain the rationale behind the new process, and promote transparency. The University of Minnesota Medical Center-Fairview, found a strong correlation between measured pharmacy workload and an adjusted census formula. If the daily census and admission report indicate that the adjusted census will provide enough workload for the fully staffed department, no further action is needed. If the census report indicates the adjusted census is less than the breakeven point, staff members are asked to leave work, either voluntarily or involuntarily. The opposite holds true for days when the adjusted census is higher than the breakeven point, at which time additional staff are required to synchronize worked hours with predicted workload. Successful staffing-to- demand models were implemented in two hospital pharmacies. Financial savings, as indicated by decreased labor costs secondary to reduction of staffed shifts, were approximately $42,000 and $45,500 over a three-month period for Abbott Northwestern Hospital and the University of Minnesota Medical Center-Fairview, respectively. Maintenance of 100% productively allowed the departments to continue to replace vacant positions and avoid permanent staff reductions.