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

Area of Science:

• Pharmacy Management
• Health Services Research
• Operations Management

Background:

• Hospital pharmacies face challenges in aligning staffing levels with fluctuating patient volumes.
• Traditional staffing models may not accurately reflect real-time workload demands.
• Optimizing pharmacy staffing is crucial for operational efficiency and cost control.

Purpose of the Study:

• To describe the creation and implementation of data-driven staffing-to-demand models in two hospital pharmacy settings.
• To evaluate the effectiveness of these models in managing pharmacy labor costs and productivity.
• To provide a framework for pharmacy managers to adjust staffing based on predictive workload metrics.

Main Methods:

• Development and implementation of predictive workload tools based on hospital volume metrics.
• Establishment of clear productivity monitoring systems and processes for adjusting staff hours.
• Correlation analysis between measured pharmacy workload and adjusted census formulas.

Main Results:

• Successful implementation of data-driven staffing models at two institutions.
• Achieved significant financial savings through reduced labor costs, approximately $42,000 and $45,500 over three months.
• Maintained 100% productivity, enabling the replacement of vacant positions without permanent staff reductions.

Conclusions:

• Predictive workload tools serve as effective guidelines for pharmacy managers to align staffing with hospital volume.
• These models facilitate transparent communication and rationale for staffing adjustments.
• The implemented models demonstrate a successful strategy for optimizing pharmacy operations and achieving financial efficiencies.