A system dynamics approach to workload management of hospital pharmacy staff: modelling the trade-off between dispensing backlog and dispensing errors

Background (or Rationale): The traditional hospital pharmacy staffing management model does not account for the complex interactions of social, technical, and environmental factors that can affect performance and safety. Conventionally, workload and dispensing errors within the hospital pharmacy system have been analysed on a factor-by-factor level, using linear and static approaches that ignore feedback mechanisms. Purpose: We aimed to explore the potential of a system dynamics approach to modelling staffing level management in a hospital pharmacy. Methods: Qualitative and quantitative system dynamics models were created to simulate dynamic aspects contributing to dispensing backlog and errors in a hospital pharmacy. A baseline scenario was tested in a “normal” condition, and three different staffing level scenarios (fixed, flexible, and equivalent-fixed) were tested in an extreme condition (hospital winter pressures). Results: During hospital winter pressures, the unintended negative effect on rework due to dispensing errors made it more challenging to deal with demand variability. Findings from the scenario-based simulations revealed that a flexible staffing level arrangement, which dynamically adjusts the number of staff to demand variability during winter pressure, is less effective in reducing the amount of rework than maintaining an equivalent-fixed staffing level. Dispensing backlog during winter pressure can be averted or substantially diminished by proactively employing an equivalent-fixed staffing level that accounts for total staff capacity needed vis-à-vis the current workload. Premature release of extra staff and delayed calling of additional staff from wards can have significant impacts on backlog. Conclusions: Our results demonstrate that system dynamics can provide practical insights into staffing level management in a hospital pharmacy, by accounting for dynamic factors causing dispensing backlog and errors and presenting decision-makers with a holistic understanding of elements affecting system safety and performance.