Estimating the Relationship Between Nurse Staffing and Medication Pass Workload Using National Barcode Data.

Background: Measuring and assessing the relationship between inpatient nurse staffing and workload across a national health system is difficult because of challenges in systematically observing inpatient workload at the unit level.

Objective: The objective of this study was to apply a novel measure of inpatient nurse workload to estimate the relationship between inpatient nurse staffing and nurse workload at the unit level during a key nursing activity: the peak-time medication pass.

Methods: A retrospective observational study was conducted in the Veterans Health Administration, the largest employer of nurses in the United States. The sample included all patients ( n = 1,578,399 patient days) admitted to 311 non-intensive care unit inpatient acute care units in 112 hospitals in 2019 (104,588 unit days). Staffing was measured as the unit-level, nurse-to-patient ratio, and workload was measured using average time (duration) for RNs to complete the peak-time medication pass.

Results: We found a negative relationship between the RN-to-patient ratio and average peak-time medication pass duration after adjusting for unit-level patient volume and average patient severity of illness and other unit-level factors. This relationship was nonlinear: The marginal effect of staffing on workload decreased as staffing increased.

Discussion: As unit-level nurse staffing increased, average RN workload decreased. This result suggests that interventions to improve nurse staffing may have larger nonlinear effects for units with lower staffing levels. Understanding the effect of differing staffing decisions on variations in nursing workload is critical for adopting models of care that effectively use scarce staffing resources and contribute to retaining nurses in the inpatient workforce. This work provides evidence that peak-time medication pass duration is a valid process-based measure of workload and highlights the potential diminishing returns to increasing staffing.