Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed-methods approach

Aim To identify, simulate and evaluate the formal and informal patient-level and unit-level factors that nurse managers use to determine the number of nurses for each shift. Background Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last-minute adjustments or inappropriate staffing levels. Methods Staffing schedules at a paediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modelled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels. Results Nurse-to-patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift. Conclusion Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand. Implications for Nursing Management Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.

Automated summary

This study aimed to identify and evaluate the factors that nurse managers use to determine the number of nurses for each shift. Through simulation and multivariate regression, it was found that patient turnover was the primary factor causing discrepancies in nurse staffing levels, and new rules were able to reduce this difference, allowing for better matching of demand with nurse schedules.