Abstract
Aim: 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 pediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modeled 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.
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 pediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modeled 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.
| Original language | English |
|---|---|
| Pages (from-to) | 2278-2287 |
| Number of pages | 10 |
| Journal | Journal of Nursing Management |
| Volume | 29 |
| Issue number | 7 |
| Early online date | 24 Apr 2021 |
| DOIs | |
| Publication status | Published - Oct 2021 |
Funding
We thank the numerous doctors, nurses, analysts, administrators and others from the Lucile Packard Children's Hospital that provided us with data and extensive feedback via interviews.