Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed‐methods approach. (3rd August 2021)

• Record Type: Journal Article
• Authors: Ostberg, Nicolai; Ling, Jonathan; Winter, Shira G.; Som, Sreeroopa; Vasilakis, Christos; Shin, Andrew Y.; Cornell, Timothy T.; Scheinker, David
• Is Part Of: Journal of nursing management. Volume 29:Number 7(2021)
• Page Start: 2278
• Page End: 2287
• Publication Date: 2021-08-03
• Subjects: burnout -- nurse staffing -- paediatric intensive care -- simulation -- staffing ratios; Nursing services -- Administration -- Periodicals; Nursing services -- Business management -- Periodicals; 610.73068
• DOI: 10.1111/jonm.13346
• Languages: English
• ISSNs: 0966-0429

Abstract

Abstract: 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.