Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed-methods approach.
burnout
nurse staffing
paediatric intensive care
simulation
staffing ratios
Journal
Journal of nursing management
ISSN: 1365-2834
Titre abrégé: J Nurs Manag
Pays: England
ID NLM: 9306050
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
revised:
08
04
2021
received:
06
01
2021
accepted:
16
04
2021
pubmed:
25
4
2021
medline:
13
10
2021
entrez:
24
4
2021
Statut:
ppublish
Résumé
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. 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. 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. 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. Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand. Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.
Sections du résumé
AIM
OBJECTIVE
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
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
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
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
CONCLUSIONS
Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand.
IMPLICATIONS FOR NURSING MANAGEMENT
CONCLUSIONS
Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.
Types de publication
Journal Article
Langues
eng
Pagination
2278-2287Informations de copyright
© 2021 John Wiley & Sons Ltd.
Références
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