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Intensive care unit staffing during the periods of fluctuating bed occupancy: An alternative dynamic model
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Intensive care unit staffing during the periods of fluctuating bed occupancy: An alternative dynamic model
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Journal Article
Intensive care unit staffing during the periods of fluctuating bed occupancy: An alternative dynamic model
2021
Overview
Staffing is the single biggest cost component in the critical care budgets. Due to the fluctuation in both bed occupancy and the level of care needs, nursing staff requirement can vary considerably from day to day. This makes the traditional ‘fixed roster’ staffing system inefficient, costly and potentially unsafe. In this study, we used the existing bed occupancy data to test the viability two ‘dynamic’ workforce management models.
Nursing requirement data were prospectively collected over one year at a thirty-two-bed critical care unit. Using mathematical models, we then tested the concept of two alternative workforce management models and compared the level of staffing, as well as the estimated cost per year. The first was an ‘on-call’ model, which was a two-tier roster with a standard staffing level and an additional on-call component; the second was a ‘predictive’ model, which estimated the staffing requirement based on the bed occupancy a few days prior.
Single centre study in a busy district general hospital with a 32-bed critical care unit.
The number of days with safe staffing levels and the cost of the alternative workforce management models.
Data were collected over 331 days. The on-call model was estimated to cost 16% less per year (£431,320, or 2,630 nurse-shift equivalent) compared to the fixed roster, while fulfilling the adequate staffing standards in 97% of the days. While the predictive model could also be used to improve the workforce efficiency, this was overall less efficient than the on-call model.
The modelled data suggests that the implementation of an ‘on-call’ model in critical care nursing rostering could potentially improve coverage and appear to be cost effective.
