Development and evaluation of a prospective staffing model to improve retention.

Aims: To improve predictability and accuracy of hiring using historical staffing data, quality improvement and workforce engagement.

Background: Twenty-three per cent of newly licensed nurses leave their first job within one year, costing employers $52,100 per nurse replacement. Tools for anticipatory hiring strategies are not available in the literature.

Methods: We used retrospective, secondary data analysis to develop a Prospective Staffing Model and conduct a five-year longitudinal evaluation of the implementation of the model in a convenience sample at a quaternary academic Cardiothoracic Intensive Care Unit. We used a team-based, quality improvement approach to restructure recruitment and hiring strategies, standardize new graduate nurse orientation and implement AACN Healthy Work Environment standards.

Results: Over the five-year prospective evaluation period (2014-2018), 388 nurses were hired and included in the evaluation cohort. Retention increased (n = 286 days) and turnover decreased (17.6%) between 2014 and 2018. Improvements in workforce stability were sustained at five years.

Conclusions: Use of a Prospective Staffing Model is associated with improved nurse retention and decreased turnover, and may improve workforce stability.

Implications For Nursing Management: Results suggest that an innovative tool can mitigate the deleterious effects of turnover, adding to current knowledge and providing a method for anticipatory assessment of local turnover.