Flexible nurse staffing based on hourly bed census predictions

Workloads in nursing wards depend highly on patient arrivals and lengths of stay, both of which are inherently variable. Predicting these workloads and staffing nurses accordingly are essential for guaranteeing quality of care in a cost-effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies that allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center Amsterdam (AMC). This case study demonstrates the method's potential to evaluate the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, as well as surgical block planning. Inspired by the quantitative results, the AMC concluded that implementing this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations in the upcoming years.