Flexible nurse staffing based on hourly bed census predictions

Abstract

a b s t r a c tWorkloads 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.

Daily nurse requirements planning based on simulation of patient flows

“…The assumption of a flexible workforce has been adopted as it is currently practised in many hospitals. In the case of floating nurses-within a hospital's buffer capacity and dynamically allocated to different medical units-wages are likely to be more expensive due to their higher specialization and cross-training (Kortbeek et al 2015). This aspect needs to be investigated in depth in further studies, comparing the savings from assuring a high service level (also rescheduling fewer nurses than planned) to the costs of paying for more qualified nurses.…”

“…Nurses were assigned to four different care units characterized by distinct load parameters, such as the number of beds available, as well as statistics related to occupation or the available number of working days, the duration of shifts, etc. Another recent contribution is that of Kortbeek et al (2015), addressing a stochastic method for allocating nurse staffing based on hourly bed census prediction, but not considering the specific health condition (acuity) and needs of patients occupying beds.…”

“…On the other hand, unbalanced or understaffed nursing teams can influence the quality of care (Hurst 2003), even determining burnouts and job dissatisfaction for nurses and a higher risk of mortality for patients (Aiken et al 2002). Predicting workload over time and allocating nurses accordingly are essential for guaranteeing quality of care in a cost-effective manner (Kortbeek et al 2015;Molema et al 2007). This can be realized by means of a flexible workforce, which is considered a powerful tool to respond to variability in patient demand by many authors (Kortbeek et al 2015).…”

“…Predicting workload over time and allocating nurses accordingly are essential for guaranteeing quality of care in a cost-effective manner (Kortbeek et al 2015;Molema et al 2007). This can be realized by means of a flexible workforce, which is considered a powerful tool to respond to variability in patient demand by many authors (Kortbeek et al 2015). It has been adopted all over the world in various modes, including part-time employees, overtime, temporary agency employees and float nurses (Gnanlet and Gilland 2009), providing pools of cross-trained nurses assigned to specific care units at the start of each shift, in an attempt to balance nurse requirement fluctuations among wards while reducing the total employee buffer capacity.…”

Time‐Staged Overtime Staffing for Services with Updated Forecasts and Availabilities

“…Belien, Demeulemeester, and Cardoen () describe how a “master surgical schedule” drives demand for resources at a hospital. Similarly, Kortbeek, Braaksma, Burger, Bakker, and Boucherie () describe relationships between surgical planning and staffing requirements. Kim and Horowitz () also describe how elective surgeries affect demand for hospital services in a fairly predictable way.…”