Staggered work shifts: a way to downsize and restructure an emergency department workforce yet maintain current operational performance

Abstract

Starting from the last decade of the twentieth century, most hospital Emergency Department (ED) budgets did not keep up with the demand for ED services made by growing populations and aging societies. Since labor consumes over 50% of the total monies invested in EDs and other healthcare systems, any downsizing, streamlining and reorganization plan needs to first address staffing issues such as determining the correct size of the workforce and its work shift scheduling. In this context, it is very important to remember that downsizing certainly does not mean a general cut-across-the-board. This study shows that a selective downsizing process in which each resource is treated separately (increasing the work capacity of some resources is also possible), based on its unique contribution to the overall ED operational performance, can approximately maintain current ED operational measures in terms patient length of stay (LOS) despite an overall reduction in staff hours. A linear optimization model (S-model) and a heuristic iterative simulation based algorithm (SWSSA) are used in this study for scheduling the resources' work shifts, one resource at a time. The algorithm was tested using data that was gathered from five general hospital EDs. By leveling the workload of the different resources in the ED, SWSSA was able to achieve LOS values within -19 to 4% of the original values despite a reduction of 8-17.5% in physicians' work hours and a reduction of 13-47% in the nurses' work hours.

A Simulation Study to Improve Quality of Care in the Emergency Department of a Community Hospital

“…Such a tool can help health care management evaluate the efficiency of the current practices, carry out "what if" analyses to predict the impact of staffing, resource, and operational changes to determine the optimal system configurations, and investigate the relationships or trade-offs among system variables. For example, recent simulation studies on reducing patient waiting time, determining ED configuration, and allocating resources are reported by Samaha et al, 3 Sinreich and Marmor, 4 Komashie and Mousavi, 5 Hung et al, 6 Sinreich and Jabali, 7 Hoot et al, 8 and Brenner et al 9 The community hospital in this study is a 468-bed tertiary medical center offering patient care to the community of central and eastern parts of the state. The emergency department at this community hospital also faces the challenges of increasing patient visits (48,000 annually), a nursing workforce shortage, and long delays.…”

Bloody fast blood collection

“…From a technical point of view, the paper by Izady and Worthington [113] is most closely related to this chapter, as they use similar performance indicators: sojourn time percentiles and average waiting times. The other three papers that use a network of queues [4,45,168] only use simulation for performance evaluation. Two of these papers, the papers by Ahmed and Alkhamis [4] and by Centeno et al [45], use a sojourn time percentile for performance evaluation, similar to this chapter.…”

“…Of those papers, seven are applied to an emergency department [4,45,56,98,113,168,191] and one is applied to ambulance services [78]. The paper by Defraeye and Van Nieuwenhuyse uses a similar performance metric as this chapter -the expected waiting time.…”

Integrated decision making in healthcare : an operations research and management science perspective

“…Staggered shift scheduling is when shifts do not have to start and finish at the same time. This results in more flexibility to accommodate shifts to the required staffing levels at specific intervals, leading to improved utilization of resources [447]. Shift schedules are impacted by the preferences of staff and by laws prescribing emergency staff is only available for a limited number of hours [164,220].…”

“…Methods: computer simulation [267,447,448,532], heuristics [447,448], queueing theory [215,216,220], literature review [231,282,393].…”