Critical Care Capacity Management: Understanding the Role of a Step Down Unit

Abstract

In hospitals, Step Down Units (SDUs) provide an intermediate level of care between the Intensive Care Units (ICUs) and the general medical‐surgical wards. Because SDUs are less richly staffed than ICUs, they are less costly to operate; however, they also are unable to provide the level of care required by the sickest patients. There is an ongoing debate in the medical community as to whether and how SDUs should be used. On one hand, an SDU alleviates ICU congestion by providing a safe environment for post‐ICU patients before they are stable enough to be transferred to the general wards. On the other hand, an SDU can take capacity away from the already over‐congested ICU. In this work, we propose a queueing model of patient flow through the ICU and SDU in order to determine when an SDU is needed, what size it should be, and what are the main drivers influencing these decisions. Using first‐ and second‐order analysis, we examine the tradeoff between reserving capacity in the ICU for the most critical patients and gaining additional capacity achieved by allocating nurses to the SDU due to the lower staffing requirement. We find that under some circumstances the optimal size of the SDU is zero, while in other cases, having a sizable SDU may be beneficial. Moreover, we identify two parameters which play a prominent role in the SDU sizing decision: p, which captures the demand for SDU beds, and ν, which captures the supply gains by moving nurses to the SDU. The insights from our work provide rigorous justification for the variation in SDU use seen in practice as well as highlight which factors should be considered when making such sizing decisions for critical care.

Staffing Tandem Queues with Impatient Customers Application in Financial Service Operations

“…We demonstrate how to extend our model to include other features and achieve wider applicability. For example, it can be modified to contain tandem queueing networks with flexible servers; with flexible servers, it can optimally solve the ICU-SDU model studied by Armony et al (2017).…”

“…As such systems are likely too complex to have a closed form solution, several authors suggest using approximations. For example, Zychlinski et al (2017) use fluid approximation to analyze tandem queues with blocking, while Armony et al (2017) use diffusion approximation to analyze a tandem healthcare system with flexible servers and abandonment. Our theoretical contribution is to develop an efficient numerical exact solution for a Markovian two-station tandem queues with abandonment and blocking.…”

“…Staffing a single-stage queue only requires the total number of servers, but staffing a queueing network requires assigning these servers to different stations. Armony et al (2017) use diffusion approximation to study a healthcare system with an upstream Intensive Care Unit (ICU) and a downstream Step Down Unit (SDU), where critical patients arriving at the ICU may abandon and the SDU has no waiting room for semi-critical patients who have been served by the ICU. In this application, the ICU beds are flexible, so semi-critical patients can be served in ICU, but critical patients have preemptive priority over semi-critical customers in the ICU.…”

“…Notably, whereas diffusion approximation results are only valid when the workload is high, our method can provide exact analysis for any workload. Moreover, incorporating external arrivals into the downstream SDU, a practical element not captured by the model in Armony et al (2017), is straightforward as per the discussion in Section 2.…”

Assessing the Impact of Service Level When Customer Needs Are Uncertain: An Empirical Investigation of Hospital Step-Down Units

“…Gans et al (2003)), hospitals (e.g. Armony et al (2017), Best et al (2015)), cloud-computing (e.g. Maguluri et al (2012)), among many others.…”