Managing Hospital Inpatient Bed Capacity Through Partitioning Care into Focused Wings

Abstract

W e consider the partitioning of care types into wings from the perspective of a hospital administrator who wishes to optimize the use of a fixed number of beds that provide services for heterogeneous care types. The hospital administrator decides on the number of wings to form, the number of beds to allocate to each wing, and the set of care types to assign to each wing to maximize the total utility to the hospital. The administrator faces an inherent trade-off between forming large wings to pool demand and bed capacity, and forming specialized wings to focus on narrow ranges of care types. Specialized wings not only provide advantages from focused care but also allow the protection of beds for high-utility care types. We provide an optimization model for the wing formation decision and address the advantages of focus endogenously in our model. Using data from a large urban teaching hospital in the United States along with a national database, we report on a number of managerial insights. In particular, as the overall demand increases across all care types, wings are formed to reserve more beds for higher-utility types, which leads to higher overall hospital utility but also some disparity across types, such as increased hospital access for some and decreased access for others. Furthermore, overall bed occupancy decreases as the hospital is split into wings. However, if sufficient focus is attained, shorter lengths-of-stay associated with focused care may increase overall patient throughput. We also observe that when patients are willing to wait longer for admission, the hospital tends to form more wings. This implies that hospitals that garner longer waits can form more specialized wings and thereby benefit from focused care, whereas hospitals that cannot will tend to form fewer, if any, wings, choosing to pool demand and bed capacity.

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

“…There has been a growing body of work in healthcare operations management using mathematical models to manage heterogeneous patients in systems with differentiated server types. Best et al (2015) examines how to determine the amount of flexibility allowed in hospital wings in order to minimize costs associated with lack of access to care. Dai and Shi (2017) uses an approximate dynamic programming approach to determine how to allocate patients to primary and non-primary 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.…”

Queueing Models for Patient-Flow Dynamics in Inpatient Wards

“…The literature concerning hospital queues is quite large, with much attention given to the ED, operating rooms and ICUs, see for example Song et al (2015), Chan et al (2012, Green and Savin (2008); here we focus on IW-related queueing models. Best et al (2015) consider an optimal partitioning of the hospital beds to wings, and employ the Erlang-A model to approximate the fraction of patients that abandon when waiting for a bed. The interaction between the ED and the hospital wards is studied in Mandelbaum et al (2012), where the hospital is modeled as an inverted V model, and the "randomized most-idle" policy is proposed so as to ensure servers fairness (measured in bed idleness ratios) in an asymptotic diffusion limit for a stationary system.…”

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

“…The hospital's objective is to determine the size of the SDU and ICU and the balking threshold in order to minimize the costs associated with patient balking, abandonment, holding in queue, and bumping. Cost minimization and reward maximization formulations are common in the healthcare literature (see e.g., Best et al 2015, Chan et al 2012, Green et al 2006a, Mason et al 2017, Mills et al 2013.…”