Identifying the Bottleneck Unit: Impact of Congestion Spillover in Hospital Inpatient Unit Network

Song-Hee Kim (), Fanyin Zheng () and Joan Brown ()
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Song-Hee Kim: SNU Business School, Seoul National University, Seoul 08826, South Korea
Fanyin Zheng: Columbia Business School, Columbia University, New York, New York 10027
Joan Brown: Keck School of Medicine, University of Southern California, Los Angeles, California 90033

Management Science, 2024, vol. 70, issue 7, 4200-4218

Abstract: Because a hospital is an interconnected, interdependent network of care units, allocating resources—beds, nurses, and improvement initiatives—to one unit to reduce its congestion may have spillover effects on other units. If such congestion spillover is substantial, ignoring it may lead to unintended consequences and missed opportunities. We use data collected over five years from a hospital with 16 inpatient units to empirically examine whether and how much congestion propagates through the network of inpatient units. Our estimation result suggests that the magnitude of the congestion spillover is indeed substantial in our study hospital. For example, increasing one inpatient unit’s utilization by 10 percentage points today can increase its neighboring inpatient unit’s utilization by up to 4.33 percentage points tomorrow. Using counterfactual analyses, we estimate the effect of adding a bed to each unit. We find that due to congestion spillover, adding one bed to the bottleneck unit can free up 4.14 beds in the hospital, which translates to 383.53 more hospital visits per year or a 3% increase in hospital throughput. This effect is about three times bigger in magnitude compared with what one can achieve by naively choosing which unit to add a bed to. Hospitals and other manufacturing and service systems with complex interdependence across resources can use our empirical framework to examine the spillover effect of resources on performance metrics and leverage such understanding to effectively improve their operations.

Keywords: empirical operations management; healthcare; network effect; bottleneck; congestion; congestion spillover; inpatient unit (search for similar items in EconPapers)
Date: 2024
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