Capacity Pooling in Hospitals: The Hidden Consequences of Off-Service Placement

Hummy Song (), Anita L. Tucker (), Ryan Graue (), Sarah Moravick () and Julius J. Yang ()
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Hummy Song: The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Anita L. Tucker: Questrom School of Business, Boston University, Boston, Massachusetts 02215
Ryan Graue: Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215
Sarah Moravick: Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215
Julius J. Yang: Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215

Management Science, 2020, vol. 66, issue 9, 3825-3842

Abstract: Hospital managers struggle with the day-to-day variability in patient admissions to different clinical services, each of which typically has a fixed allocation of hospital beds. In response, many hospitals engage in capacity pooling by assigning patients from a service whose designated beds are fully occupied to an available bed in a unit designated for a different service. This “off-service placement” occurs frequently, yet its impact on patient and operational measures has not been rigorously quantified. This is, in part, because of the challenge of properly accounting for the endogenous selection of off-service patients. We use an instrumental variable approach to quantify the causal effects of off-service placement of hospitalized medical/surgical patients, having accounted for the endogeneity issues. Using data from a large academic medical center with 19.6% of medical/surgical patients placed off service on average, we find that off-service placement is associated with a 22.8% increase in remaining hospital length of stay (LOS) and a 13.1% increase in the likelihood of hospital readmission within 30 days. We find no significant effect on in-hospital mortality or clinical trigger (rapid response) activation. We identify longer distances to the service’s home unit as a key mechanism that drives the effect on LOS. In contrast, a mismatch in nursing specialization does not seem to explain this effect. By quantifying the effects of off-service placement on patient and operational outcomes, we enable clinicians and hospital managers to make better-informed short-term decisions about off-service placement and longer-term decisions about capacity allocation.

Keywords: off-service placement; instrumental variables; capacity pooling; capacity allocation; admission control; congestion; healthcare; empirical operations (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (18)

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