Determining the Number of Bariatric Beds Needed in a U.S. Acute Care Hospital. (July 2021)

• Record Type: Journal Article
• Authors: Magazine, Michael; Murphy, Matt; Schauer, Daniel P.; Wiggermann, Neal
• Is Part Of: HERD. Volume 14:Number 3(2021)
• Page Start: 14
• Page End: 26
• Publication Date: 2021-07
• Subjects: hospital resource planning -- cost optimization -- simulation modeling -- bariatric bed; Health facilities -- Design and construction -- Periodicals; Medical care -- Periodicals; Health facilities -- Design and construction; Medical care; Periodicals; 725.5
• DOI: 10.1177/19375867211012488
• Languages: English
• ISSNs: 1937-5867

Abstract

Aim: This project used historical hospital data to forecast demand for specialized bariatric beds. Models were evaluated that determined the relationship between the number of bariatric beds owned and service level for patients of size requiring these beds. A calculator was developed for minimizing the equipment costs of meeting demand. Background: Failing to provide enough bariatric beds may negatively affect outcomes for patients of size and healthcare workers, whereas owning more bariatric beds than required to meet demand means unnecessary cost. With rising rates of obesity increasing care costs, minimizing equipment costs is increasingly important. Method: One year of hospital admissions data were used to determine arrival rates and lengths of stay for patients of size. Two subsequent years verified the consistency of these rates. Simulations modeled the flow of patients of size through the hospital and the service level associated with the number of beds owned. A minimization function determined the optimal number of bariatric beds to be provided. A simplified, generalizable model was compared to the simulation. Results: The simplified model produced similar results to more complex simulation. The optimization was robust, or insensitive to small changes in inputs, and identified substantial opportunity for savings if demand for beds was substantially over- or underestimated. Conclusions: The simplified model and cost optimization could be used in many situations to prevent costly errors in equipment planning. However, hospitals should consider customized simulation to estimate demand for high-cost equipment or unique circumstances not fitting the assumptions of these models.