ArticleHERD2021
Determining the Number of Bariatric Beds Needed in a U.S. Acute Care Hospital.
Article in HERD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- An Observational Study of the Factors Affecting Staffing Demands During Intrahospital Transfers.HERD · 2026Observational
- Study on value-based design of healthcare facilities: Based on review of the literature in the USA and Japan.Frontiers in public health · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThis 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.
backgroundFailing 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.
methodOne 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.
resultsThe 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.
conclusionsThe 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.
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Registered trials
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