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Managing ICU throughput and understanding ICU census

Authors: Michael D, Howell;

Managing ICU throughput and understanding ICU census

Abstract

Traditionally, hospitals have coped with chronically high ICU census by building more ICU beds, but this strategy is unlikely to be tenable under future financial models. Therefore, ICUs need additional tools to manage census, inflow, and throughput.Higher ICU census, without compensatory surges in nursing capacity, is associated with several adverse effects on patients and providers, but its relationship to mortality is uncertain. Providers also discharge patients more aggressively during times of high census. Little's Law (L = λ W), a cornerstone of queuing theory, provides an eminently practical basis for managing ICU census and throughput. One target for improving throughput is minimizing process steps that are without value to the patient, e.g., waiting for a bed at ICU discharge. Larger gains in ICU throughput can be found in ICU quality improvement. For example, spontaneous breathing trials, daily wake-ups, and early physical/occupational therapy programmes are all likely to improve throughput by reducing ICU length of stay. The magnitude of these interventions' effects on ICU census can be startling.ICUs should actively manage throughput and census. Operations management tools such as Little's Law can provide practical guidance about the relationship between census, throughput, and patient demand. Standard ICU quality improvement techniques can meaningfully affect both ICU census and throughput.

Related Organizations
Keywords

Intensive Care Units, Hospital Bed Capacity, Decision Making, Humans, Censuses, Patient Care, Length of Stay, Safety, United States

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
38
Top 10%
Top 10%
Top 10%
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