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In most emergency departments, beds are a hot commodity. But some patients don’t actually need a bed for their treatment – and accurately identifying those people is a low-lift but effective way to increase efficiency without sacrificing care quality, finds a study recently published in the journal Management Science.

The researchers developed a simple decision tree, informed by mathematical and machine learning models, to help staff decide whether a patient truly needs a bed or can be routed to a “vertical processing pathway” – that is, an area where they can be treated sitting up. Many hospitals already have such an area but don’t necessarily have a systematic way to decide which patients are treated there.

The artificial intelligence (AI) boom has ignited the demand for data centers. Goldman Sachs predicts a doubling of the power demand to run the data centers in just two years.

Generative AI tools such as ChatGPT, Gemini, and Preplexity require a significant amount of computing power to train large language models so they are able to answer user queries coming from the average person who is doing a search about basketball game statistics to investment bankers who are looking for nuggets of information that will help them invest in high return stocks.