The INFORMS Analytics+ Conference showcased how companies are turning advanced analytics and AI into measurable operational results
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.
Diversity alone won’t get the votes for funding public education. It takes communities that are more integrated and connected, where residents have a sense of shared stake.
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They’ve slowed for now, but as we begin to emerge from our homes, we need to brace for a resurgence.
A new COVID-19 model from Georgia Tech projects increases of thousands of cases and deaths in the state by mid-August. The university’s Center for Health and Humanitarian Systems and Health Analytics Group say that depending on how much the public follows physical distancing measures in the coming weeks, Georgia’s total number of COVID-19 deaths could range from 6,100 to 17,900 by that August time frame.
Each day behind closed doors in a handful of Wichita offices, they pick up the phone and start dialing. On the other end of the line are the latest people to test positive for the coronavirus in Sedgwick County and those who came close to them in the days before their symptoms began.
University of Toronto researchers in the Faculty of Applied Science & Engineering have designed a tool to help hospitals make the most of their resources during the COVID-19 pandemic by more efficiently matching staff to open jobs.
A study has found that popular rideshare platforms present racial and other biases that penalise under-represented minorities and others seeking to use their services.
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