Tech leaders want ‘balanced’ AI regulation in private Senate meeting
AI companies attended the AI Insight Forum in Washington, DC.
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.
American teenagers start their school day too early.
Medical experts have been clear for years: sleep cycle changes during puberty make it hard for teenagers to learn in the early morning. Later school start times have been linked to better sleep, improved mental health, stronger academic performance and even safer driving.
Most school district leaders know about this problem and care about fixing it. But changing the schedule can feel operationally impossible.
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AI companies attended the AI Insight Forum in Washington, DC.
Sunderesh Heragu, Associate Dean in the College of Engineering, Architecture and Technology at Oklahoma State University
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Finding the right medication regimen to treat Parkinson’s disease (PD) is a complex healthcare challenge. Wearable health trackers provide physicians with a detailed window into patients’ symptoms, but translating this complex data into useful treatment insights can be difficult. New research in the INFORMS journal Management Science accomplishes just that. Researchers found that combining wearable health tracker data with state-of-the-art algorithms results in promising treatment strategies that could improve PD patients’ outcomes.
Transparency around data collection and risk assessments should be mandated by law, especially in high-risk applications of AI.

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