Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates.
Jul 21, 2022
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Emergency departments (EDs) that see fewer pediatric patients are more likely to give delayed diagnoses for serious medical conditions compared to those who see pediatric patients more often, according to a Northwestern Medicine ...
Mar 6, 2024
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Proprietary artificial intelligence software designed to be an early warning system for sepsis can't differentiate high- and low-risk patients before they receive treatments, according to a new study from the University of ...
Feb 15, 2024
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Ordering a palliative care consultation by "default"—via an automatic order programmed into the electronic medical record that doctors may cancel if they choose—is an effective strategy to give more hospitalized patients ...
Jan 16, 2024
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About 20% of patients whose medical records showed them as being alive with a serious illness were in fact deceased according to California data, leading to hundreds of unnecessary interactions such as appointment reminders, ...
Dec 4, 2023
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An artificial intelligence (AI) tool developed at Vanderbilt University Medical Center accurately identified pediatric patients at high risk for blood clots in a clinical trial. However, there was no difference in outcomes ...
Oct 18, 2023
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A world-first clinical trial published in JAMA could provide an easy way to save tens of thousands of units of blood every year in Canada and much more worldwide.
Oct 12, 2023
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Researchers and physicians at the University of Pittsburgh and UPMC used machine learning to create and deploy an accurate and flexible model for predicting patients at high-risk for complications after surgery, according ...
Jul 10, 2023
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An early advocate for the electronic health records now found in hospitals across the country has developed a plan to improve their use, lighten clinicians' workload and enhance patient care.
May 25, 2023
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A team of medical researchers, engineers and computer scientists affiliated with multiple institutions across the U.S. has found that machine learning technology can help doctors predict which patients are at risk of developing ...
