Science daily health12/11/2023 Development of the M-CURES model, she says, began in early 2020, at the onset of the COVID-19 pandemic. Wiens explains that the ability to develop effective predictive models quickly can be crucial in a situation like a pandemic, where fast action is essential and the threat is poorly understood. “We were able to develop the M-CURES model in a fraction of the time it took to build past models through close collaboration between clinicians and data scientists and by enabling other health systems to validate the model without sharing any of their patient data,” said Jenna Wiens, an associate professor of electrical engineering and computer science at U-M and a lead author of the paper. They can also help providers identify patients at the lowest risk for serious complications-patients who may benefit from earlier hospital discharge or transfer to a lower-intensity care setting. Patient deterioration models help doctors and nurses make better decisions about care, for example, by proactively transferring the highest risk patients to the ICU before they deteriorate. In addition to the model’s effectiveness, the way it was made points the way to the dramatically faster development of future models, working around the challenges of sharing sensitive patient data. Now, a study published in the British Medical Journal demonstrates that it is effective at 12 other hospital centers around the United States, outperforming the accuracy of the widely used EPIC Deterioration Index by more than 21%. Tested without needing hospitals to share data, the method for developing the model could speed further improvements in medical prediction toolsĪ newly developed open-source patient deterioration model is improving care at the University of Michigan’s health system.
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