Listening to your gut
Researchers are working to integrate nurses' intuition into early warning systems using artificial intelligence to identify at-risk patients. By incorporating data points that reflect nurse concern, such as extra vital signs and medication administration, AI algorithms can more accurately predict patient deterioration.
Hospital nurses often have trouble articulating their gut feelings about patients who seem off, but objective measures are normal. Kelly Gleason, an associate professor at Johns Hopkins School of Nursing, is working to augment AI in early warning systems to incorporate nurses' intuition. Early warning systems use machine learning to predict patient risk based on vital signs and electronic health records. Researchers are adding data points that reflect nurse concern, such as extra vital signs and medication administration, to the algorithms. This integration can help identify at-risk patients more accurately and timely. By leveraging AI, hospitals can provide more comprehensive monitoring than humans alone.
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