A sophisticated system that analyzes electronic data about hospital patients, identifies those at risk of deteriorating, and issues an alert to a centralized team of specially trained nurses resulted in a lower mortality rate, Kaiser Permanente researchers found.
The evaluation of the Advance Alert Monitor, or AAM, used in 21 Kaiser Permanente Northern California hospitals, was published in the New England Journal of Medicine.
The study describes the results of a staggered deployment to Kaiser Permanente hospitals in Northern California between August 2016 and February 2019. The authors compared the outcomes for 15,487 patients who reached the alert threshold and 28,462 comparison patients who would have triggered an alert if the system had been active. The analysis found a 16% lower mortality rate among patients in the intervention cohort.
“Along with saving lives, the Advance Alert Monitor has demonstrated that it is possible to integrate predictive models into day-to-day operations in our medical centers,” said lead author Gabriel Escobar, MD, a research scientist with the Kaiser Permanente Division of Research and regional director for Kaiser Permanente Northern California hospital operations research.
AAM predicts the probability that hospitalized patients are likely to decline, require transfer to the intensive care unit or emergency resuscitation, and benefit from interventions. Early warnings could be helpful for patients at risk of deterioration where early intervention may improve outcomes.
“Predictive analytics and machine learning are unlocking new frontiers in the use of complex patient data to improve our care in real time. They augment our clinicians’ practice by finding signals hidden within the electronic health record,” said coauthor Vincent Liu, MD, MS, a practicing intensivist and research scientist with the Division of Research, and regional director for Kaiser Permanente Northern California hospital advanced analytics.
The predictive model uses algorithms created from