Permanente physicians quoted in HealthITAnalytics on patient monitoring study

A real-time clinical decision support system that alerts specially trained nurses when patients are at risk of deteriorating resulted in significantly shorter hospital stays, improved outcomes, and fewer deaths, according to Kaiser Permanente research highlighted in HealthITAnalytics.

Developed by a research team at the Kaiser Permanente Division of Research in Northern California, the Advance Alert Monitor is an algorithm-based system embedded in the electronic health record, which predicts when a hospitalized patient is at risk of being transferred to the intensive care unit or needing emergency resuscitation.

The HealthITAnalytics story quoted several physicians and researchers from The Permanente Medical Group (TPMG) on the study results and Advance Alert Monitor program. The alert, which has been implemented in all 21 Kaiser Permanente hospitals in Northern California, allows care teams to begin providing interventions that help prevent further deterioration in patients.

“The Advance Alert Monitor program is a wonderful example of how we combine high-tech and high-touch in caring for hospitalized patients,” Stephen Parodi, MD, associate executive director of TPMG and executive vice president of The Permanente Federation, told the publication.

The article referenced a study published in the New England Journal of Medicine which found that patients monitored with the system had lower ICU admission rates than patients without it, as well as shorter hospital stays and lower mortality within 30 days of an alert.

“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 Gabriel Escobar, MD, the study’s lead author and research scientist with the Division of Research.

Co-author Vincent Liu, MD, intensivist with TPMG and research scientist with the Division of Research, told HealthITAnalytics: “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.”

Note: Read the HealthITAnalytics article here.