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Cutting Fall Rates With Predictive Analysis

April 18, 2017

According to a recent article, a hospital has cut its rate of patient falls by 39% using advanced predictive analysis—a strategy that other facilities with high fall rates could theoretically utilize as well (Healthcare IT News, April 12, 2017).

The 420-bed hospital located in California, El Camino Hospital, contacted the predictive analysis company, Qventus, when they decided they needed a serious change when it came to the hospital’s rates for patient falls.

“We were having a lot of difficulty being able to get our falls under control and at the level we wanted them, to be in the top quartile in the nation. It seemed like a lot of the efforts we tried were not getting us to where we wanted to be,” said Chief Nursing Officer Cheryl Reinking, RN, MS, in the article.
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The company first started this project by sitting down with the El Camino staff to discuss the details of the problem. Ms Reinking said, “They were able to help us come up with a methodology where we could in real-time know and understand which patients are at higher risk for falls so we could focus our efforts clinically in real-time on those patients.”

Qventus started using data collected by the El Camino electronic health records to determine which patients are at high risk for falls; they then analyzed nurse call light and bed alarm data. After taking these factors into account, Qventus is able to see who uses alarms the most and who has the most risk factors, then they are able to alert nurses to the high fall-risk patients.

These efforts allowed the hospital to reduce falls by 39% within 6 months, but Ms Reinking made sure to note that other factors contributed to this reduction as well: “We also were working on initiatives like making sure the risk assessments were really appropriate for our patients when they first came in. We also did a lot of training on the bed alarms. We got more chair alarms in place. We also did a lot of training on bathroom falls.”—Amanda Del Signore

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