AI hospital software knows who’s going to fall

June 25, 2018

El Camino Hospital, located in the heart of Silicon Valley, has a problem. Its nurses, tending to patients amid a chorus of machines, monitors, and devices, are only human. One missed signal from, say, a call light—the bedside button patients press when they need help—could set in motion a chain of actions that end in a fall. “As fast as we all run to these bed alarms, sometimes we can’t get there in time,” says Cheryl Reinking, chief nursing officer at El Camino.

Falls are dangerous and costly. According to the Department of Health and Human Services’ Agency for Healthcare Research and Quality, 700,000 to 1 million hospitalized patients fall each year. More than one-third of those falls result in injuries, including fractures and head trauma. The average cost per patient for an injury caused by a single fall is more than $30,000, according to the Centers for Disease Control and Prevention. In 2015, medical costs for falls in the U.S. totaled more than $50 billion.

Like most other U.S. hospitals, El Camino had invested time and money in fall prevention efforts, such as the call lights, but the various methods hadn’t been effective enough. The parameters for at-risk patients are wide enough that many are tagged as likely to fall at some point. It’s even harder if a hospital has a bigger share of high-risk patients as El Camino does—about 50% of its patients are at risk for falls. Effectively monitoring that many people can be tough when nurses are already overworked.

Four years ago, El Camino turned to a health-care technology startup called Qventus Inc., based a few miles away in Mountain View, CA, to help it prevent falls. The hospital had worked with Qventus the year before to devise a better system of scheduling Cesarean sections. The company created software that would predict the number of women coming in for the surgery to ensure there were enough rooms.

Qventus Chief Executive Officer Mudit Garg and his co-founders, Brent Newhouse and Ian Christopher, quickly began developing a program that predicts falls resulting from what’s known as alarm fatigue—when clinicians experience sensory overload from the many hospital sounds and alerts, leading them to sometimes miss critical alarms altogether. “If I tell you everything is important, nothing is important,” says Garg. “You’re applying the same level of focus to everything.”

Qventus came up with a program that extracts and analyzes data from call lights, bed alarms, and electronic medical records. It also pulled in other information such as a patient’s age, the medication he’s on and when it was last administered, and the vitals last recorded by a nurse. Analysis of the data exposed patterns, such as the time of day when most falls occur or the sequence of events that typically lead to falls. For example, patients who have changed rooms are especially vulnerable.

From the data, Qventus identified several fall indicators used to predict which patients need more monitoring. If a patient meets all the indicators, an alert is sent to a special badge worn by nurses—a “nudge,” as Qventus calls it, reminding them to check on the patient within the next 12 hours. “In the long run, it should cut down on those bed alarms, because they’re intervening earlier,” says Reinking.

Bloomberg has the full article

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