AI hospital software knows who’s going to fall
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 percent 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, Calif., 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.
Read more: Bloomberg