After incorporating machine learning technology into its fall prevention program, El Camino Hospital significantly improved fall rates.
Identifying patients who are risk for falling is a key component in traditional fall prevention programs. However, successfully reducing falls requires interventions that go beyond simply acknowledging risk factors.
El Camino Hospital, a 446-bed, two-hospital health system located in Silicon Valley, California, has added a high-tech twist to its fall prevention program and significantly improved fall rates.
"We had fall rates that were beyond the benchmark," says the organization's Chief Nursing Officer Cheryl Reinking, RN, MS, NEA-BC. "We're a Magnet hospital, so we like to stay in the top quartile or the top decile. It's important to us not only for that reason, but also for patient safety."
The organization leveraged its Silicon Valley location and worked with Qventus (formerly analyticsMD), a neighboring technology firm, to help it incorporate machine learning and prescriptive analytics into its fall prevention program.
"We explained our problem to them: There's all this data, we screen our patients, we do all these things, and we're still having falls—more than we want to have, and more than the benchmark," Reinking says. "They said to us, 'We think we can help you.'"
And Qventus has. Six-months after the hospital began incorporating the prescriptive analytics technology, fall rates dropped by 39%.
The Data Goldmine
Medicare patients make up about half of El Camino's patient population and often have multiple comorbidities which, in addition to age, put them at risk for multiple adverse events, not just falls.
Keeping track of which issue needs immediate attention can be a challenge.
"Nurses have lots of alarms, lots of data coming at us from all different directions. We do lots of mini risk assessments on our patients. We screen them for their risk of DVT, their risk for falls, their risk for infections, and on and on," Reinking says.
"When our patients are at high risk for so many things, it's hard to pull out those threads that are most important at the time."
This is where prescriptive analytics has helped. The technology takes data from multiple sources, including the EMR, call lights, and bed alarms, and can identify if a patient's risk for a fall is escalating.
"It can tell you a lot of things about your patients' behavior," She says. "It shows us how many times the patient has set off the bed alarm, how many times the patient has pulled the alarm in the bathroom, how many times the patient has put on the call light."
Reinking explains that the technology takes data from these multiple sources and analyzes it to predict which patient is a fall risk in that moment. When a patient gets to a certain threshold, the system sends an alert to the nurse via the Vocera badge system.
"Then the nurse knows there is something going on with this patient right now, and he or she needs to get in there, or send the CNA in there to look at the patient," she says.
The nurse can then determine if the patient needs to be moved to someplace in clearer view of the staff, if camera monitoring is necessary, or if a family member needs to be called.
"The nurses have been able to do further assessments and try to prevent a fall before it even happens, or even gets close to happening," Reinking says.
The technology has been successful in helping reduce falls at El Camino, and, although it's not the only tool used to keep patients safe, Reinking says it is something she encourages other nurse leaders to consider.
"A lot of our systems hold data elements that you may never have thought could help achieve improved outcomes. When you have someone who can really help you, from a technology standpoint, understand what [data is] there and how it could help you—it's eye opening," she says.
"We had this valuable data there that we never dreamed could help us in this way. You have to think outside the box sometimes."
Jennifer Thew, RN, is the senior nursing editor at HealthLeaders.