El Camino Hospital, 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 that 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.
Like most other US hospitals, El Camino had invested time and money in fall-prevention efforts, such as the call lights, but the various methods had not been effective enough.
The parameters for at-risk patients are wide enough that many are tagged as likely to fall at some point. It is even harder if a hospital has a bigger share of high-risk patients, as El Camino does.
Effectively monitoring that many people can be particularly tough when nurses are already overworked.
Four years ago, El Camino turned to a health-care technology start-up called Qventus to help it prevent falls.
The hospital had worked with Qventus the year before to devise a better system of scheduling Caesarean sections.
Qventus CEO Mudit Garg and his co-founders, Brent Newhouse and Ian Christopher, quickly began developing a programme that predicts falls resulting from what’s known as alarm fatigue – when clinicians experience sensory overload from the many hospital sounds and alerts, sometimes leading them to miss critical alarms altogether.
“If I tell you everything is important, nothing is important,” says Mr Garg. “You are applying the same level of focus to everything.”
Qventus came up with a programme that extracts and analyses data from call lights, bed alarms and electronic medical records.
It also pulled in other information, such as a patient’s age, the medication they are on, 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 Ms Reinking.
At El Camino, where the software has been installed since 2014, head nurse Ms Reinking said staff had to be convinced to adapt the new procedures.
But overall, it wasn’t too hard a sell, given how persuasive the fall-prevention results have been. Since 2014, nurses have seen a 29pc drop in falls.
“Once we were able to demonstrate the value of the technology,” Ms Reinking added, “people kind of began to come around.”