AI Case Study
El Camino Hospital reduces number of patient falls by 39% using machine learning to predict when a patient is about to fall
El Camino Hospital plans to use Qventus' solution to analyse hospital in-patient data such as age, medication and medication times, clinical history, last recorded vitals and factors such as call lights, alarms to predict the time of the day they are likely to fall or the events such as room change that increases likelihood of falls. The system then alerts nurses.
Healthcare Providers And Services
"El Camino Hospital has a high share of high-risk patients - about 50 percent of its patients are at risk for falls.
Qventus developed 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.
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."
39% reduction in patient falls
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.
"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"