Houston Methodist developed AI app to predict risk and prevent severe patient falls
This AI technology was developed by Houston Methodist and tested over an eight-month period to help address the growing concern of severe patient falls with seniors and the worry it causes their care-providers and care-givers.
How it works
The AI app predicts the risk of getting injured when a patient falls. Clinical parameters and patient demographics such as bone density, diagnosis, past procedures, etc. are used to populate the app so it then triggers tailored interventions to prevent these high-risk severe injury patients from falling—whether they are in the hospital setting or with home caregivers. This AI technology can be integrated into the patient's electronic medical record (EMR) and make things easier for clinicians since it will be part of the record and will automatically flag or alert the care-providers for high risk fall with harm patients when they enroll in the hospital. This will then trigger a prevention-focused intervention plan or clinical care path. The immediate benefit to the patient is to avoid falling and injuries or even death. The benefit for the hospital is that additional costs and/or lawsuits for these types of patients can be avoided.
In 2015, the estimated medical costs attributable to fatal and nonfatal falls was approximately $50.0 billion. For nonfatal falls, Medicare paid approximately $28.9 billion, Medicaid $8.7 billion, and private and other payers $12.0 billion. Overall medical spending for fatal falls was estimated to be $754 million.
New research & expert availability
The manuscript titled, "Preventing Inpatient Falls with Injuries using Integrative Machine Learning Prediction: A Cohort Study," will be available for full review soon, and I'll follow-up with a link. I just wanted to get this on your radar and see if you might be interested in learning more about this new AI app and research. I'd be happy to connect you to Dr. Stephen Wong, who spearheaded the study and can speak to the increased need for technology like this for clinicians and caregivers to help patients at risk for severe falls.
npj Digital Medicine, DOI: 10.1038/s41746-019-0200-3
Provided by Houston Methodist