Studies show that patients who undergo an unplanned transfer to the ICU experience a greater chance of negative outcomes than patients admitted directly. These patients typically stay in the hospital 8 to 12 days longer and have significantly higher mortality rates – these patients account for only 5% of patients but represent one-fifth of all hospital deaths. The challenge is to find patients before they decline and need to be moved to the ICU, but these patients often don’t have symptoms that clinicians can recognize as leading to a serious change in condition.
AI models built within the Refactor Health platform can be used to find patients who are likely to crash. The machine learning models use patient medical records, laboratory results, and vital signs from patients to find early warning signals of deteriorating condition. These models can then be used with existing patients in real-time to determine their risk of a crash and as part of an early warning system for clinicians so they can intervene before the ICU transfer is needed.