In February 2021, the U.S. Food and Drug Administration (FDA) approved “CLEWICU,” an AI-based and first-of-its-kind solution for ICUs. Developed by Tel Aviv-based CLEW Medical, the solution helps predict hemodynamic instability, a common COVID-19 complication, in adult patients. The hemodynamic instability space is getting quite crowded, with venture capital-backed startup Fifth Eye creating a noninvasive clinical tool called Analytic for Hemodynamic Instability (AHI), which is, according to the company, the first solution to establish real-time streaming data for early onset hemodynamic instability. A research study about it in the International Journal of Medical Health Sciences was awarded “Best Paper” at the 2020 International Conference on Health Analytics. The company is now awaiting approval from the U.S. Food & Drug Administration before marketing it to other health systems. 

Hemodynamic instability can be defined as one or more out-of-range vital sign measurements, such as low blood pressure, abnormal heart rate (arrhythmias), and it can result in chest pain, cold extremities, loss of consciousness, shortness of breath, and even death. It is one of the most common causes of death in critically injured patients. Clinicians favor using the most common vital signs of heart rate and blood pressure to diagnosis their patients. However, they are often busy doing multiple patients at a time and can miss the disease, often leading to death. Fifth Eye’s AHI system uses a single lead of ECG to recognize hemodynamic deterioration well before it is outwardly evident, improving diagnosis considerably.

According to the CLEW Medical, its system provides notification of clinical deterioration up to eight hours in advance, enabling prompt, proactive patient care. CLEWICU constantly monitors and categorizes a patient’s risk level, providing doctors with physiological insight into a patient’s likelihood of future hemodynamic instability. These two AI-based solutions provide preemptive and potentially life-saving information that reduces alarm fatigue and improves clinical outcomes. Patient data is input from various sources and run through an AI-based algorithm. The machine-learning models are trained to identify the likelihood of occurrence of significant clinical events for ICU patients. The data is analyzed in near real-time to present calculated insights and notifications for dedicated AI models which provide a picture of a unit’s overall status. Low-risk patients unlikely to deteriorate are also identified, enabling more optimized ICU resource management.