AI-Based Predictive Analytics Platform Receives FDA Emergency Use Authorization in Support of COVID-19 Patients
By HospiMedica International staff writers Posted on 24 Jun 2020 |
Image: Predictive analytics platform CLEW (Photo courtesy of CLEW)
CLEW-ICU, a new ICU solution, has become the only device to receive FDA Emergency Use Authorization (EUA) for providing early identification of patients who are likely to experience respiratory failure or hemodynamic instability, both potentially common but significant complications associated with COVID-19.
Developed by CLEW (Netanya, Israel), CLEW-ICU allows healthcare providers to use predictive screening information to help identify patients with an increased likelihood of being diagnosed with respiratory failure or hemodynamic instability. The AI-based algorithms are machine-learning models trained to identify respiratory failure and or hemodynamic instability hours in advance. This allows for additional evaluation and potentially early intervention, planning, resource management.
CLEW’s AI models have been trained on nearly 100,000 patients in the ICUs, and scales to cope with patient volume surges while reducing a caregiver’s exposure risk to infected patients. CLEW’s models were developed and intended for both local ICUs and TeleICUs and integrates best practice modules. The streamlined at-a-glance web application is designed for near real-time access to patient data and provides tools for both worklist, unit and multiunit views, featuring unit occupancy and patient risk level. CLEW-ICU integrates caregiving of local and remote teams enabling workflow and resource decision making.
“Healthcare providers need more than simple analytics. Systems need to integrate into the provider’s workflow, offering ease of use and actionable data. The CLEW-ICU platform is designed to enable healthcare providers to monitor patient predicted risk levels across all units in real-time allowing for smart decision making about clinical resource allocation, ensuring prompt, proactive and efficient patient care” said Gal Salomon, CLEW CEO.
Related Links:
CLEW
Developed by CLEW (Netanya, Israel), CLEW-ICU allows healthcare providers to use predictive screening information to help identify patients with an increased likelihood of being diagnosed with respiratory failure or hemodynamic instability. The AI-based algorithms are machine-learning models trained to identify respiratory failure and or hemodynamic instability hours in advance. This allows for additional evaluation and potentially early intervention, planning, resource management.
CLEW’s AI models have been trained on nearly 100,000 patients in the ICUs, and scales to cope with patient volume surges while reducing a caregiver’s exposure risk to infected patients. CLEW’s models were developed and intended for both local ICUs and TeleICUs and integrates best practice modules. The streamlined at-a-glance web application is designed for near real-time access to patient data and provides tools for both worklist, unit and multiunit views, featuring unit occupancy and patient risk level. CLEW-ICU integrates caregiving of local and remote teams enabling workflow and resource decision making.
“Healthcare providers need more than simple analytics. Systems need to integrate into the provider’s workflow, offering ease of use and actionable data. The CLEW-ICU platform is designed to enable healthcare providers to monitor patient predicted risk levels across all units in real-time allowing for smart decision making about clinical resource allocation, ensuring prompt, proactive and efficient patient care” said Gal Salomon, CLEW CEO.
Related Links:
CLEW
Latest COVID-19 News
- Low-Cost System Detects SARS-CoV-2 Virus in Hospital Air Using High-Tech Bubbles
- World's First Inhalable COVID-19 Vaccine Approved in China
- COVID-19 Vaccine Patch Fights SARS-CoV-2 Variants Better than Needles
- Blood Viscosity Testing Can Predict Risk of Death in Hospitalized COVID-19 Patients
- ‘Covid Computer’ Uses AI to Detect COVID-19 from Chest CT Scans
- MRI Lung-Imaging Technique Shows Cause of Long-COVID Symptoms
- Chest CT Scans of COVID-19 Patients Could Help Distinguish Between SARS-CoV-2 Variants
- Specialized MRI Detects Lung Abnormalities in Non-Hospitalized Long COVID Patients
- AI Algorithm Identifies Hospitalized Patients at Highest Risk of Dying From COVID-19
- Sweat Sensor Detects Key Biomarkers That Provide Early Warning of COVID-19 and Flu
- Study Assesses Impact of COVID-19 on Ventilation/Perfusion Scintigraphy
- CT Imaging Study Finds Vaccination Reduces Risk of COVID-19 Associated Pulmonary Embolism
- Third Day in Hospital a ‘Tipping Point’ in Severity of COVID-19 Pneumonia
- Longer Interval Between COVID-19 Vaccines Generates Up to Nine Times as Many Antibodies
- AI Model for Monitoring COVID-19 Predicts Mortality Within First 30 Days of Admission
- AI Predicts COVID Prognosis at Near-Expert Level Based Off CT Scans