Luminare

Luminare, developed by Luminare Inc., is a healthcare solution designed to improve sepsis outcomes in hospitals through faster detection and automated intervention. According to the vendor, Luminare aims to assist hospitals in identifying cases of early sepsis, leading to timely intervention and improved patient outcomes. The system also focuses on increasing sepsis screening compliance to ensure hospitals meet the necessary requirements.

Key Features

Improved Sepsis Outcomes: Luminare aims to enhance sepsis outcomes in hospitals by providing faster detection combined with automated intervention. According to the vendor, the system helps hospitals identify early sepsis cases, potentially leading to improved patient outcomes. Luminare also emphasizes the importance of increasing sepsis screening compliance.

AI-Powered Sepsis Detection: Luminare utilizes artificial intelligence and machine learning algorithms to enhance sepsis detection. The vendor claims that the AI-generated sepsis alarm is specifically developed for sepsis deterioration and is not a generic algorithm. The system is built outside of the hospital and has been co-authored by medical professionals and Luminare's CEO and Co-Founder, Sarma Velamuri MD.

Machine Learning Process Improvement: Luminare's machine learning capabilities are utilized for process improvement in sepsis management. The system leverages machine learning algorithms to analyze data and identify areas for improvement. According to the vendor, incorporating machine learning aims to enhance the effectiveness of sepsis management processes.

Workshops for Improved Outcomes: Luminare offers workshops that focus on improving sepsis outcomes quickly and within the budget of healthcare organizations. These workshops provide insights and strategies to optimize sepsis management practices. According to the vendor, healthcare professionals can benefit from Luminare's expertise and experience in sepsis management.

Data Abstraction for Cerebrovascular Events: Luminare's AI tool, Leonardo, uses Natural Language Processing to parse clinical information from unstructured data related to cerebrovascular events. The tool can extract relevant data from clinical notes, EMS reports, physician notes, and radiology reports. Leonardo aims to convert unstructured data into a reportable format, making data abstraction 10x faster for cerebrovascular event accreditation reporting.