Healcisio, a leader in AI and digital healthcare, announces it has received a $1 Million STTR Phase II award from the NIAID to advance the development of its critical care decision support platform and a new AI-powered software suite for abstracting and reporting quality measures.
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In what is still one of the country’s only peer reviewed, prospective implementation studies for sepsis AI, Healcisio previously demonstrated a remarkable reduction in sepsis mortality by 17% and an improvement in SEP-1 bundle compliance by nearly 10%. In the coming year Healcisio will begin expanding this work to two additional health systems and deploying new generative AI tools related to quality assessment for the in-hospital care of sepsis patients.
“Predictive analytics is an increasingly important part of the healthcare delivery process; however, it’s only one factor in a highly complex system. Maturing how we measure quality and making it efficient is long overdue and can profoundly benefit patients and providers. We think Healcisio’s holistic approach to this problem will drive meaningful change for the treatment of sepsis and many other complex medical conditions,” said Aaron Boussina, Healcisio CEO.
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Currently all hospitals that receive CMS funding have a requirement to self-report a host of quality measures related to the care they deliver. Within US physician practices, the annual cost of quality reporting has been reported as 785 hours per physician and over $15 b******. One of the most debated and time consuming measures is related to sepsis and is called SEP-1. The SEP-1 measure requires significant manual abstraction and time to complete. Using the latest advances in Large Language Models (LLMs), Healcisio has developed a number of quality measure automation tools, including an LLM-based system that ingests patient charts, via Fast Healthcare Interoperability Resources (FHIR), and outputs a completed Severe Sepsis and Septic Shock (SEP-1) abstraction.
As part of the multi-site deployment of its sepsis AI model, Healcisio will be partnering with quality improvement stakeholders with the hope of alleviating the clinical workload associated with manual chart reviews, thereby reallocating precious clinician time to enhance care quality initiatives at the patients’ bedside.
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