NOMA AI provides a predictive patient monitoring platform that continuously aggregates clinical data from hospital IT and bedside devices, applying deep‑learning models to generate real‑time risk scores and alerts for complications such as maternal hemorrhage. The solution integrates directly into EHR workflows, offering customizable decision‑support panels that enable clinicians to intervene proactively, improving patient outcomes and reducing costly adverse events.
Funding
$100K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CSFounders
Product
Problem
Approximately one in seven patients experiences a preventable medical complication, leading to adverse health outcomes and an estimated $78 billion in annual U.S. healthcare costs. Current hospital practices rely on reactive monitoring of symptoms rather than anticipating complications, which limits timely intervention.
Solution
NOMA AI offers a predictive patient monitoring platform that continuously ingests multimodal clinical data from existing hospital IT and device systems. Using deep‑learning models, the platform generates real‑time risk scores and alerts for complications, enabling clinicians to intervene before adverse events occur. The solution integrates directly into electronic health record (EHR) workflows, allowing hospitals to customize alerts and decision‑support panels according to their protocols and safety guidelines. By shifting care from reactive to proactive, NOMA AI aims to reduce complication rates, improve patient outcomes, and lower associated treatment costs.
Target Audience
Primary customers are hospitals and health systems seeking to enhance patient safety, particularly obstetric departments and maternal‑care teams responsible for managing hemorrhage risk.
Features
- Real‑time data aggregation from bedside monitors, lab results, and EHRs into a unified analytics pipeline
- Deep‑learning risk models that predict complications such as maternal hemorrhage with higher sensitivity than existing early‑risk tools
- Seamless integration with hospital EHR and clinical workflow systems, delivering actionable alerts within clinicians’ native interfaces
- Customizable risk panels and protocol mapping to align AI recommendations with hospital‑specific guidelines
- Continuous model monitoring and updates to maintain accuracy across diverse patient populations