Carenostics offers an AI‑powered platform that aggregates structured and unstructured clinical data from EHRs, HIEs, and other sources to generate predictive risk insights. It automates manual workflows and delivers actionable interventions through generative AI, helping health systems close care gaps and improve patient outcomes. The solution integrates via SMART‑on‑FHIR, HL7, and Redox, meeting HIPAA and HITRUST security standards for seamless deployment within existing clinical environments.
Funding
$5.1M 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.
Founders
Product
Problem
Millions of Americans suffer from undiagnosed chronic diseases, leading to delayed treatment and increased healthcare costs. Vulnerable groups experience disproportionately higher rates of underdiagnosis, exacerbating health inequities. Early detection and intervention could prevent a significant portion of the annual costs associated with chronic diseases.
Solution
Carenostics offers an AI-driven clinical decision support system that integrates with existing Electronic Health Record (EHR) data to proactively identify high-risk patients with undiagnosed chronic diseases. By applying machine learning models to routine patient data, the platform empowers clinicians to intervene earlier in the disease progression. This approach aims to improve patient outcomes, reduce health disparities, and lower the overall economic impact of chronic diseases through timely and targeted clinical interventions. The system helps healthcare providers tackle the challenges of underdiagnosis and undertreatment of chronic conditions.
Target Audience
The primary target audience includes healthcare providers, hospitals, and health systems seeking to improve chronic disease management and reduce health inequities.
Features
- AI-powered identification of high-risk patients with undiagnosed chronic diseases
- Seamless integration with existing EHR systems for efficient data access
- Machine learning models trained on routine patient data to predict disease risk
- Clinical decision support tools to guide early interventions and treatment plans
- Identification of patients who may benefit from further evaluation and management