AI designed for early intervention
Built by an expert team of PhD data scientists and clinicians, Sarvas Health’s pioneering AI model is designed to identify risk of chronic disease earlier, before symptoms escalate and outcomes worsen.
Our technology analyses medical data within electronic health records to detect patterns associated with early disease onset. The model is condition-agnostic and adaptable, meaning it can be trained to predict risk across different chronic conditions and deployed using a wide range of medical data systems.
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By enabling earlier identification of patients at risk, our products support timely clinical assessment, more accurate diagnosis, and earlier entry into appropriate treatment pathways, improving outcomes for patients and reducing avoidable pressure on healthcare systems.
HF2.0
Earlier identification of heart failure risk​.
Heart failure is a common, progressive and incurable condition affecting an estimated 26 million people worldwide. When diagnosed late, patients can deteriorate rapidly, leading to poor outcomes, reduced quality of life and increased healthcare utilisation.​
Despite the prevalence of heart failure, diagnosis is often delayed. Around 80% of cases are diagnosed late, and evidence shows that 40% of patients diagnosed in hospital had symptoms recorded in their GP care record that could have enabled earlier detection.
Women are disproportionately affected, being almost twice as likely to be misdiagnosed and waiting significantly longer for diagnosis following their initial GP visit.​ Late diagnosis doesn’t only carry a substantial human cost, it incurs significant and avoidable incremental costs of hospitalisation and ongoing care.
Sarvas Health’s HF2.0 AI model is designed to identify patients at risk of heart failure onset earlier by analysing patterns within coded primary care data. By surfacing risk sooner, the model supports earlier clinical assessment and access to cost-effective treatments, helping improve outcomes for patients while reducing the burden associated with late-stage diagnosis.
Coming Soon
Chronic Kidney Disease
(CKD)
Our AI model will be further trained to identify early indicators of chronic kidney disease, supporting earlier diagnosis and intervention in a condition where progression is often silent until advanced stages.
Transthyretin Amyloidosis (ATTR)
We are developing future capabilities to support earlier identification of ATTR, a rare but frequently misdiagnosed condition where delayed diagnosis significantly impacts outcomes.