What we do

We detect diseases at an earlier stage

Volv Global’s inTrigue machine learning technology detects incidence of disease earlier than currently possible, by analysing vast amounts of medical data to detecting subtle patterns among biomarkers.

This ensures that people on the path to developing diseases can have start their healthcare journey early by receiving early interventions. This improves patient outcomes, reduces healthcare burdens and improves quality of life. 

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Why early detection matters

The importance of early diagnosis

Early detection of diseases can significantly alter the course of treatment and improve prognosis. Patients diagnosed early often have better health outcomes, fewer complications, and a higher quality of life. By uncovering patients at the earliest disease stages, we ensure timely medical interventions.

Improved
patient outcomes​

Early treatment, before serious onset of disease, can hinder disease progression and reduce long-term complications for patients.

Enhanced
clinical trials

Clinical trials benefit from a larger patient cohort. Identifying suitable candidates earlier, before conventional treatment, bolsters clinical trial recruitment and leads to more effective trials.

Personalise
the patient journey

Early diagnosis ensures an optimal patient journey, in which patients receive the right treatments saving years of patient suffering and reducing long-term healthcare costs by prioritising medical resources by need.

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Industry-leading technology at work

How inTrigue enables earlier detection

inTrigue employs sophisticated AI and machine learning algorithms to sift through vast amounts of medical data, identifying subtle patterns that indicate the early presence of diseases. Our technology surpasses traditional methods, ensuring that no potential patient is overlooked. 

  • AI-Driven Insights: detects early signs of disease by analysis of electronic health records, claims data, and other medical records. 
  • Pattern Recognition: identifies subtle indicators and correlations that might be missed by conventional diagnostics. 
  • Comprehensive Data Analysis: combines structured and unstructured data for a holistic view of patient health.