What we do

We predict patient outcomes

Volv Global’s inTrigue machine learning methodology can uncover subtle signals and patterns in large-scale medical and biological data that indicate how a disease may progress, or how patients are likely to respond to specific therapies. 

This empowers clinicians and healthcare providers to make more informed, proactive decisions and minimise trial-and-error treatment approaches, helping to prevent disease complications, reduce healthcare costs, and improve overall patient well-being. 

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Why outcome prediction matters

The importance of predicting patient disease outcomes

Predicting patient outcomes is a critical step in optimising care and ensuring the best possible treatment for each individual. Forecasting disease trajectories allows the identification of optimal treatment pathways and the highlighting of potential risks earlier in the patient journey.

Find the right
treatments

Machine learning models can detect subtle patterns in patient data, indicating how individuals are likely to respond to specific therapies. This offers clear evidence for matching treatments to patients

Timely
interventions

Early forecasting of disease trajectories highlights risks and warning signs before they escalate. This enables preventative and proactive treatments (or lifestyle recommendations) for more cost-effective care.

Optimise
the patient journey

Predictive insights offer guidance on the most efficient and effective treatment pathway, streamlining care from diagnosis to recovery. This allows for better health outcomes and lower long-term costs.

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Industry-leading technology at work

How inTrigue predicts patient outcomes

inTrigue uses advanced AI and machine learning to forecast how patients may respond to treatments and how their conditions could progress. By analysing extensive clinical data, electronic health records, and real-world evidence, inTrigue empowers healthcare providers to make proactive decisions that optimise care and accelerate research. 

  • AI-Driven Forecasting: builds predictive models from historical and real-time data to anticipate disease progression and complications. 
  • Personalised Treatment Pathways: identifies the most effective therapies for each patient, enabling targeted interventions. 
  • Adaptive Learning: continuously refines predictions with new data, ensuring insights remain accurate and actionable.