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Meaningful real-world data for Value and Access, HEOR

Volv Global provides data-driven understanding of disease endpoints and outcomes to support pricing and reimbursement calculations, regulatory and HTA submissions, payer evidence packages and comparative economics studies with real-world evidence. 

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Better understanding
of disease

Analyse real-world treatment pathways and their effectiveness to define more precise endpoints for better analysis of disease progression and outcomes.

Generate evidence
from credible RWD

Compare treatment effectiveness via analysis of real-world outcomes of different intervention.

Refine
value proposition

Identify cohorts more precisely and at an early stage for better longitudinal assessment of patient health outcomes.

Quantify
disease burden

Understand the real-world impact on quality of life and healthcare utilisation for payer evidence packages and comparative economic studies

Value, access and HEOR in the face of complexity

The challenge of understanding disease phenomenology

Learn about our methodology

Quantifying Disease Burden

Integrating real-world evidence (RWE) to understand the real-world clinical, economic, and social impact of disease on quality of life and healthcare utilisation.

Variability in Outcomes

Disparate healthcare systems and patient populations create inconsistent datasets lacking standardised outcome measures.

Economic Attribution

Linking medical records to cost-related data is often difficult.

Population Bias

Differences in healthcare access across regions affect generalisability of findings.

Supporting Value and Access, HEOR

Actionable, state-of-the-art solutions for drug development

inTrigue
finds
patients

We find undiagnosed and misdiagnosed patients, expanding the pool of patients for clinical trial recruitment.

inAdvance
detects
disease earlier

We find patients before the development of traditional symptoms, before they are given conventional therapies.

inFlow
predicts what
happens next

We enable triage of patients according to prognosis of outcomes, e.g., fast progressors, or progressors to more acute symptoms.

inCare
finds eligible
patients

We differentiate and cluster heterogeneous patient cohorts for more accurate definition of trial inclusion and exclusion criteria.

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Leading AI/ML methodology to support Value and Access, HEOR

Volv Global’s proprietary machine learning technology

Through comprehensive data integration from clinical and claims sources, our proprietary machine learning methodology equips Value & Access and HEOR teams to quantify the real-world performance of therapies.

By merging clinical, claims, and other healthcare data, our methodology provides a holistic view of disease burden, allowing for robust cost-effectiveness and outcomes analyses.

This data-backed insight aids stakeholders in demonstrating value to payers, supporting earlier detection strategies, and ultimately enhancing patient access through clearer evidence of a therapy’s impact. 

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Shaping the future today

Our case studies & challenges in Value and Access, and HEOR​

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Proprietary Machine Learning Methodology