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Andreas Vesalius

Use cases

What a question looks like

Four short scenarios, one per client type. All four are illustrative: they describe the kind of analysis the data support, not studies we have completed.

  1. 01 · Pharma Illustrative

    Adherence and persistence in a chronic therapy

    Among members who start a given chronic therapy, what share remain on treatment at twelve months, and what characterises those who stop?

    Data used
    drug_exposure and cost from the claims layer for persistence and spend; condition_occurrence and measurement from the clinical layer for severity; payer_plan_period for a stable denominator.
    What comes back
    Persistence curves by subgroup, discontinuation predictors, cost per persistent patient-year. Aggregate tables suitable for an RWE dossier.
    Access mode
    Clean Room

    Illustrative scenario. Figures and outcomes are not results from our data.

  2. 02 · CRO Illustrative

    Feasibility for a phase III protocol

    How many patients meet the inclusion and exclusion criteria of a planned trial, and how are they distributed by age, sex and recent utilisation?

    Data used
    condition_occurrence, procedure_occurrence and measurement for eligibility; visit_occurrence for recency of contact with the hospital.
    What comes back
    Rounded eligible counts under alternative criteria, distributions, and an estimate of patients seen at the hospital in the last twelve months.
    Access mode
    Feasibility API

    Illustrative scenario. Figures and outcomes are not results from our data.

  3. 03 · Employer Illustrative

    Disease burden in a covered workforce

    What are the leading drivers of utilisation and cost in an employer’s covered population, and how do they compare with a matched benchmark?

    Data used
    Financial and behavioural layers for the employer’s members; condition_occurrence for prevalence; a matched comparison drawn from the wider membership.
    What comes back
    Prevalence and cost by condition group, utilisation patterns, gaps in care. Aggregated at group level, never by employee.
    Access mode
    Clean Room

    Illustrative scenario. Figures and outcomes are not results from our data.

  4. 04 · Health system Illustrative

    Licensing the decision engine

    Can a payer or health system in another Latin American country apply the same cohort logic, risk stratification and cost models to its own data?

    Data used
    The OMOP-based models and cohort definitions developed on our data, deployed on the licensee’s infrastructure. The licensee’s data never leave its environment.
    What comes back
    A licensed engine with documented definitions, validation against the licensee’s data and ongoing updates.
    Access mode
    Licence

    Illustrative scenario. Figures and outcomes are not results from our data.

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