Illustrative
Where is the unmet need, and how big is it?
The question. A medical affairs team needs to size the population still cycling through therapy lines without reaching a durable response, before committing to an evidence programme.
The shape of the work. National claims plus dispensing, in a market with near-universal coverage. Define lines of therapy from dispensing sequences, identify switching and discontinuation, and quantify the group that exhausts available options.
Where it stops. Claims record what was funded, not why it was stopped. Discontinuation is inferred, not observed, and the analysis must say so.
Illustrative
What does standard of care actually look like here?
The question. A clinical team designing a regional trial needs the real comparator arm, not the guideline one.
The shape of the work. Hospital and EMR data in two markets, harmonised by one written specification and executed locally in each. Extract observed first-line regimens, sequencing and time-to-next-treatment.
Where it stops. The two markets cannot be joined at the record level. Results are compared as summaries, with an explicit statement of where the comparison stops being defensible.
Illustrative
Will the payer accept this as evidence of value?
The question. A market access team faces a submission deadline and needs to know whether local data can produce the burden-of-illness evidence the HTA body expects.
The shape of the work. A feasibility read first: is the cohort identifiable, is the cost captured, is the follow-up long enough. Then a costed utilisation analysis against the HTA body's own stated evidence requirements.
Where it stops. If the follow-up window is too short for the endpoint the payer wants, no analysis fixes it. That is a finding, and it is cheaper delivered in week three than month six.
Illustrative
Where is the volume, and who is treating it?
The question. A commercial team needs to know which hospitals actually deliver a procedure, at what share, and how that has moved, to target field effort properly.
The shape of the work. Published hospital activity data, segmented by procedure, ranked by hospital, with share of national and share of state. In Australia this is a Health Grid Platform view rather than a project.
Where it stops. Activity data shows what was done, not what it cost the hospital or how it was decided.
Illustrative
Is this rare disease population even findable?
The question. A rare-disease team needs to know whether a defensible incidence figure exists in a region where the condition has no dedicated registry.
The shape of the work. A landscape assessment across three markets: which coding systems capture the condition at all, whether a claims-based case definition can be validated, and whether an existing epidemiological cohort is faster than building one.
Where it stops. Sometimes the answer is that the population cannot be identified reliably from routine data, and primary research is the only honest route. We say so.
Illustrative
Does the biomarker-defined cohort exist in this market?
The question. A precision-medicine programme needs to know whether testing rates are high enough for a biomarker-defined cohort to be assembled locally.
The shape of the work. Laboratory results linked to clinical records in a market where genomic and clinical data sit close together. Establish testing rate, positivity, and whether the tested population is representative.
Where it stops. If only a selected subgroup is tested, the cohort is biased by construction, and the size of that bias has to be stated, not buried.
