Studio RWE is built on a transparent, indication-aware decision engine. This page walks through how your inputs are translated into a structured view of feasibility, effort & investment, and regional realism – step by step.
Every assessment starts with clinically meaningful, operationally relevant inputs. These anchor the engine in your real-world context instead of abstract templates.
Oncology, Cardiology, Metabolic, Neurology, Autoimmune, Rare, and more – each with its own complexity profile.
NSCLC, HF, Stroke, RA, CKD, Breast cancer, etc. Indication drives endpoint libraries and feasibility expectations.
Treatment patterns, effectiveness, safety, PRO, economic or hybrid objectives refine what “feasible” should look like.
India, APAC, EU5, US or multi-region. Each region changes data reality, governance, and timelines.
Single / multi-site EMR, registries, claims, hybrid or app-based flows – mapped to what endpoints are realistic.
Clinical, lab, imaging, PRO, resource use – the mix influences abstraction burden, bias and design choice.
Studio RWE uses a structured, pillar-based scoring system. Instead of a single opaque score, each decision is anchored in six dimensions.
Are the questions, endpoints and design clinically meaningful and aligned to current practice?
Does the proposed data source realistically capture what you need – with acceptable quality?
Can sites, teams and workflows support the abstraction, coordination and timelines you expect?
How aligned is the idea with regional ethics, privacy, and regulatory expectations?
Is the study positioned to deliver insights that matter for decisions beyond a single analysis?
What could break feasibility – and can those risks be mitigated through design tweaks?
Not every RWE question requires the same operational footprint. The engine classifies studies into Low, Medium and High Effort & Investment tiers – without exposing cost numbers.
Simple endpoints, limited sites, minimal abstraction, no imaging / pathology, high EMR completeness.
Moderate abstraction, 2–4 sites, some lab / PRO requirements, focused governance and validation.
Deep multi-site abstraction, lab + imaging + pathology, complex endpoints, multi-layer governance.
E&I tier is assigned only after all inputs are evaluated – including TA, endpoints, data source, region and rough scale of the program.
The same study looks very different in India, APAC, EU5 or the US. Studio RWE adjusts feasibility expectations using region-aware heuristics.
Fragmented EMRs, variable coding, strong clinician engagement, mixed lab integration and EC timelines.
High-quality data pockets (e.g., Taiwan, Korea) balanced with emerging, variable-quality ecosystems.
Standardised coding and robust linkages, paired with strict privacy and governance frameworks.
Rich, linked datasets and strong infrastructure, with high costs and complex contracting structures.
Finally, the engine assembles a narrative summary – not just a score – highlighting feasibility, E&I tier, and mitigation options.
GO, Conditional GO, or No-Go – explained through the pillars rather than a single opaque number.
A structured breakdown of what is strong, what is fragile, and where design changes help most.
Low / Medium / High E&I with a short rationale – to align expectations and resourcing discussions.
How the same idea might need to adapt across India, APAC, EU5, or US settings.