A concise summary detailing what this solution is, why it matters, and who it is designed to help.
RWE Strategy & Post-Market Evaluation
Design and deliver real-world evidence (RWE) plans that validate performance, support adoption, and monitor impact after launch.
Overview
We help organizations generate credible evidence from real-world settings—before and after launch. We design RWE strategies that align with the intended use case, stakeholders, and decision needs, using data sources such as EHR, claims, registries, digital tools, and pragmatic program data. Our approach emphasizes transparent methods, governance-ready documentation, and outputs that support adoption, continuous improvement, and responsible communication.
Deliverables
A list of tangible outputs and concrete products clients receive upon completion of the engagement.
- RWE Strategy & Study Blueprint — We define the decision question, target population, comparator, time horizon, and outcomes (fit-for-purpose).
- Data Source & Feasibility Assessment — We assess available real-world data sources, data quality, linkage feasibility, and governance requirements.
- Protocol & Analysis Plan (RWE) — We develop an RWE protocol and pre-specified analysis plan, including confounding strategy and sensitivity checks.
- Post-Market Monitoring Framework — We define KPIs, safety/tolerability signals (where relevant), equity monitoring, and refresh cadence.
- Reporting & Stakeholder Package — We deliver decision-ready reporting (tables/figures, technical notes, executive summaries) to support adoption and iteration.
Methods
The specific scientific approaches, analytical techniques, and standards used to execute the work.
- Observational & Pragmatic Designs — We use cohort designs, matched comparisons, interrupted time series, and pragmatic evaluations as appropriate.
- Causal Inference Approaches — We apply propensity methods, DiD/ITS, negative controls (when relevant), and sensitivity analyses to address bias and confounding.
- Endpoint & Measurement Strategy — We map outcomes to measurable endpoints across real-world data (clinical, utilization, PROs, adherence).
- Data Quality & Governance Alignment — We define QA/QC checks, privacy-by-design practices, and audit-ready documentation.
- Equity & Subgroup Assessment — We evaluate heterogeneity of effects across key groups where appropriate.
- Iterative Learning — We design monitoring so evidence improves over time and informs product/program refinement.
Metrics we track
The key performance indicators and measurable outcomes used to evaluate success and demonstrate impact.
- Evidence Readiness — We track whether the RWE plan supports clear stakeholder questions and defensible endpoints.
- Data Feasibility & Quality — We monitor completeness, consistency, linkage success (when applicable), and QA/QC flags.
- Bias & Robustness — We track diagnostics and sensitivity results to assess stability of conclusions.
- Outcome Performance — We track clinical, utilization, and experience outcomes aligned with the RWE plan (where relevant).
- Equity Signals — We track subgroup performance and access indicators when relevant.
- Decision Impact — We assess whether outputs support adoption, optimization decisions, or stakeholder approvals.
Related Focus Areas
Key domains, settings, and populations where this solution is most frequently applied and drives significant impact.
Related Solutions
Additional methods and capabilities that often complement this solution for comprehensive project execution.
