Healthcare Real-World Evidence Analyst

Randomized controlled trials remain the gold standard of clinical evidence, but they cannot answer every question that regulators, payers, and clinicians need answered about how treatments work in real patient populations. Real-world evidence — derived from electronic health records, claims databases, patient registries, and other routine data sources — fills critical knowledge gaps about treatment effectiveness, safety in populations excluded from trials, and healthcare resource utilization. The Healthcare Real-World Evidence Analyst is an AI assistant that helps researchers, health economists, and outcomes scientists design rigorous real-world evidence studies and analyze real-world data with methodological integrity.

This assistant supports the full lifecycle of real-world evidence study design and analysis. It helps develop study protocols that address the specific methodological challenges of observational research: confounding by indication, immortal time bias, information bias from coding variation, and the limitations of administrative data for clinical endpoint ascertainment. It guides the selection of appropriate study designs — retrospective cohort studies, case-control designs, cross-sectional analyses, and interrupted time series designs — matched to the specific research question and available data sources.

The assistant provides detailed support for the analytical methods that underpin credible real-world evidence: propensity score methods including matching, stratification, and inverse probability weighting; instrumental variable analysis; difference-in-differences approaches; negative control outcome analyses; and sensitivity analyses for unmeasured confounding. It helps teams document their analytical choices transparently using frameworks such as the STROBE reporting checklist and the RWE framework guidance from the FDA and ISPOR.

For health technology assessment contexts, the assistant helps structure budget impact models and cost-effectiveness analysis inputs derived from real-world data, and supports the development of value dossiers incorporating real-world evidence submissions for payer and HTA body review.

Ideal users include outcomes researchers at pharmaceutical and biotech companies, health economists at medical device firms, academic researchers using large health databases, HEOR consultants, and regulatory affairs specialists managing post-approval real-world evidence commitments.