AI for Pharma Real-World Evidence Generation |…

AI for Pharma Real-World Evidence Generation

Automate data curation, cohort building, and causal analysis on real-world data to generate robust evidence for regulators, payers, and clinicians.

RWE studies require high-quality, linked data and defensible methods. AI accelerates cleaning, deduplication, cohort selection, and causal inference, while maintaining transparency and regulatory readiness.

Outcome ascertainment (mortality, readmission, AEs).

Comparator selection and matching.

Propensity scores, weighting, and balance diagnostics.

live demo · related simulation● LIVE

Audit trails, protocol versions, statistical analysis plans.

ETL to OMOP/FHIR; normalization; vocabulary services.

PHI handling, tokenization, and access control.

Frequently asked questions

What are reproducible pipelines and notebooks with locked cohorts?

Reproducible pipelines; notebooks with locked cohorts.

What is meant by regulatory-ready exports and audit bundles?

Regulatory-ready exports and audit bundles.

How should I normalize data to OMOP/FHIR, including setting PHI controls and tokenization?

Normalize data to OMOP/FHIR; set PHI controls and tokenization.

What steps are involved in building NLP and entity resolution workflows, and how do I validate the cohorts and outcomes?

Build NLP and entity resolution; validate cohorts and outcomes.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation