Portfolio Accelerator · Life Sciences · Snowflake
Real-World Evidence AI on Snowflake: An Implementation Blueprint, Not a Slide Deck
A documented Snowflake architecture for real-world evidence analytics using the OMOP Common Data Model and Snowflake Cortex.
definition
What Real-World Evidence AI on Snowflake Actually Means
Real-world evidence, or RWE, is clinical evidence about a drug's use and outcomes drawn from real patient data (claims, electronic health records, registries) rather than a controlled clinical trial. The FDA now accepts RWE for new drug indications, and EMA's DARWIN EU initiative is scaling to 100 studies a year. Real-world evidence AI on Snowflake means running that cohort-building and outcomes analysis natively on a Snowflake data cloud, using the OMOP Common Data Model as the shared schema and Snowflake Cortex for the AI layer.
demo
The Accelerator: A Documented Snowflake Architecture, Built in the Open
This page shows Kriv AI's real-world evidence accelerator on Snowflake. In the interest of transparency: the environment and reference-architecture setup is in progress; the data loading, cohort analytics, and dashboard layers are specified but not yet built. We're presenting this honestly as a blueprint.
Architecture: Snowflake Data Cloud and Cortex AI for RWE Cohort Analytics
The design maps patient and claims data into the OMOP Common Data Model (version 5.4) inside Snowflake, with Snowpark Python (scikit-learn, lifelines) handling propensity score matching and Kaplan-Meier survival analysis, Snowflake Cortex handling natural-language-to-SQL cohort queries, and a five-page Streamlit-in-Snowflake dashboard for cohort building, comparative effectiveness, and drug repurposing signal detection.
What the Design Targets: Scale and Governance
The design targets roughly 96 million records across 2.3 million CMS SynPUF Medicare beneficiaries and 10,000 Synthea synthetic patients, mapped to OMOP CDM alongside the OHDSI Athena vocabularies (SNOMED, ICD-10-CM, RxNorm, LOINC), governed by Snowflake's Dynamic Data Masking, Row Access Policies, and Time Travel, the specific controls a HIPAA and 21 CFR Part 11 environment would require if real patient data were loaded.
status
What's Real Today, and What's Roadmap
Honestly: the environment setup is in progress, with 30 reference repositories reviewed across OMOP CDM tooling, Snowflake platform patterns, and statistical and causal-inference methods. The data loading, cohort-analytics SQL, Snowpark models, and Streamlit dashboard are designed and specified but not yet built and exercised on synthetic data. We'll update this page with real, measured results once the pipeline is running end to end.
differentiation
Why Kriv AI as Your Snowflake Real-World Evidence Implementation Partner
Build-Demo-Vanish: Why We Show Working Architecture Instead of a Capability Deck
A Big 4 real-world evidence engagement typically opens with an RWE strategy deck and a staff-augmentation plan. Kriv AI's methodology is to build a working proof of concept on synthetic data and real infrastructure first, demonstrate it, then either scope a production engagement or step back, no lingering idle cloud spend either way.
compliance
Governed, Auditable RWE: Compliance Designed Into the Snowflake Architecture
Dynamic Data Masking, Row Access Policies tied to defined roles (RWE administrator, researcher, analyst, auditor), and Time Travel for reproducible historical queries are all designed into the architecture from the start, the specific controls a HIPAA-aligned and 21 CFR Part 11 environment expects.
usecases
Use Cases: What Life Sciences Teams Build on This Foundation
Once built, this foundation is designed to support label-expansion evidence generation, post-market surveillance signal detection, payer value dossiers built on comparative-effectiveness analysis, and drug-repurposing signal detection from co-prescribing patterns, the core real-world evidence use cases a VP of R&D or Head of Real-World Evidence at a pharma or biotech company evaluates a platform against.
engagement
How a Real-World Evidence Snowflake Engagement Works With Kriv AI
A scoped engagement builds this architecture out against your real Snowflake environment and patient or claims data under a signed SOW and BAA, carrying the same masking, row-access, and audit design through from day one.
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Straight answers
Frequently asked questions about Real-World Evidence AI on Snowflake: An Implementation Blueprint, Not a Slide Deck
Is the real-world evidence accelerator built and running on Snowflake today?
No, and we want to be direct about it. The environment and reference-architecture setup is in progress (30 reference repositories reviewed). The data loading, cohort analytics, and dashboard layers are specified but not yet built and exercised on synthetic data.
What is Snowflake Cortex used for in this design?
Snowflake Cortex is designed to handle natural-language-to-SQL cohort queries and other AI-assisted analytics tasks directly inside the Snowflake data cloud, alongside Snowpark Python for propensity score matching and survival analysis.
Is Snowflake good for real-world evidence analytics?
Snowflake's governance features, Dynamic Data Masking, Row Access Policies, Time Travel, and Secure Data Sharing, map directly to what a regulatory-grade RWE platform needs, which is why this accelerator is designed around Snowflake rather than a generic data warehouse.
What data does this design target?
Roughly 96 million records across CMS SynPUF Medicare beneficiaries and Synthea synthetic patients, mapped to the OMOP Common Data Model alongside standard vocabularies including SNOMED, ICD-10-CM, RxNorm, and LOINC.
Is any real patient data used?
No. This accelerator, once built, will run entirely on synthetic and public reference data. No real patient data is used in the demonstration.
Can Kriv AI build this out for our pharma or biotech company?
Yes. A scoped engagement can build this architecture out against your real Snowflake environment under a signed SOW and BAA. Contact us to discuss scope and timeline.
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