In modern precision medicine, two patients can walk into a clinic with the exact same clinical profile and receive the exact same treatment, yet follow entirely different disease trajectories and outcomes.

Real-World Evidence (RWE) captures these variations but often lacks biological depth. While EHR data reflects the longitudinal trajectory of care, it cannot explain the underlying molecular mechanisms -genomic variants, transcriptomic dysregulation, or pathway alterations driving those outcomes.

Our LLM-assisted platform Polly standardizes fragmented, multi-modal clinical data into an AI-ready foundation. By automating the alignment of diverse RWD to shared analytical frameworks, we bridge the gap between phenotypic RWE and multi-omics data up to 3x faster.

The Problem: The Hidden Costs of the Traditional ETL Workflow

Systematic integration of clinical EHR outcomes with molecular data (like TCGA) relies on establishing linkage anchors that are - shared data points (like demographics, diagnoses, or encounter timelines) that connect a patient's clinical record to their multi-omics profile.

To create these anchors, organizations must standardize fragmented data sources into a Common Data Model (CDM) like OMOP v5.4. Historically, this is a laborious, manual workflow that consistently breaks down across four critical stages:

• Data Profiling: Data scientists spend weeks just trying to understand source structures, distributions, and completeness before mapping can even begin.
• Schema Chaos: Proprietary tabular schemas vary wildly across hospitals, making Source-to-OMOP alignment incredibly complex and brittle.
• Vocabulary Mapping: Converting localized source codes and unstructured physician notes into standard concepts is a painstaking process highly prone to human error.
• Lack of Native Support: Standard OMOP is optimized purely for observational data; it lacks native support for Genomics or Transcriptomics, requiring teams to build complex, custom workarounds from scratch.

These hurdles result in about 80% of a data scientist's time lost in manual data preparation. It requires heavy expertise in maintaining ETLs, leading to increased costs, longer time-to-value, and a 6-month bottleneck just to make the data usable.

The Solution: LLM-Assisted Harmonization in 72 Hours

Our LLM-powered platform, Polly, standardizes multi-modal clinical data into an AI-ready foundation which helps teams transition from raw data to biologically stratified patient phenotypes up to 3x faster.

• Unprecedented Efficiency: Compress the data preparation phase. Turn raw, fragmented EHR (Text, Tabular, FASTA, CSV) into an analysis-ready OMOP dataset in 72 hours, not 6 months.
• True Multi-Modal Scale: We extend the standard OMOP model by building custom Variant Tables with direct Foreign Key relationships to core clinical records. This provides the native support for genomics that single-source vendors cannot match.
• Transparent AI Mapping and Standardization: Polly uses advanced LLMs trained on millions of biomedical sentences for validated concept mapping. Every mapping comes with a confidence score based on semantic alignment and data quality ensuring an auditable, regulatory-ready trail results.

Case Study: Uncovering Hidden Lung Cancer Phenotypes

A preclinical R&D team needed to de-risk their oncology programs by integrating Real-World Data (RWD) with Public Omics Data (TCGA) in under 4 months.

• The Execution: Using Polly platform, millions of RWD records were mapped to our extended OMOP model in just 4 weeks. By establishing linkage anchors  based on age bins and disease ICD codes, the team built a unified feature matrix and applied unsupervised clustering to the data.
• The Breakthrough: The pipeline successfully uncovered 4 distinct, molecularly informed lung cancer phenotypes. While these patients looked identical based on standard EHR billing codes, the integrated data revealed drastically different Overall Survival (OS) trajectories:
  • EGFR-driven (n=94): EGFR 71%, stage I–II, young (OS: 44 months)
  • Older / comorbid early-stage (n=377): Low EGFR, early stage, no drivers (OS: 33 months)
  • KRAS/STK11-mutant (n=138): KRAS 73%, STK11 67%, TP53 62% (OS: 23 months)
  • Advanced / high-burden (n=191): Stage III–IV, CEA 80, Hgb 11 (OS: 12 months)

Without the integrated molecular layer, these crucial survival differences would have remained entirely hidden.

Enabling Precision for Clinicians and Bioinformaticians

Standardized, multi-modal EHR data transforms your evidence pipeline. Trusted by over 70+ Biopharma, Biotech, and Diagnostics partners, Polly enables:

• Pharmacovigilance: Harmonized surveillance for faster adverse event monitoring.
• Comparative Effectiveness Research: Reproducible, head-to-head outcome studies across multiple health systems.
• Trial Feasibility: Quickly assess site viability and identify eligible patients using biologically grounded cohorts.
• FDA RWE Submission-Ready Lineage: Fully auditable trails with transparent AI confidence scoring that meet regulatory standards.

The Elucidata Impact: Scale, Speed, and Precision

By moving to Polly’s LLM-assisted harmonization pipeline, organizations unlock massive scale:

• 72 Hours vs. 6 Months: Reclaim the 80% of time data scientists currently waste on manual data wrangling and ETL maintenance.
• Millions of Records in 4 Weeks: Unprecedented ingestion and standardization speed for multi-modal patient profiles spanning EHR, omics, imaging, and claims.
• Validated AI Confidence: Our penalty-based AI calibration combats "LLM overconfidence," ensuring high-fidelity mapping with transparent scoring rather than opaque black-box transformations.
• 70+ Industry Partners: Trusted by leading Biopharma, Biotech, and Diagnostics organizations to eliminate ETL bottlenecks and power downstream AI/ML models.

See Polly Norm in Action

At Elucidata, we eliminate manual ETL bottlenecks. Automate your clinical data harmonization, seamlessly link real-world outcomes to molecular profiles, and discover biologically stratified cohorts faster.

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