Data Integration
Data Integration is where your journey to AI-ready clinical data begins. This is the foundational layer that connects to your healthcare systems, extracts clinical information from any format, and transforms it into standardized, analysis-ready datasets.
Unify Multimodal Clinical Data into OMOP
With Data Integration, you'll turn fragmented clinical data scattered across EHRs, documents, images, and legacy systems into a unified, standardized OMOP dataset. The platform handles everything automatically—connecting to sources, extracting facts from unstructured notes, mapping to standard terminologies, removing PHI, and organizing everything into patient timelines ready for research, registries, or AI applications.
Think of it as your data preparation autopilot: you point it at your clinical systems, configure what you need, and it continuously maintains clean, complete, standardized patient data.
How Data Integration Works
The platform follows a logical sequence to transform raw clinical data into AI-ready patient journeys:
Connect to Your Systems
Set up secure connections to EHRs, imaging systems, labs, and file repositories
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Ingest Clinical Data
Automatically pull documents, images, and structured data on schedules or on-demand
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Extract Clinical Facts
AI reads unstructured notes to find diagnoses, medications, procedures, and findings
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Standardize & Normalize
Map local terms and codes to standard vocabularies (SNOMED, RxNorm, LOINC)
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Build Patient Timelines
Organize all events into longitudinal OMOP patient journeys with full provenance
Data Integration Components
This section covers seven key modules that work together to prepare your clinical data. Here's what each one does and where to learn more:
Data Sources
What it does: This is where you configure connections to your clinical systems—EHRs, S3 buckets, SFTP servers, FHIR endpoints, and more. Once configured, these connections become reusable for all your ingestion jobs.
When to use it: Start here to set up your first connection before you can import any data.
Data Ingestion
What it does: The control center for actually importing data. You can run manual imports (upload files from your computer) or pull data from configured sources on schedules. Watch in real-time as documents are processed, de-identified, and transformed into OMOP format.
When to use it: After setting up data sources, use this to start importing clinical documents and see the entire pipeline in action.
Information Extraction
What it does: Uses healthcare-specific AI models to read unstructured clinical notes and extract structured facts—diagnoses, medications, lab results, procedures, and more. This is how the platform captures the critical clinical information that only exists in free-text notes.
When to use it: This runs automatically during ingestion, but you can configure custom extraction pipelines for specialized clinical domains or document types.
Go to Information Extraction →
Medical Terminology
What it does: Maps your local terms, abbreviations, and codes to standard medical vocabularies. Ensures that "MI" from one hospital and "myocardial infarction" from another both map to the same SNOMED concept for consistent analysis.
When to use it: Review this after initial ingestion to see how your terms are being standardized, or configure custom mappings for organization-specific terminology.
Medical Reasoning
What it does: Resolves conflicting information across sources, deduplicates entities, sequences events in time, and scores confidence. This is the "intelligence" layer that makes sense of messy real-world clinical data.
When to use it: Automatic during ingestion, but understanding this helps you interpret provenance and confidence scores in your final datasets.
Clinical Measures
What it does: Defines and computes clinical quality metrics, registry measures, and custom analytics on your OMOP data. Calculate things like cancer staging completeness, diabetes control rates, or treatment response metrics.
When to use it: After data is in OMOP format, use this to compute quality measures or registry-specific metrics for reporting and analytics.
Database Explorer
What it does: A SQL query interface to explore your OMOP data directly. Run queries, build custom cohorts, check data quality, and export results for external analysis tools.
When to use it: Anytime you want to validate your data, run ad-hoc queries, or export datasets for use in R, Python, or other analytics platforms.
De-Identification
What it does: Automatically removes protected health information (PHI) from clinical text, documents, and images using HIPAA-compliant methods. Creates research-ready datasets with consistent date-shifting and pseudonymization.
When to use it: Enable during ingestion when creating datasets for research, quality improvement, or any secondary use that requires PHI removal.
Your Path to Getting Started
Ready to integrate your first clinical data source? Follow this recommended path:
Start with Data Sources
Connect to a single EHR system or clinical database first. Test with one source before scaling up.
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Run Your First Ingestion
Import a small sample dataset (100-1000 documents) to validate the pipeline end-to-end.
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Validate Data Quality
Use Database Explorer to check completeness, review terminology mappings, and verify extracted facts.
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Scale and Refine
Add more sources, configure custom extraction pipelines, set up scheduled ingestion, and expand coverage.
Why Data Integration Transforms Healthcare AI
From Weeks to Hours
Traditional data engineering requires weeks of manual ETL development, custom parsers, and one-off scripts for each data source. Data Integration automates the entire pipeline—you'll have standardized OMOP data in hours, not weeks.
Capture the Missing 40%
Most clinical analytics miss critical diagnoses and findings documented only in unstructured notes. The platform's healthcare NLP automatically extracts this information, giving you complete clinical context.
One Standard, Infinite Uses
By standardizing everything to OMOP CDM v5.4, you build your data foundation once and reuse it for registries, quality measures, research cohorts, and AI models—without rebuilding pipelines each time.
Built-In Compliance
De-identification, audit trails, and provenance tracking are included. No need for separate PHI removal tools or custom lineage tracking systems.
Common Questions
What data formats can I import?
The platform supports FHIR (R4, STU3), HL7 v2, CSV, JSON, XML, PDF documents, clinical notes (TXT, RTF), scanned documents, DICOM metadata, and direct database connections (JDBC/ODBC).
How long does initial setup take?
Most organizations connect their first data source and complete an initial ingestion within 1-2 days. Full enterprise deployment typically takes 8-12 weeks.
Can I keep my data updated automatically?
Yes. Configure scheduled ingestion (daily, weekly, monthly) on any data source, and the platform will continuously maintain up-to-date patient timelines.
What happens to data quality issues?
The Medical Reasoning module automatically resolves conflicts, deduplicates entities, and scores confidence. You'll see provenance and confidence scores for every fact, making it easy to identify and address quality issues.
Quick Reference
Supported Terminology Standards
SNOMED CT for conditions, procedures, and clinical findings | RxNorm for medications | LOINC for lab tests and measurements | ICD-10-CM for diagnosis codes | CPT for procedures
OMOP Common Data Model v5.4
All data is standardized to OMOP CDM v5.4—the leading standard for observational research. This enables direct compatibility with OHDSI tools (ATLAS, ACHILLES), multi-institutional collaboration, and reproducible research.
Enterprise Scale
The platform supports millions of patients, billions of clinical events, and terabytes of unstructured content with distributed processing for enterprise-scale deployments.