De-identified OMOP Research Registry

The Patient Journey Intelligence platform automatically maintains synchronized identified and de-identified OMOP datasets in parallel, enabling researchers to query compliant data instantly while operational systems use the same standardized patient journeys

Clinical research depends on access to comprehensive patient data, but protecting patient privacy creates a fundamental bottleneck. The De-identified OMOP Research Registry eliminates this tradeoff by automatically maintaining parallel identified and de-identified versions of your OMOP patient journeys—synchronized in real-time, standardized to OMOP CDM, and ready for immediate querying. Researchers get instant access to compliant data without waiting for manual de-identification, while clinical operations use the same curated patient journeys with full PHI for care delivery and quality improvement.

The Research Data Access Bottleneck

Healthcare organizations collect rich clinical data during routine care, but making it available for research remains painfully slow and expensive.

The Gap: Manual De-Identification Can't Scale

Researchers need access to real-world clinical data to study treatment effectiveness, develop AI models, conduct epidemiological studies, and assess trial feasibility. But accessing this data requires removing Protected Health Information (PHI) to comply with HIPAA, GDPR, and institutional review board requirements. This creates persistent challenges:

4-8 Week De-Identification Delays Manual chart review and PHI removal take weeks or months per dataset request. By the time researchers receive data, clinical knowledge has advanced and patient populations have changed. Feasibility studies that could take hours stretch into months, delaying grant applications and trial recruitment.
Inconsistent De-Identification Methods Each research project re-invents de-identification pipelines. One team uses simple regex patterns and misses contextual PHI. Another applies over-aggressive masking that destroys clinical utility. Date-shifting approaches differ across studies, breaking temporal relationships. This inconsistency creates compliance risk and makes results non-reproducible.
Research-to-Production Gap Models trained on manually de-identified research exports fail when deployed on operational data because feature definitions differ, terminologies don't match, and preprocessing steps aren't documented. A readmission prediction model trained on research data can't run on live EHR feeds without extensive re-engineering.
Stale Snapshots Instead of Living Data Traditional research databases are point-in-time exports that quickly become outdated. Researchers work with 6-month-old data while recent clinical insights and emerging patient cohorts remain inaccessible. Continuous research questions require repeated manual extracts and endless IRB amendments.
Separate Infrastructure for Identified and De-Identified Data Organizations maintain duplicate databases—one identified for operations, one de-identified for research—doubling storage costs and creating version drift. Updates to operational data don't automatically flow to research databases, requiring manual synchronization and introducing inconsistencies.

Real-World Impact

Consider a health system developing an AI model to predict sepsis onset:

Months waiting for de-identified training data as IT manually reviews clinical notes, redacts PHI, and exports database snapshots
Model trained on 18-month-old data because that's the last approved research extract, missing recent protocol changes and emerging patient populations
Feature definitions differ between research and production because research used manually processed notes while production uses live EHR extracts with different parsing logic
Model fails at deployment because date references shifted differently, medication codes don't align, and lab values were normalized using inconsistent logic
New cardiovascular research project starts from zero six months later, rebuilding similar de-identification pipelines despite identical underlying data needs

This cycle wastes resources, delays discoveries, and prevents organizations from compounding their AI and research investments across projects.

How Patient Journey Intelligence Eliminates the Research Data Bottleneck

Patient Journey Intelligence provides a fundamentally different approach: continuously synchronized identified and de-identified OMOP datasets that stay up-to-date in real-time, eliminating manual de-identification delays while ensuring research and operational systems use identical data foundations.

Parallel Datasets: One Pipeline, Two Compliant Outputs

The platform automatically maintains two synchronized versions of every patient journey, generated from the same source data pipeline:

Identified OMOP Dataset

Full PHI preserved for clinical operations, care coordination, quality improvement, and point-of-care AI applications

Complete patient names, MRNs, addresses
Exact dates and timestamps
Provider identifiers and locations
Contact information for care coordination
Real-time updates as clinical data arrives

De-identified OMOP Dataset

HIPAA Safe Harbor and GDPR compliant with consistent pseudonyms, date-shifting, and PHI removal for research and external collaboration

Pseudonymized patient IDs (consistent across visits)
Date-shifted with temporal relationships preserved
Geographic info limited to state/country level
All 18 HIPAA identifiers removed or generalized
Automatically synchronized with identified dataset

The Critical Advantage: Identical Semantics, Different Privacy Levels

Because both datasets are generated from the same automated pipeline, they maintain identical data semantics:

Same feature definitions: A "diabetes diagnosis" means the same thing in both datasets—same extraction logic, same terminology mappings, same temporal windows
Same data lineage: Both datasets trace back to the same source documents with identical transformation steps documented
Same OMOP structure: Identical table schemas, concept IDs, vocabulary mappings, and relationship hierarchies
Same update cadence: New clinical data flows into both datasets simultaneously, keeping them synchronized

This eliminates the research-to-production gap. Train an AI model on the de-identified research dataset, then deploy it directly on the identified operational dataset—no re-engineering, no feature drift, no surprises. The model sees the same data structure, terminologies, and temporal patterns in both environments.

Continuous Synchronization: Always-Current Research Data

Unlike traditional point-in-time research extracts, the de-identified OMOP dataset stays continuously synchronized with your clinical systems:

New clinical documents arrive (notes, labs, imaging reports) from your EHR or data feeds
AI-powered extraction processes unstructured content and maps findings to OMOP concepts
Parallel updates occur simultaneously:
- Identified OMOP dataset updated with full PHI for operational use
- De-identified OMOP dataset updated with compliant pseudonyms and date-shifting
Researchers query immediately without waiting for manual de-identification or IT requests

This means researchers always work with current patient populations, recent treatment patterns, and up-to-date outcomes—not stale snapshots from months ago.

De-Identification Compliance: HIPAA Safe Harbor and Expert Determination

The platform supports two de-identification pathways to meet different institutional needs and regulatory requirements.

HIPAA Safe Harbor Method

The Safe Harbor method provides straightforward compliance by removing or generalizing all 18 HIPAA identifiers. This approach requires no statistical analysis or external certification—if the specified identifiers are removed, the data is considered de-identified under HIPAA.

🔒 HIPAA Safe Harbor: 18 Required Removals

Names (patient, relatives, employers)
Geographic subdivisions smaller than state (addresses, cities, ZIP codes except first 3 digits)
Dates directly related to the individual (birth, admission, discharge, death) - except year
Telephone, fax, email
Social Security numbers
Medical record numbers
Health plan beneficiary numbers
Account numbers
Certificate/license numbers

Vehicle identifiers and serial numbers (license plates, VINs)
Device identifiers and serial numbers (pacemakers, insulin pumps)
Web URLs
IP addresses
Biometric identifiers (fingerprints, voiceprints, retinal scans)
Full-face photos and comparable images
Any other unique identifying number, characteristic, or code

Dates Handling: All dates are shifted by a consistent random offset per patient (e.g., +127 days), preserving temporal relationships between events while obscuring exact calendar dates. Ages over 89 are aggregated to "90+".

When to Use Safe Harbor:

Straightforward compliance without statistical expertise required
External data sharing where re-identification risk must be minimized
Multi-site collaborations where consistent de-identification rules simplify governance
Limited loss of data utility is acceptable (e.g., precise dates not needed for your analysis)

HIPAA Expert Determination Method

The Expert Determination method allows retention of more granular data (e.g., 5-digit ZIP codes, month-level dates, ages over 89) by having a qualified statistician certify that the re-identification risk is "very small." This preserves more clinical utility while maintaining HIPAA compliance.

📊 HIPAA Expert Determination: Risk-Based Assessment

Under the Expert Determination pathway, a qualified expert with statistical and scientific knowledge analyzes your dataset and certifies that the risk of re-identification is "very small." This allows retention of more detailed information than Safe Harbor while remaining HIPAA compliant.

What Can Be Retained:

More precise geographic data (5-digit ZIP codes, county-level locations) if population density is sufficient
Month and year of key clinical events (instead of year-only)
Ages over 89 without aggregation if cohort size justifies it
Rare diagnoses or procedures that might be indirectly identifying, if statistical risk assessment demonstrates low re-identification probability

John Snow Labs Expert Determination Service: Our team includes qualified statisticians who can perform the required risk assessment and provide formal Expert Determination documentation for your de-identified OMOP dataset. This service includes statistical disclosure control analysis, re-identification risk modeling, and comprehensive written certification that satisfies HIPAA requirements and institutional review board expectations.

When to Use Expert Determination:

Preserving granular dates or geographic data is critical for your research questions
Studying rare diseases or small populations where Safe Harbor might over-suppress data
Internal use cases where additional utility justifies the expert review process
You need formal documentation for IRB submissions or regulatory audits

For organizations subject to the European Union's General Data Protection Regulation (GDPR), de-identification must address additional sensitive attributes beyond HIPAA's 18 identifiers. GDPR considers special categories of personal data that require explicit consent or lawful processing basis, and pseudonymization must account for these categories.

🔒 GDPR: Additional Sensitive Attributes

GDPR requires removal or pseudonymization of all personal data identifiers, including the core HIPAA identifiers plus additional sensitive attributes that could reveal:

Core Identifiers (overlaps with HIPAA):

Names and surnames
Postal addresses and precise location data
Email addresses and phone numbers
National identification numbers (passport, tax ID)
Health insurance and medical record numbers
Bank account and financial identifiers
IP addresses and device identifiers
Biometric data (fingerprints, facial images)
Genetic data identifiers

GDPR-Specific Sensitive Attributes:

Religious or philosophical beliefs (e.g., religious affiliation documented in social history)
Trade union membership (documented in employment or insurance records)
Political opinions or affiliations (e.g., mentioned in clinical notes)
Sexual orientation or sexual life (documented in sexual history, partner gender references)
Racial or ethnic origin (requires careful handling beyond clinical race/ethnicity categories)
Criminal convictions or offenses (mentioned in social history, forensic evaluations)

Examples in Clinical Context:

Religious beliefs: "Patient declined blood transfusion due to Jehovah's Witness faith" → Redacted or generalized to "declined transfusion due to personal beliefs"
Political affiliation: "Patient active in local labor union, reports workplace stress" → Union membership reference removed
Sexual orientation: "Patient reports same-sex partner" → Gender-neutral language or partner reference removed if not clinically essential
Criminal history: "Patient has history of incarceration" → Redacted unless directly relevant to clinical care and approved by ethics board

GDPR requires additional documentation:

Data Processing Agreements (DPA) with any third parties accessing the data
Legitimate interest assessments or explicit consent documentation
Data retention policies and automated deletion workflows
Subject access rights mechanisms for patients to request their data or deletion

The platform's audit logging and provenance tracking support these GDPR requirements by documenting data lineage, access patterns, and transformation history.

Automated De-Identification Across All Data Modalities

Patient Journey Intelligence applies comprehensive de-identification across every data type in your clinical environment, ensuring consistent privacy protection regardless of where PHI appears.

Detect and Redact PHI from Unstructured Clinical Text

Medical Language Models trained on clinical documentation identify and remove PHI from free-text notes, discharge summaries, pathology reports, and radiology interpretations—handling medical jargon, abbreviations, and contextual references that generic NER tools miss.

Pseudonymize Structured EHR Fields

Database fields containing names, MRNs, SSNs, and identifiers are replaced with consistent pseudonyms across all tables. Each patient receives the same pseudonym throughout the de-identified dataset, preserving referential integrity for longitudinal analysis.

Shift Dates with Temporal Consistency

All dates for each patient are shifted by the same random offset, preserving time intervals between events (e.g., days between diagnosis and treatment start) while obscuring exact calendar dates. Ages over 89 are aggregated to "90+" per HIPAA requirements.

De-identify DICOM Imaging Metadata

DICOM headers containing patient names, birth dates, study dates, and device serial numbers are automatically scrubbed or pseudonymized, while preserving clinically relevant imaging metadata and study identifiers for cross-referencing.

Maintain Provenance and Audit Trails

Complete data lineage tracking documents which source records contributed to each de-identified patient journey, what transformations were applied, and when de-identification occurred—satisfying IRB and regulatory audit requirements without manual documentation.

Generate Validation Reports for Compliance

Automated reports summarize de-identification coverage (% of records processed), PHI detection rates, and compliance with Safe Harbor or Expert Determination requirements—providing IRB-ready documentation without manual chart audits.

Accessing Your De-Identified Research Data

The platform provides multiple access methods to fit your research workflows, from direct SQL queries to API integrations and batch exports.

Query Directly with SQL

Full PostgreSQL access to all OMOP CDM v5.4 tables with standard structure and vocabulary tables. Use your preferred SQL client or BI tools (DBeaver, DataGrip, Tableau) for complex cohort queries and longitudinal analysis.

Use for: Exploratory analysis, custom queries, BI dashboards

Integrate via REST API

Programmatic access with cohort definition endpoints, patient-level data retrieval, aggregate statistics, and FHIR R4-compatible resources. OAuth 2.0 authentication with role-based permissions.

Use for: Custom applications, automated pipelines, real-time integrations

Export for External Analysis

Full database dumps (PostgreSQL, Parquet), CSV/flat files for R/Python/SAS, FHIR bundles for interoperability, or custom formats (Spark, BigQuery, Snowflake) tailored to your analytical needs.

Use for: Statistical analysis, ML model training, external collaborations

What You Get: Measurable Outcomes from De-Identified Research Datasets

Organizations using the De-identified OMOP Research Registry report significant improvements in research productivity, compliance efficiency, and AI development velocity. By eliminating manual de-identification bottlenecks and maintaining synchronized datasets, institutions can compound their research investments across multiple use cases rather than rebuilding infrastructure for each project.

⚡ 95% Faster Time-to-Research

Eliminate 4-8 week de-identification bottlenecks. Researchers query pre-built de-identified data immediately—start analyzing in minutes instead of waiting months for manual PHI removal and data preparation.

🔄 Always-Current Research Data

Continuous synchronization means researchers always work with the latest clinical data. No stale snapshots, no manual refresh cycles—your research database stays current automatically as new patient data arrives.

🤖 Zero Feature Drift Between Research and Production

Train AI models on de-identified research data, deploy on identified operational data with identical semantics. Same feature definitions, terminologies, and temporal logic—eliminating the research-to-production gap that causes model failures.

🌐 Accelerate Multi-Site Studies

Standardized OMOP format enables seamless data sharing across institutions. Launch federated research networks without complex data harmonization or restrictive data use agreements.

🎯 10x More Research Output

Self-service access empowers researchers to explore multiple hypotheses and iterate quickly. Complete feasibility studies in hours, not weeks—dramatically increasing your institution's research productivity.

🏆 Competitive Grant Advantage

Demonstrate immediate data availability in grant applications. Provide preliminary data and feasibility evidence that reviewers demand—funded researchers can start analyzing on day one.

🔐 Enterprise-Grade Security

End-to-end encryption, role-based access controls, comprehensive audit logs, and flexible deployment options (cloud or on-premise). Air-gapped environments supported for maximum security requirements.

📈 Scale Research Without Scaling Staff

Automated workflows and self-service tools mean your existing team can support exponentially more research projects. Handle batch processing and high-volume queries without adding headcount.

🔍 Full Audit Trail & Reproducibility

Complete provenance tracking and version control ensure reproducible research. Every query, export, and analysis is logged—satisfying journal requirements and regulatory audits with zero manual effort.

Research Use Cases

The De-identified OMOP Research Registry supports a wide range of research applications, from retrospective outcomes studies to AI model development and multi-site collaborations. Because the data is already standardized to OMOP and continuously updated, researchers can launch new studies immediately without waiting for custom data preparation.

🔬 Retrospective Outcomes Studies

Analyze treatment effectiveness, disease progression, and long-term outcomes using real-world clinical data spanning multiple years of patient care.

Example: Comparative effectiveness of diabetes medications in elderly populations with comorbidities

🧪 Clinical Trial Feasibility

Assess patient availability and eligibility for proposed clinical trials before recruitment begins, reducing screen failures and accelerating enrollment timelines.

Example: Identify 200 treatment-naive metastatic breast cancer patients for Phase III trial in under 24 hours

📊 Comparative Effectiveness Research

Compare real-world outcomes across different treatment strategies, medications, or interventions using observational data that reflects actual clinical practice.

Example: Compare surgical vs. medical management of coronary artery disease in high-risk populations

🤖 AI Model Development

Generate curated training datasets with proper train/test splits, temporal validation sets, and feature engineering—all from standardized OMOP data that matches your production environment.

Example: Train predictive models for hospital readmission risk, then deploy on identified operational data with zero feature drift

🏥 Multi-Site Collaboration

Share standardized, de-identified datasets across institutions for federated research networks, rare disease studies, or comparative effectiveness analyses requiring larger sample sizes.

Example: Multi-center rare disease natural history studies with harmonized OMOP data from 10+ institutions

📈 Epidemiological Studies

Investigate disease incidence, prevalence, risk factors, and population trends using longitudinal patient journeys that capture complete care sequences over time.

Example: COVID-19 outcomes in immunocompromised populations, stratified by immunosuppression type and duration

The Research Data Access Bottleneck​

The Gap: Manual De-Identification Can't Scale​

Real-World Impact​

How Patient Journey Intelligence Eliminates the Research Data Bottleneck​

Parallel Datasets: One Pipeline, Two Compliant Outputs​