Secondary Use of Clinical Data - Eliminating Engineering Overhead
Every healthcare organization faces the same challenge: clinical data is scattered across dozens of systems, trapped in formats that don't talk to each other, and buried in documents that machines can't read. Before any research study can begin, before any AI model can be trained, before any quality measure can be calculated, someone has to spend months turning this chaos into something usable.
This is the data engineering gap, and it's consuming resources that should be going toward actual healthcare innovation.
Why Healthcare Data Is So Hard to Work With
Consider what happens when a researcher wants to study diabetes outcomes. The data they need exists, but it's fragmented across the organization:
- Diagnoses live in the EHR problem list, but also scattered throughout clinical notes where physicians document "poorly controlled DM" or "patient's sugar has been running high"
- Medications appear in pharmacy systems, but dosage changes and adherence issues are documented in visit notes
- Lab results come from the lab system, but the clinical interpretation, "HbA1c trending up despite medication adjustment", exists only in free text
- Complications like neuropathy or retinopathy might be coded, or might only appear in specialist consultation notes
No single system contains the complete picture. And the systems that do contain pieces of it use different patient identifiers, different coding schemes, and different data formats.
The Reality of Healthcare Data
Critical clinical facts are frequently embedded in unstructured text, scanned documents, and reports rather than discrete fields. A patient's complete clinical story is never in one place, it's distributed across EHRs, PDFs, imaging systems, lab platforms, and claims databases, with limited interoperability and inconsistent standards.
The Hidden Costs of Data Fragmentation
When organizations try to work with this fragmented data, they run into predictable problems:
Loss of Clinical Context
When a note says 'no evidence of pneumonia,' a simple text search for 'pneumonia' will find it, and incorrectly suggest the patient had pneumonia. Temporal relationships, negation, and uncertainty are routinely lost during naive data extraction.
Inconsistent Coding
One system codes diabetes as ICD-10 E11.9, another uses a local code, and clinical notes refer to 'T2DM,' 'type 2 diabetes,' or 'NIDDM.' Without normalization, these are treated as different conditions.
Broken Relationships
A medication prescribed in one system, a lab result in another, and a diagnosis in a third all relate to the same patient, but connecting them requires reconciling different patient identifiers and timestamps.
Non-Deterministic Results
When every project builds its own data pipeline, the same underlying data produces different results depending on who processes it and how. This destroys trust in clinical and regulatory settings.
The Engineering Tax Every Organization Pays
These problems don't solve themselves. Someone has to fix them, and that someone is usually a team of data engineers spending months on work that has nothing to do with the actual research or clinical question.
Before a researcher can ask "which patients developed kidney disease after starting this medication?", someone has to connect to the pharmacy system, the EHR, and the lab system. Someone has to figure out how each system identifies patients and link those identifiers together. Someone has to map the medication names from the pharmacy's local codes to a standard vocabulary. Someone has to parse the lab results to understand what "kidney disease" looks like in the data. Someone has to handle the fact that half the relevant clinical information is buried in unstructured notes that no database query can reach.
This isn't glamorous work. It's data plumbing, tedious, time-consuming, and invisible to the people who ultimately use the results. But without it, the research question can't even be asked.
The Hidden Cost
Healthcare organizations invest man-years of engineering effort annually just to make clinical data usable for secondary purposes. Even mature organizations routinely report multi-year backlogs just to keep existing pipelines operational, before any new analytics or AI projects can even begin.
Every secondary use initiative, whether it's a research study, a registry, or an AI application, requires teams to work through the same painful sequence:
Connect to Data Sources
Navigate complex EHR integrations, APIs, and data extracts. Negotiate access. Handle authentication and security requirements.
Reverse-Engineer Schemas
Decipher proprietary data models. Figure out what fields actually mean. Document undocumented systems.
Reconcile Patient Identifiers
Link records across systems with different MRNs. Build or configure matching algorithms. Handle duplicates and conflicts.
Normalize Terminologies
Map local codes to standard vocabularies like SNOMED, RxNorm, and LOINC. Handle edge cases and unmapped concepts.
Extract from Unstructured Text
Build NLP pipelines to parse clinical notes. Handle negation, uncertainty, and context. Validate extraction accuracy.
Reasoning with Conflicting and Missing Facts
Detect duplicate information from multiple sources. Identify gaps in the clinical record. Merge redundant entries and flag inconsistencies for manual review.
The worst part? This effort is repeated across teams, departments, and use cases. The research team builds a pipeline for their study. The quality team builds another for their measures. The AI team builds a third for their models. Each pipeline solves the same problems independently, with slight variations that make them incompatible.
The Solution: a Patient Journey Intelligence Platform
What if, instead of rebuilding data pipelines for every project, organizations invested once in a reusable foundation?
That's the core idea behind Patient Journey Intelligence: a single platform that transforms fragmented clinical data into standardized, analysis-ready patient journeys, and keeps them continuously updated as new data arrives.
Build Once, Use Everywhere
Instead of every team solving the same data problems independently, create a shared foundation that all secondary use applications can build on.
Create Complete, Longitudinal Patient Journeys
When Patient Journey Intelligence processes your data, it creates complete, longitudinal views of each patient's clinical history. But what does "complete" actually mean?
It means that every piece of clinical information, whether it came from a physician's note, a lab system, a claims feed, or a scanned document from 2015, gets woven into a single, coherent patient story. The platform doesn't just dump data into a database; it understands how clinical facts relate to each other across time and across sources.
Consider what this enables: A researcher querying for "patients with diabetes who later developed kidney disease" doesn't need to manually link diagnosis codes to lab values to medication lists. The platform has already done that work, creating patient journeys where temporal relationships are explicit and clinical context is preserved.
Here's what that looks like in practice:
Longitudinal Patient Views
Complete timelines showing every encounter, diagnosis, treatment, and outcome, in chronological order with proper temporal relationships.
Cross-Source Integration
Data from EHRs, labs, imaging, clinical notes, and claims unified into a single patient record. No more silos.
Clinical Context Preserved
Negation, uncertainty, and assertion status captured correctly. 'No pneumonia' won't be confused with 'pneumonia.'
Beyond capturing data, the platform also addresses the operational challenges that make healthcare analytics so difficult to sustain:
Temporal Reasoning
The platform understands that a diagnosis in January, a treatment in February, and an outcome in March are part of the same clinical story.
Deterministic Processing
The same input always produces the same output. Results are reproducible, auditable, and trustworthy.
Continuous Updates
New data is automatically ingested and integrated. Patient journeys stay current without manual re-processing.
Transform Raw Clinical Data into Queryable Patient Records
The Patient Journey Intelligence automates the complex journey from raw healthcare data to analysis-ready patient intelligence through five integrated stages. Each stage addresses a specific challenge that would otherwise require custom engineering work for every project.
Raw clinical data doesn't arrive ready for analysis. A clinical note contains valuable information about diagnoses, medications, and symptoms, but it's buried in narrative text. A lab result might use a local code that means nothing outside your institution. Two different systems might record the same medication with different names, or the same patient with different identifiers. The platform handles all of this automatically, transforming fragmented inputs into clean, standardized, queryable patient records.
Here's how the transformation works:
Ingestion
Connect to EHR systems (FHIR, HL7 v2), ingest clinical notes (text, PDFs, scanned documents), import lab results, imaging metadata, and claims data.
Extraction
Apply NLP to identify clinical entities, extract relationships between them, and detect assertion status (present, absent, historical, family history).
Normalization
Map all concepts to standard vocabularies: SNOMED CT for diagnoses, RxNorm for medications, LOINC for labs, ICD-10-CM and CPT for billing codes.
Reasoning
Deduplicate entities, resolve conflicts between sources, ensure temporal consistency, and assign confidence scores to extracted facts.
Enrichment
Construct patient timelines, identify care episodes, analyze treatment pathways, and track outcomes over time.
OMOP Transformation
Map all processed data to OMOP CDM v5.4 tables, populate standard concept IDs, and generate analysis-ready datasets compatible with OHDSI tools.
Deliver Unified Data, OMOP Standards, and Full Provenance
Once data flows through this pipeline, your organization has:
Multimodal Data Integration
All your clinical data sources unified:
- Free-text clinical notes and reports
- Structured EHR extracts
- Laboratory results
- Medical imaging metadata
- Claims, registry data, and FHIR resources
OMOP Standardization
All data transformed to OMOP CDM v5.4:
- Consistent representation across sources
- Interoperability with OHDSI research tools
- Reproducible analytics methodology
- Cross-institutional collaboration
Complete Provenance
Every fact traceable to its source:
- Which system and document it came from
- AI model confidence scores
- Full transformation audit trail
- Precise timestamps at every step
Accelerate Research, Reduce Engineering Burden, and Ensure Compliance
When data engineering becomes a solved problem rather than an ongoing burden, the impact ripples across the organization:
Eliminate Duplicated Effort
Build the data foundation once. Every research study, registry, quality measure, and AI project builds on the same trusted source, no more parallel pipelines solving the same problems.
Accelerate Time to Value
What used to take months of data engineering now takes hours. Researchers can focus on research. Clinicians can focus on quality. Data scientists can focus on models.
Capture More Clinical Information
By extracting facts from unstructured notes, not just structured fields, organizations capture up to 40% more clinical information that would otherwise be invisible to analytics.
Enable Regulatory Trust
Deterministic, auditable processing with full provenance tracking. When regulators or auditors ask how a number was calculated, you can show them exactly.
Free Engineering Resources
Data engineering teams stop maintaining repetitive pipelines and start working on innovation. The backlog of 'data plumbing' work shrinks instead of grows.
Keep Data Secure
The platform runs entirely within your infrastructure, on-premises or in your private cloud. No PHI leaves your network. No data is shared with third parties.
Applications Across Healthcare
Once you have reliable, standardized patient journeys, a wide range of applications become possible. The key insight is that most secondary use challenges—whether research, quality measurement, population health, or AI development—share the same underlying requirement: complete, accurate, longitudinal patient data in a consistent format. When that foundation exists, teams stop rebuilding data pipelines for each project and start building on a shared asset that improves with every use case.
The applications below represent common starting points, but they're not separate products—they're different lenses on the same underlying patient journeys. A cohort identified for a research study can feed into a disease registry. Risk scores calculated for population health can power clinical decision support. AI models trained on de-identified research data can deploy directly to identified operational data. This interconnection is only possible because everything builds on the same standardized foundation:
Clinical Research
- Retrospective outcomes studies
- Clinical trial feasibility
- Comparative effectiveness research
- Multi-institutional collaboration
Quality & Performance
- Clinical performance measurement
- Registry reporting
- Care gap identification
- Performance benchmarking
Population Health
- Cohort identification
- Disease surveillance
- Risk stratification
- Care coordination
Patient Registries
- Disease-specific registries
- Automated abstraction
- Longitudinal outcome tracking
- Multi-site coordination
AI & Machine Learning
- Training data preparation
- Clinical decision support
- Predictive modeling
- Natural language applications
Drug Safety
- Adverse event detection
- Medication error identification
- Drug interaction surveillance
- Post-market monitoring
The Technical Foundation
All data is standardized to OMOP Common Data Model v5.4, the leading standard for observational health research adopted by over 400 institutions worldwide. OMOP provides a common data structure that represents patients, visits, conditions, medications, procedures, measurements, and observations in a consistent format, regardless of which EHR system or data source the information originated from. This standardization enables cross-institutional research collaboration, compatibility with the extensive OHDSI (Observational Health Data Sciences and Informatics) ecosystem of open-source tools and validated study packages, and reproducible cohort definitions that work identically across organizations. The platform populates all core OMOP domains and is architected for enterprise scale, supporting millions of patients and billions of clinical events with cloud-native or on-premises deployment options:
Supported OMOP Domains:
- Person, Observation Period, Visit
- Condition, Drug, Procedure Occurrence
- Measurement, Observation, Device
- Note, Specimen, Provider, Care Site
Why OMOP Matters:
- Enables cross-institutional analytics
- Compatible with OHDSI tools and methods
- Supports reproducible research
- Industry-standard cohort definitions
The platform architecture is designed for enterprise scale:
- Millions of patients, billions of events, parallel processing for high throughput
- Cloud-native or on-premises deployment, AWS, Azure, Databricks, Snowflake, or your own infrastructure
- Enterprise-grade security, HIPAA compliance, encryption at rest and in transit, role-based access control
Replace Fragmented Pipelines with a Unified Data Foundation
Most healthcare organizations face a common pattern: every new analytics initiative, research study, or AI project requires building custom data pipelines from scratch. Teams wait months for data engineering resources, accept incomplete datasets because unstructured data is too hard to process, and end up with results that can't be reproduced or compared across projects. The contrast between this fragmented approach and a unified data foundation is stark:
Without a Unified Foundation:
- Rebuild pipelines for every project
- Wait months for data engineering backlogs
- Accept incomplete data from structured fields only
- Sacrifice reproducibility to ad-hoc processing
- Struggle with inconsistent results across teams
With Patient Journey Intelligence:
- Build once, leverage everywhere
- Automated processing replaces manual engineering
- Complete data from structured + unstructured sources
- Standardized OMOP outputs with full provenance
- Built-in governance, audit trails, and de-identification
FAQ
The data engineering gap is the recurring cost of transforming fragmented clinical data into formats usable for research, AI, quality measurement, and registries. Healthcare organizations spend man-years of engineering effort annually rebuilding data pipelines for each new project because clinical data is scattered across EHRs, lab systems, imaging platforms, claims databases, and unstructured clinical notes.
Healthcare data is fragmented across dozens of systems that use different patient identifiers, coding schemes, and data formats. Critical clinical facts are embedded in free-text notes, scanned PDFs, and narrative reports rather than discrete fields. Without automated extraction and normalization, teams must manually reconcile these differences for every project.
Data fragmentation causes four major problems: loss of clinical context (negation and uncertainty are missed), inconsistent coding (the same condition has different codes across systems), broken relationships (patient records can't be linked across systems), and non-deterministic results (different teams get different answers from the same data).
Up to 40% of clinically relevant information exists only in unstructured text, including nuanced diagnoses, medication adherence issues, clinical interpretations of lab results, and specialist findings. Patient Journey Intelligence extracts these facts using language models that understand negation, uncertainty, and temporal context.
Without a unified platform, every project requires: connecting to data sources, reverse-engineering proprietary schemas, reconciling patient identifiers across systems, normalizing terminologies to standard vocabularies, extracting information from unstructured text, and reasoning through conflicting and missing facts. Patient Journey Intelligence automates all six steps.
Patient Journey Intelligence creates a single, shared data foundation that all secondary use applications build on. Instead of each research study, registry, quality measure, and AI project building its own pipeline, all teams query the same standardized OMOP CDM v5.4 patient journeys with one continuously updated source.
Longitudinal patient journeys are complete, chronological views of each patient's clinical history assembled from all data sources. Patient Journey Intelligence weaves together information from clinical notes, lab systems, imaging, claims, and EHR extracts into a single coherent timeline where temporal relationships, clinical context, and cross-source linkages are explicit and queryable.
Patient Journey Intelligence uses Language Models that detect negation ("no evidence of pneumonia"), uncertainty, assertion status (present, absent, historical, family history), and temporal relationships in clinical text. This prevents common errors like treating a ruled-out condition as a confirmed diagnosis, which naive text search would miss.
Patient Journey Intelligence processes data through six stages: ingestion (connecting to EHRs, notes, labs, claims), extraction (NLP to identify clinical entities and assertion status), normalization (mapping to SNOMED CT, RxNorm, LOINC, ICD-10-CM), reasoning (deduplication, conflict resolution, confidence scoring), enrichment (constructing patient timelines and treatment pathways), and OMOP transformation (mapping to CDM v5.4 tables).
OMOP Common Data Model v5.4 is the leading open standard for observational health research, adopted by over 400 institutions worldwide. Patient Journey Intelligence standardizes all data to OMOP to enable cross-institutional research collaboration, compatibility with OHDSI ecosystem tools (ATLAS, ACHILLES, CohortMethod), and reproducible analytics that work identically across organizations.
Patient Journey Intelligence populates all core OMOP CDM v5.4 domains: Person, Observation Period, Visit, Condition Occurrence, Drug Occurrence, Procedure Occurrence, Measurement, Observation, Device, Note, Specimen, Provider, and Care Site. The platform is architected for enterprise scale, supporting millions of patients and billions of clinical events.
Patient Journey Intelligence supports clinical research (retrospective studies, trial feasibility, comparative effectiveness), quality measurement and registry reporting, population health (cohort identification, risk stratification, care coordination), patient registries with automated abstraction, AI and machine learning model development, and drug safety surveillance including adverse event detection.
Patient Journey Intelligence is designed for healthcare organizations where multiple teams need standardized clinical data for secondary use: clinical research departments, quality improvement teams, population health analysts, registry abstractors, data science groups, and healthcare IT leaders. The platform is most valuable when multiple teams are rebuilding similar pipelines independently.
Patient Journey Intelligence is not designed for primary clinical care delivery, real-time EHR documentation, or billing system replacement. Organizations that need only a single, narrow data extract for one project may not benefit from a platform-level investment. The platform focuses exclusively on secondary use of clinical data.
Custom ETL pipelines address one use case at a time and must be rebuilt for each new project, creating an ever-growing engineering backlog. Patient Journey Intelligence replaces this pattern with a single platform that ingests all clinical data sources, extracts facts from unstructured text, normalizes to standard vocabularies, and produces continuously updated OMOP datasets with full provenance.
Yes. Patient Journey Intelligence uses deterministic processing so the same input always produces the same output. Every clinical fact includes full provenance tracking showing which source it came from, how it was extracted, and what transformations were applied. This reproducibility is essential for regulatory submissions and clinical audits.
Patient Journey Intelligence runs entirely within your infrastructure, on-premises or in your private cloud. No PHI leaves your network and no data is shared with third parties. The platform includes HIPAA-compliant encryption at rest and in transit, role-based access control, and comprehensive audit logging.
Patient Journey Intelligence maps all clinical concepts to standard vocabularies: SNOMED CT for conditions, procedures, and findings; RxNorm for medications; LOINC for laboratory tests; ICD-10-CM for billing codes, etc. This means "T2DM," "type 2 diabetes," "NIDDM," and ICD-10 E11.9 are all recognized as the same condition.
Patient Journey Intelligence supports cloud-native deployment on AWS, Azure, Databricks, or Snowflake, as well as on-premises deployment within your own infrastructure. The platform is designed for enterprise scale with parallel processing for high throughput across millions of patients and billions of clinical events.