Transform Fragmented Clinical Data into Unified Patient Journeys
A single, reusable foundation that eliminates duplication and automates the complex journey from raw healthcare data to analysis-ready patient intelligence.
The Problem: Fragmented Clinical Data Creates a Massive Engineering Burden
The Reality of Healthcare Data
Healthcare data is scattered across EHRs, PDFs, imaging systems, lab platforms, and claims databases, with limited interoperability and inconsistent standards. Critical clinical facts are frequently embedded in unstructured text, scanned documents, and reports rather than discrete fields.
The Hidden Cost of Data Fragmentation
📉 Loss of Clinical Context
Temporal relationships, negation, and uncertainty are stripped away during data extraction
🔀 Inconsistent Coding
Different terminologies and vocabularies across systems create integration nightmares
🔗 Broken Relationships
Difficulty maintaining cross-document clinical connections and patient linkages
⚠️ Non-Deterministic AI
Unpredictable results that reduce trust in clinical and regulatory settings
The Engineering Tax
💰 The Hidden Cost
Healthcare organizations invest man-years of engineering effort annually just to make clinical data usable for secondary purposes. This effort is not driven by innovation, but by necessity: raw clinical data is heterogeneous and fundamentally unprepared for analytics or AI.
Even mature organizations routinely report multi-year backlogs just to keep secondary use pipelines operational—before any actual analytics or AI value is delivered.
Across large health systems, life sciences companies, and payers, secondary use initiatives repeatedly require teams to:
Discover and connect to new data sources
Navigate complex EHR integrations, APIs, and data extracts
Reverse-engineer undocumented schemas
Decipher proprietary data models and field meanings
Reconcile inconsistent patient identifiers
Link records across systems with different MRNs and matching algorithms
Normalize incompatible terminologies
Map local codes to standard vocabularies (SNOMED, RxNorm, LOINC)
Re-extract clinical meaning from raw text
Build NLP pipelines to parse clinical notes and reports
The Duplication Problem
🔁 Repeated Across Teams
Crucially, this effort is repeated across teams, departments, and use cases. Instead of investing once in a reusable secondary use foundation, organizations end up rebuilding similar pipelines over and over again.
The Solution: Patient Journey Intelligence
Build Once, Use Everywhere
A single, reusable foundation that eliminates duplication and transforms raw clinical data into standardized, analysis-ready patient journeys.
A Single Foundation for All Secondary Use Applications
Patient Journey Intelligence is the systematic process of assembling, standardizing, and enriching multimodal healthcare data to create comprehensive, chronological views of individual patient experiences across care settings, timeframes, and data sources.
Longitudinal Patient Views
Complete timelines of clinical events, encounters, diagnoses, treatments, and outcomes
Cross-Source Integration
Unified representation of data from EHRs, imaging, labs, and clinical notes
Temporal Reasoning
Understanding the sequence and relationships of clinical events over time
Clinical Context Preservation
Maintained semantic meaning, provenance, and confidence across transformations
Deterministic Processing
Reproducible, auditable transformations for regulatory and clinical trust
Instant Updates
Automatic ingestion and analysis of new data as it becomes available, with seamless integration into the unified OMOP knowledge base
How It Works: From Raw Data to Clinical Intelligence
The Automated Pipeline
The platform automates the complex journey from disparate healthcare data to actionable intelligence through five integrated stages.
Ingestion
Sources: EHR systems (FHIR, HL7 v2), clinical notes (text, PDFs, scanned docs), lab results, imaging metadata, claims and registry data
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Extraction
Capabilities: Named Entity Recognition, Relation Extraction, Assertion detection (negation, uncertainty, temporal context), Clinical context preservation
↓
Normalization
Vocabularies: SNOMED CT, RxNorm, LOINC, ICD-10-CM, CPT
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Reasoning
Capabilities: Entity deduplication, Conflict resolution, Temporal consistency, Confidence scoring
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Enrichment
Features: Timeline construction, Care episode identification, Treatment pathway analysis, Outcome tracking
Key Capabilities
Multimodal Data Integration
Free-text clinical notes and reports
Structured EHR extracts
Laboratory results
Medical imaging metadata
Registry data & FHIR resources
OMOP-Centered Standardization
Consistent representation across sources
Interoperability with research tools
Reproducible analytics and research
Cross-institutional collaboration
Quality measure computation
Provenance & Auditability
Source attribution: Which system and document
Extraction confidence: AI model scores
Transformation lineage: Full audit trail
Temporal markers: Precise timestamps
Business Impact
⚡ Eliminate Duplication
Build once, use everywhere. Create a single, reusable secondary use foundation instead of rebuilding pipelines for every project.
🚀 Accelerate Time to Value
Transform weeks of manual data engineering into hours of automated processing.
💼 Reduce Engineering Burden
Free specialized data engineering teams from repetitive pipeline maintenance to focus on innovation.
📈 Improve Data Completeness
Capture up to 40% more clinical information by extracting facts from unstructured notes.
✅ Enable Regulatory Trust
Deterministic, auditable processing with full provenance tracking meets clinical and regulatory requirements.
🔒 Secure On-Premises Deployment
Zero data sharing. Platform runs entirely within your VPN or on-premises infrastructure. No PHI leaves your network or is shared with third parties.
Clinical Applications Powered by Patient Journey Intelligence
🔬 Clinical Research & RWE
- Retrospective outcomes studies
- Clinical trial feasibility
- Comparative effectiveness research
- Multi-institutional collaboration
📊 Quality & Performance
- Clinical performance measurement
- Registry development and reporting
- Care gap identification
- Performance benchmarking
👥 Population Health
- Cohort identification and segmentation
- Disease surveillance
- Risk stratification
- Care coordination
📝 Patient Registries
- Disease-specific registry development
- Automated data extraction and abstraction
- Longitudinal outcome tracking
- Multi-site registry coordination
🤖 AI & Machine Learning
- Training data for predictive models
- Clinical decision support systems
- Natural language understanding
- Computer vision for medical imaging
💊 Pharmacovigilance & Drug Safety
- Adverse event detection and reporting
- Medication error identification
- Drug-drug interaction surveillance
- Post-market safety monitoring
Technical Foundation
🧬 OMOP Common Data Model v5.4
All data standardized to the leading observational research standard
Supported Domains:
- Person, Observation Period, Visit
- Condition, Drug, Procedure Occurrence
- Measurement, Observation, Device
- Note, Specimen, Provider, Care Site
Benefits:
- Cross-institutional analytics
- Reproducible research methodology
- Interoperability with OHDSI tools
- Consistent cohort definitions
⚙️ Distributed Processing Architecture
- Scalable to millions of patients and billions of events
- Parallel processing for high throughput
- Cloud-native or on-premise deployment
- Enterprise-grade security and compliance
Why Patient Journey Intelligence Matters
❌ Traditional Approaches Require:
- Rebuild pipelines repeatedly for each new use case
- Wait months or years for data engineering backlogs to clear
- Accept incomplete data due to structured field limitations
- Sacrifice reproducibility due to ad-hoc transformations
✅ Patient Journey Intelligence Provides:
- A reusable foundation: Build once, leverage everywhere
- Automated processing: Replace manual engineering
- Complete data capture: Structured + unstructured
- Standardized outputs: OMOP CDM consistency
- Built-in governance: Provenance, audit trails, de-ID
Begin Your Implementation Journey
Ready to transform your healthcare data into patient journey intelligence?
Understand Requirements
Learn about the Requirements for Secondary Use of Clinical Data
Explore the Architecture
Review the Anatomy of Patient Journey Intelligence platform
Assess Readiness
Evaluate your Data Readiness for implementation
Plan Deployment
Contact sales@johnsnowlabs.com to discuss deployment options (AWS, Azure, Databricks, Snowflake, On-Premise)