was successfully added to your cart.

Patient Journey Intelligence

Ingest messy, multimodal patient records with automated de-identification, conflict resolution, and confidence tracking.

Mapped to OMOP and standard codes, the data powers cohorts, AI agents, registries, and clinical research.

Trusted by Leading Healthcare Organizations

Production-Grade Accuracy

Independently verified by leading health systems and published in peer-reviewed research
99%+

De-identification
Accuracy

96%+

Medical Information
Extraction Accuracy

98%

Entity Coding
Accuracy

Clinical De-identification
Independent blind testing vs. human expert reviewers validated 99% de-identification accuracy
We can now share de-identified data with research partners confidently, knowing it meets the highest privacy standards.
Vivek Tomer Principal Data Scientist, Providence Health
Oncology Data Curation
Extracted 60,717 SDOH factors from 13,258 oncology notes.
"NLP-derived elements dramatically improved our ability to match oncology patients to clinical trials — especially when cancer staging and biomarker findings were crucial but missing from structured EMR data
Scott Newman, Senior VP of Life Sciences
Peer-Reviewed Publication
Extracted 60,717 SDOH factors from 13,258 oncology notes.
John Snow Labs outperformed benchmark models by 5%. Its extensive pretraining delivered accurate predictions with limited test cases — critical for diverse clinical documentation
Patricia Lasserre, PhD, University of British Columbia
THE CHALLENGE

Why Your Patient Data Is Incomplete

Even in modern EHRs, critical patient information is buried in unstructured clinical notes and inaccessible.
Structured Fields Are Dangerously Incomplete

Even the data you trust from structured EHR fields has critical gaps that silently compromise research quality — disease severity (NYHA class, tumor grade), treatment response (adherence, effectiveness), functional status (ECOG score, mobility), etc.

Most Clinical Context Exists Only in Notes

The most valuable clinical information—treatment rationale, symptom severity, functional status, patient preferences—exists exclusively in unstructured clinical narratives, never captured in structured fields.

Data Integration Is Prohibitively Complex

Combining data from disparate EHR systems, claims databases, pharmacy records, and lab interfaces results in incompatible formats, duplicate entries, conflicting or inconsistent information, all requiring specialized clinical expertise to resolve.

THE SOLUTION

Your Secondary Use Data Platform:
From Raw to Research-Ready

All clinical data sources unified and mapped to OMOP standard

Unify All Data Sources

  • Multi-Modal Data Ingestion – Clinical notes, lab reports, imaging studies, claims data, HL7 messages, FHIR, JSON, CSV and structured EHR exports. Single pipeline.
  • Standardize to OMOP – Disparate sources become OMOP CDM format. Ready for analytics and research.
  • Eliminate Integration Complexity – Unify fragmented systems into one coherent clinical data warehouse within your infrastructure

Extract Hidden Clinical Intelligence

  • Clinical NLP Extraction – Identify and extract 400+ medical entities from unstructured text: diagnoses, medications, procedures, lab results, clinical measurements, etc.
  • Capture Clinical Context – Temporal relationships, severity indicators, negations, patient-specific context that structured fields never capture.
  • Map to Standard Terminologies – Automatic mapping to SNOMED-CT, ICD-10, RxNorm, LOINC, etc.

Reasoning, Confidence, and Provenance Tracking

  • Automatic Conflict Resolution – Reconcile contradictory information across EHR systems, notes, and external sources using clinical reasoning, while keeping track of supporting and contradictory evidence.
  • Confidence Scoring – Every extracted fact includes a confidence level. Know what’s reliable.
  • Complete Provenance – Trace every clinical fact to source document, line number, and extraction timestamp. Full audit trail.

Automated Multimodal Data De-Identification

  • Multi-Format De-Identification – Remove PHI from clinical notes, pathology reports, claims data and structured EHR fields automatically at patient level or document level.
  • DICOM Image De-Identification – Strip burned-in PHI from medical images while preserving diagnostic quality and DICOM metadata integrity.
  • Validated 99%+ Accuracy – Independently tested by Providence Health against human expert reviewers for HIPAA-compliant de-identification.
THE LEAP FORWARD

Prebuilt AI Agents

Ready to use powerful agents and applications on top of your unified, OMOP-standardized data foundation

Intelligent Cohort Creation

  • Natural Language Queries – Ask questions in plain English across all clinical data sources: “Show me diabetes patients who started metformin in 2023 with HbA1c > 8”
  • Complex Inclusion/Exclusion Criteria – Define sophisticated eligibility rules combining diagnoses, medications, lab values, procedures, and clinical notes context
  • Real-Time Results -Identify trial candidates in minutes instead of months

Complete Patient Timelines

  • Longitudinal Clinical History – Automatically construct complete timelines spanning years of care across all encounters and data sources
  • Event Timeline Visualization – Plot diagnoses, medications, procedures, lab trends, and outcomes chronologically for instant pattern recognition
  • Clinical Context at Every Point – Understand treatment rationale, response patterns, and outcome trajectories over time

Clinical Measures

  • Pre-Built Clinical Calculations – Automatic computation for validated clinical measures including BMI, A-a O2 Gradient, BSA, Calcium Correction, CKD-EPI, HOMA-IR, MAP, Osmolality, and dozens more
  • Automated Quality Metrics – Compute HEDIS, CMS quality measures, and research endpoints automatically from your unified OMOP data
  • Custom Measure Builder – Define your own clinical calculations and quality indicators using simple formulas or complex logic—all automatically computed across your patient population

Build Your Own AI Agents

Use the same high-quality, standardized data foundation that powers John Snow Labs’s AI agents to create your own specialized clinical AI applications
Build Like We Do

Use the same high-quality, standardized data foundation that powers our platform's AI agents to create your own specialized clinical AI applications

Direct Access to Curated Data

Query your complete, de-identified, conflict-resolved, confidence-scored clinical data warehouse using standard SQL against OMOP CDM tables

Your Infrastructure, Your Control

All data and agents remain within your security perimeter with your access controls, audit logs, and compliance policies intact

Build Enterprise Ready Agents

Uses open standards, integrates with major cloud providers, and supports your agentic framework of choice for rapid production deployment

Recognized for Innovation & Impact

John Snow Labs is trusted and recognized by industry leaders and independent organizations.
2025
ACQ5 Global Awards 2025
ACQ5 Global Awards 2025
2025
Best Healthcare AI Application
Global 100 Award
2025
Best Use of AI for Healthcare
AI Awards 2025

Enterprise Grade Security & Compliance

HIPAA & GDPR compliant. Built for regulated healthcare environments.
Regulatory Grade De-Identification

Automated PHI removal at 99%+ accuracy confirmed by independent health system testing. Expert determination

On-Premises or on Your Private Cloud

Deploy within your security perimeter. PHI never leaves your infrastructure. No external API calls to third-party services.

Strong Security Controls

Built for regulatory compliance from day one. Supports all HIPAA requirements and controls for storing and processing clinical data.

AI Governance Ready

Performance stays consistent as data volumes expand — from thousands to millions of patients.

Learn More

See Patient Journey Intelligence in action with real use cases

Try It on Your Own Data

Run our platform in your own environment. Test it with your real data, evaluate performance, and customize it to your needs — all without exposing sensitive information.

preloader