Patient Registry Automation
Patient registries are the infrastructure behind cancer surveillance, quality improvement, regulatory compliance, and clinical research. Building one takes months of manual chart abstraction by certified staff who extract hundreds of data points per patient from fragmented, multimodal documentation.
Patient Journey Intelligence automates that abstraction. Multimodal AI, oncology-specific agentic workflows, and structured human oversight produce a registry that updates daily and carries expert-level accuracy at scale, with every value traceable to the document it came from.
What is a patient registry?
A patient registry is a systematic, population-level collection of standardized data about patients with a specific condition, treatment exposure, or clinical characteristic. Registries capture demographics, diagnosis details, disease characteristics such as tumor morphology, behavior, and TNM staging, treatment protocols, and long-term outcomes.
Registries power several functions across the healthcare system:
- Public health policy and resource allocation: monitoring cancer incidence, survival trends, and population-level outcomes over time
- Clinical research cohort infrastructure: identifying and following patient populations for retrospective studies and clinical trials
- Quality of care measurement: benchmarking institutional performance against national standards and peer institutions
- Regulatory reporting and accreditation: meeting mandatory reporting requirements to state registries, national programs, and accreditation bodies
- International comparison of care effectiveness: enabling cross-institutional and cross-national analysis of treatment patterns and outcomes
What registries can and cannot answer
A well-maintained registry answers questions about disease frequency (which cancers occur most often, and how they are distributed by age and gender), epidemiological trends (how incidence is changing, and whether certain cancers are more common in specific regions), health risks (survival rates, risk of recurrence, long-term complications), and quality of care (regional variation and institutional benchmarking).
Traditional registries carry one structural limitation: a typical 12 to 24 month delay from diagnosis to national reporting. That lag is what keeps a conventional registry out of active patient care:
| Capability | Traditional Registry | Patient Journey Intelligence |
|---|---|---|
| Clinical decision support | ✗ | ✓ |
| Clinical trial matching | ✗ | ✓ |
| Guideline adherence monitoring | ✗ | ✓ |
| Active care coordination | ✗ | ✓ |
| Precision medicine interventions | ✗ | ✓ |
| Early intervention for at-risk patients | ✗ | ✓ |
| Immediate monitoring of clinical events | ✗ | ✓ |
Patient Journey Intelligence closes that gap with near-real-time registry data, which is what makes all seven clinical use cases above available.
Why registry abstraction is slow and under-resourced
The data in traditional registries is accurate. The process that produces it is manual, slow, and structurally under-resourced.
Manual chart abstraction takes hours per case
Registry abstraction requires trained staff to review clinical charts and extract standardized data elements by hand. Published measurements of the time involved:
- Trauma registries: 70-90 minutes per chart on average, with complex cases taking up to 2 hours (ImageTrend TRRR study)
- Cardiovascular registries: up to 1 hour per chart for experienced abstractors working with ACC-NCDR registries such as CathPCI, which carries over 270 data elements
- Time varies with case complexity, documentation quality, and abstractor experience
Cancer registry abstraction averages roughly 2 hours of manual chart review per case. A typical Oncology Data Specialist (ODS) processes 6 to 8 cases per day against a load of 15,000+ cases per year. The industry standard asks that 90% of cases be reported within 12 months of diagnosis; only 14% of registries meet that NPCR timeliness target, and the average national reporting lag stands at 23 months.
The financial burden compounds the operational one. ODS salaries range from $60,000 to $90,000 annually, and each retiring specialist takes the throughput of roughly three new hires to replace.
Certification requirements limit the talent pool
Registry abstractors need extensive education and ongoing certification:
- CTR (Certified Tumor Registrar): more than 2 years of work experience to sit the NCRA certification exam, plus 20 hours of continuing education every 2 years to maintain certification
- Cardiovascular registries: prefer nurses or staff with 2+ years of cath lab experience
- Limited talent pool: specialized knowledge requirements drive both staffing difficulty and labor cost
The guidelines run to thousands of pages and keep changing
Cancer registry standards are demanding. The NAACCR data dictionary, with AJCC and SEER guidelines, spans 2,500+ pages of rules. The NAACCR data model holds over 750 data fields, 200 to 250 of which a typical adult case requires. AJCC staging system revisions, published every 6 to 8 years, each bring changes to staging criteria, new biomarker requirements, and revised anatomic classifications, and registrars have to master every edition. The 2018 specification changes alone doubled abstracting time per case.
Manual case finding misses cases
- Abstractors rely on diagnosis codes and procedure flags that may be incomplete or delayed
- Cases documented in clinical notes rather than structured fields go undetected
- Patients treated across several facilities may not be captured in all relevant registries
Why naive AI approaches fail at registry abstraction
The intuitive response to these challenges is to feed every document for a patient into a large language model. Five things stop that from working:
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Context window limitations: a 1M-token context window holds roughly 1,000 pages. A single cancer patient's complete record often exceeds 5,000 pages, and the NAACCR, SEER, and AJCC guidelines alone exceed the limit on their own. Model performance also degrades as context length grows, the "context rot" phenomenon, which makes large-window approaches unreliable even where they are technically feasible.
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Regulatory rigor: general-purpose LLMs lack the specialized reasoning layers needed to apply 2,500+ pages of SEER and AJCC coding rules consistently across diverse cancer types and clinical scenarios.
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Multimodal clinical data: a cancer case requires integrating pathology reports with their text and tables, radiology reports, genomic test results, and physician notes. Single-modality models miss the cross-referencing cues that accurate staging depends on.
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Deterministic inference: registry data has to be reproducible, so the same patient record must produce the same abstraction result whenever it is processed. Stochastic LLM outputs fail that requirement.
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Auditability: registries require every data element to be traceable to a specific source document and text span. Regulatory reviewers reject black-box outputs.
Why patient-level reasoning is required
A cancer patient is more than a collection of documents. Combining extracted fields across documents without patient-level reasoning produces structurally incorrect data, for three reasons:
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Temporal conflict: a pre-surgical clinical staging estimate may be superseded by the pathologic stage determined after resection. Without temporal ordering, both values carry equal weight and the result is wrong.
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Assertion revision: a mass flagged on imaging may be biopsied and proven benign, and a preliminary histology may be upgraded after immunohistochemistry. Without assertion tracking, an outdated diagnosis persists beside its correction.
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Deduplication: several documents from one clinical encounter reference the same event. Stacking them creates artificial duplication of evidence, which inflates confidence in an incorrect value.
Patient Journey Intelligence resolves all three through chronological encounter grouping, assertion tracking, and deterministic conflict resolution under NAACCR, SEER, and AJCC rules.
How Patient Journey Intelligence automates registry development
Four integrated layers combine multimodal AI, oncology-specific agentic workflows, and structured human oversight, so abstraction quality and operational scale are available at the same time.
1. Data curation and extraction
Multimodal AI unifies pathology reports, radiology findings, genomic tests, and physician notes into structured, standardized formats. Healthcare-specific small language models (SLMs) deployed locally filter more than 96% of low-relevance documents before abstraction begins, so only clinically relevant documents reach downstream reasoning. Every extracted field carries its source text span, a confidence score, and an extraction timestamp.
2. Patient-level reasoning
Agentic workflows and auto-consolidation reconcile partial or conflicting records in real time under standardized NAACCR, SEER, and AJCC rules. Temporal ordering of encounters, assertion revision tracking, and cross-document deduplication run automatically, which is what turns a registrar's day into review by exception.
3. Registry-specific AI agents
Purpose-built agents select the guideline logic for the registry type and translate guideline rules and flowcharts into executable, deterministic decision graphs, for example for staging, histology, biomarker status, and treatment classification. Results are compiled against standard ontologies including NAACCR, ICD-O-3, and AJCC TNM, so every abstracted value follows the correct coding pathway.
4. Validation and feedback interface
A structured validation interface gives registrars, auditors, and clinical coders the workflow the job requires: supporting and contradictory evidence side by side, complete audit trails, team collaboration, and a learning loop where human corrections improve model performance over time.
Pre-configured registry templates combine automated case finding, AI-powered clinical data extraction, expert review workflows, and regulatory reporting on standardized OMOP CDM data, so an organization gets both the coverage of automation and the judgment of specialized abstractors.
Architecture: accuracy, scale, and governance
The pipeline runs in five stages, designed for regulatory-grade accuracy and data security:
The platform does the data extraction and case identification, and your clinical experts spend their time validating complex cases, resolving ambiguities, and maintaining quality. What reaches a reviewer is AI-extracted data that is already coded, structured, and ready for validation.
From configuration to regulatory submission
The Automated Registry Workflow
Configure the target cohort
Define inclusion and exclusion criteria, required data elements such as your ontology, and quality measures based on registry specifications (NAACCR, ACC-NCDR, custom requirements)
Automated case finding
AI algorithms scan clinical data to identify potential cases from diagnosis codes, procedures, pathology findings, and clinical notes, including the cases manual processes miss
AI-powered data extraction
John Snow Labs Medical Language Models extract the required registry data elements, such as staging, treatment, and outcomes, from structured and unstructured sources with confidence scores and full provenance tracking
Expert review and validation
Clinical experts review AI-extracted data through structured workflows, validate accuracy, and resolve ambiguous cases, with inter-rater reliability tracking on the result
Quality validation and reporting
Run quality checks, generate the required reports, and submit to regulatory bodies in compliant formats (NAACCR, CDC, state registries)
Six capabilities behind the workflow
Automated case finding across every source
AI algorithms scan your clinical data continuously, including EHR fields, clinical notes, pathology reports, and imaging studies, to identify registry cases as they are documented, for 100% case capture without manual chart review.
Extraction from structured and unstructured sources
The platform reads clinical documentation the way an experienced abstractor does, extracting staging, treatment details, outcomes, and quality metrics. Every extraction carries a confidence score and provenance back to the source document.
Expert review built for throughput
Clinical experts review AI-extracted data through structured review forms, consensus workflows for disagreements, and inter-rater reliability tracking, which cuts time spent per case by 80-90%.
Submission-ready regulatory exports
The platform generates exports in the formats regulatory bodies require: NAACCR for cancer registries, CDC for public health surveillance, and state-specific formats for regional compliance. Built-in quality validation catches errors before submission.
Real-time registry quality dashboards
Track case completeness, abstraction timeliness, data accuracy rates, and compliance with reporting requirements. Metrics and audit trails are what you show an auditor.
Continuous automated updates
The platform monitors for new cases and data updates in real time and processes changes incrementally as they arrive, so the registry stays current without batch updates or manual intervention.
Three gaps in clinical data for secondary use
The data accuracy gap
Up to 40% of critical diagnoses exist only in unstructured clinical notes and are never coded into structured EHR fields. Treatment rationale, disease progression, and clinical reasoning are documented as free text, invisible to traditional analytics. Patient Journey Intelligence's Medical Language Models extract that context, so registry work starts from the complete patient picture.
The data engineering gap
Organizations spend 10+ FTE-years annually rebuilding similar data pipelines for each registry, cohort, or AI model, because patient information sits across EHR fields, clinical notes, PDFs, imaging reports, and lab systems with no unified foundation. The Build Once, Use Everywhere pipeline standardizes all of it to OMOP CDM v5.4 once.
The AI readiness gap
FDA guidance on Real-World Evidence requires every clinical AI decision to be traceable to its source document, and claims data and ICD codes alone will not pass that scrutiny. Patient Journey Intelligence preserves full provenance for every extracted fact, covering source document, extraction timestamp, ML confidence score, and transformation lineage, which meets FDA reliability and relevance requirements out of the box.
What automation changes for a registry team
Automating case finding, chart abstraction, and data extraction moves specialized staff from repetitive data entry to quality validation, complex case review, and analysis. The registry that results is more complete, more accurate, and more timely, which is what regulatory compliance, quality measurement, and research capability each depend on.
80-90% reduction in manual effort
Repetitive chart abstraction and data entry are automated. Staff time goes to quality validation and complex cases.
Months to days
Functional registries deploy in days rather than months of manual configuration and testing, because pre-built templates carry the configuration.
100% case capture
Automated scanning across structured and unstructured data catches the cases manual review misses, which improves completeness and reduces penalties for incomplete reporting.
Improved accuracy
AI-assisted extraction with expert validation produces consistent data capture, and provenance tracking with confidence scores makes quality control possible.
Always current
Continuous monitoring keeps the registry up to date without manual intervention. New cases are identified and processed as clinical data arrives.
Available registries
Pre-configured registry templates cover the most common use cases. Each is built on the same automated foundation and tailored to a specific set of regulatory requirements and reporting standards, whether you maintain a cancer registry for state reporting, build research cohorts for clinical trials, or track cardiovascular outcomes for quality measures. Templates bring deployment down from months to days while meeting standards such as NAACCR, ACC-NCDR, and IRB requirements.
De-identified OMOP
Built for secondary use research, with automated de-identification, OMOP CDM standardization, and IRB-ready datasets for retrospective studies and clinical trial feasibility
Learn more →
Cancer Registry
NAACCR-compliant cancer registry with automated case finding, AJCC staging, treatment tracking, and outcomes monitoring, with state and national registry submissions
Learn more →
Cardiovascular Registry
CVD registry for outcomes tracking, quality measures, and research cohorts
Coming soon
Rare Disease Registry
Registry for rare and orphan diseases with longitudinal tracking and natural history studies
Coming soon
Registry applications across healthcare
Registries built on Patient Journey Intelligence support quality measurement, research, public health, and regulatory compliance.
Quality measurement and reporting
Healthcare organizations report quality performance across several frameworks. Registries power those programs through:
- Hospital quality metrics: capture of the standardized data CMS and Joint Commission measures require
- Specialty quality programs: NSQIP for surgical outcomes, STS for cardiac surgery, ACC for cardiovascular care
- Payer quality reporting: HEDIS reports that affect reimbursement and health plan ratings
Research and clinical trials
Clinical research depends on identifying the right patients and collecting longitudinal data on them. Registries support that through:
- Disease-specific cohorts: retrospective study populations that examine treatment patterns and outcomes over time
- Clinical trial feasibility: counts of eligible patients in your population, available during trial planning
- Real-world evidence: RWE that captures outcomes in routine clinical practice alongside traditional clinical trials
Public health surveillance
Public health agencies depend on timely, accurate registry data. Registries serve that through:
- State and national disease registries: monitoring cancer incidence, trauma outcomes, and chronic disease prevalence
- Reportable conditions tracking: alerting authorities to infectious diseases and potential outbreaks
- Outbreak monitoring: identifying case clusters that may indicate an emerging public health threat
Regulatory compliance
Many providers carry mandatory reporting obligations with penalties attached. Registries support compliance through:
- Mandatory reporting requirements: automated submissions for conditions such as cancer, trauma, and stroke that must be reported to state and federal agencies
- Device and implant registries: patient safety tracking and post-market surveillance as the FDA requires
- Safety surveillance: identification of adverse events and unusual patterns that need investigation
Technical foundation
Built on OMOP CDM v5.4
All registry data is stored in standardized OMOP format, which provides:
- Interoperability: compatible with research tools and analytics platforms
- Reproducibility: consistent definitions across institutions
- Flexibility: extensible with custom data elements
- Research-ready: direct integration with cohort building and analytics
AI-powered extraction
Built on John Snow Labs Medical Language Models trained on millions of clinical documents:
- Named entity recognition: extracts conditions, medications, procedures, and findings from unstructured text
- Relation extraction: identifies relationships between clinical concepts, for example that a medication treats a condition
- Assertion detection: captures negation, uncertainty, and temporal context, for example "no evidence of metastasis"
- Document classification: routes documents to the appropriate extraction pipeline by content type
Custom registry development
Alongside the pre-built registries, Patient Journey Intelligence supports custom registry development for:
- Institutional quality improvement initiatives
- Disease-specific research cohorts
- Clinical specialty registries
- Novel outcome tracking programs
Custom development process:
- Requirements gathering: define registry scope, data elements, and workflows
- Case finding logic: configure inclusion and exclusion criteria and algorithms
- Extraction rules: customize data extraction and validation logic
- Review workflows: set up expert review and adjudication processes
- Reporting templates: design the required reports and exports
- Pilot and validation: test with sample cases and validate accuracy
- Production deployment: scale to the full patient population
Need a custom registry?
Contact the Patient Journey Intelligence team to discuss custom registry development for your needs. Most custom registries deploy in 6 to 8 weeks.
Project access and team roles
Registry projects are scoped by membership. Each project has a defined team, and the role a user holds on that project governs their access to its cases and abstraction workspaces.
Roles on a registry project
| Role | What they can do |
|---|---|
| Registry Manager | Manages data source connections and ingestion jobs that feed the registry; was previously called Importer |
| Abstractor | Works on assigned cases, fills fields, and submits cases for QA review |
| Reviewer | Reviews submitted cases, approves or returns them, and can see all cases in the project |
| Viewer | Read-only access to cases and results within the project |
System administrators can view every project across the platform with View all projects. Every other role sees the projects they belong to.
My Projects view
Abstractors and reviewers see a My Projects list on the Patient Registry landing page, holding the projects they are a member of. A registrar or abstractor therefore opens a focused starting point rather than a platform-wide list of projects they have no role in.
System administrators see all projects by default and can switch to the My Projects view.
Deploy your first registry
Launch Your First Registry
Choose the registry type
Select from the available registries or request custom development
Configure parameters
Define date ranges, inclusion criteria, and required data elements
Run initial case finding
Execute automated case identification on historical data
Validate and review
Clinical experts review sample cases to validate accuracy
Enable continuous monitoring
Activate automated updates for ongoing case identification
Roughly 2 hours of manual chart review per case. A typical Oncology Data Specialist processes 6 to 8 cases per day against a load of 15,000+ cases per year. Only 14% of registries meet the NPCR target of reporting 90% of cases within 12 months of diagnosis, and the average national reporting lag is 23 months.
A single cancer patient's record often exceeds 5,000 pages, while a 1M-token context window holds roughly 1,000, and model performance degrades as context grows. Registry coding also requires applying 2,500+ pages of NAACCR, SEER, and AJCC rules deterministically, integrating pathology, radiology, genomic, and note data, reproducing the same result on every run, and tracing every value to a source span. General-purpose LLMs meet none of those requirements on their own.
Patient-level reasoning resolves conflicts across a patient's documents rather than merging extracted fields naively. It handles temporal conflict (a pathologic stage superseding a clinical estimate), assertion revision (a mass later proven benign, or a histology upgraded after immunohistochemistry), and deduplication of documents from the same encounter, using chronological encounter grouping and deterministic conflict resolution under NAACCR, SEER, and AJCC rules.
No. The pipeline runs with no API egress, so PHI stays inside the institutional environment. Local healthcare-specific small language models filter more than 96% of low-relevance documents before abstraction, and registry agents apply deterministic coding rules in the same environment.
Two pre-configured templates are available: De-identified OMOP for secondary use research and the NAACCR-compliant Cancer Registry. Cardiovascular and Rare Disease registries are coming soon. Custom registries can also be developed, and most deploy in 6 to 8 weeks.
Registry Manager (data source connections and ingestion jobs, previously called Importer), Abstractor (works assigned cases and submits them for QA), Reviewer (approves or returns submitted cases and sees all cases in the project), and Viewer (read-only). System administrators can see every project with View all projects; everyone else sees the projects they belong to.
Every extracted field links to its source text span and carries a confidence score and an extraction timestamp, with transformation lineage preserved through to export. This is what meets the FDA Real-World Evidence requirement that a clinical AI decision be traceable to its source document.