Building Clinical AI Agents for Secondary Use
When people talk about AI agents in healthcare, they usually mean agents for primary use: ordering labs, confirming appointments, answering patient portal questions, or ambient documentation during visits. These agents operate in real-time clinical workflows and interact directly with patients and providers.
This page is about something different: AI agents for secondary use. These agents work behind the scenes on clinical data that has already been collected. They power oncology surveillance, risk adjustment, clinical trial matching, patient registries, and external control arms. They don't talk to patients or place orders. Instead, they analyze millions of clinical records to find patterns, extract insights, and generate evidence for research, quality measurement, and regulatory submissions.
Secondary use agents are where healthcare organizations can have the greatest impact. They can automate registry abstraction that currently takes teams of nurses months to complete. They can identify patients eligible for clinical trials who would otherwise be missed. They can detect safety signals buried in clinical notes that never appear in structured data.
The technology to build these agents exists. Large language models can reason about clinical scenarios, extract information from unstructured notes, and answer complex questions about patient histories. But even with standardized data models like OMOP and modern analytics stacks, most teams quickly encounter the same challenge:
Clean data alone is not enough to build regulatory-grade clinical AI.
This is what we call the AI Readiness Gap: the distance between having good data and having a platform that can support production-ready clinical AI agents with the accuracy, provenance, and compliance that healthcare demands.
AI Agents Every Health Organization Wants
Across life sciences, providers, and regulators, AI initiatives tend to converge on a common set of applications. Research teams want to build patient registries that automatically abstract hundreds of data elements from clinical documentation. Pharmaceutical companies need external control arms and synthetic cohorts for regulatory submissions. Health systems want surveillance systems that detect adverse events, track outcomes, and identify at-risk populations before problems escalate.
Automate Patient Registry Abstraction
Extract hundreds of data elements from clinical notes, pathology reports, and imaging studies into longitudinal disease registries, eliminating manual chart review.
Generate External Control Arms for Regulatory Submissions
Match historical real-world patients to clinical trial populations with full provenance, creating synthetic cohorts that meet FDA evidence standards.
Detect Adverse Events and Safety Signals
Monitor clinical documentation in real-time for drug interactions, complications, and safety signals that claims data systematically misses.
Capture Risk Adjustment from Clinical Narratives
Extract HCC codes and diagnoses from unstructured notes that never made it to problem lists, improving risk stratification and revenue accuracy.
Build Complete Patient Journeys with Evidence
Build longitudinal timelines that show exactly what happened to each patient, when, with drill-down to the source documents that prove it.
Match Patients to Clinical Trials
Screen patients against complex eligibility criteria, including temporal logic and unstructured data requirements, with automated evidence for each match.
These aren't speculative use cases. They're the projects that clinical informatics teams, data science groups, and IT leadership discuss in every strategic planning session. The question isn't whether organizations want to build them. The question is whether they have the infrastructure to build them well.
Each of these looks different on the surface: different user interfaces, different regulatory requirements, different clinical domains. But under the hood, they all require the same foundational capabilities: complete patient data, standardized terminologies, provenance tracking, confidence scoring, and explainable outputs. Organizations that recognize this pattern can build infrastructure once and deploy it across every use case. Organizations that don't recognize it end up rebuilding the same capabilities over and over.
The Clinical AI Challenges
Most healthcare organizations attempting to build AI agents face a harsh reality: the technology to create intelligent assistants exists, but the infrastructure to deploy them safely, compliantly, and effectively does not.
Consider what it takes to build a single clinical AI agent, say, one that identifies patients eligible for a clinical trial. You need access to complete patient records, which means integrating data from multiple systems. You need that data normalized to standard terminologies so the agent can reason across patients consistently. You need provenance tracking so clinicians can verify why a patient was flagged. You need security controls that meet HIPAA requirements. You need audit trails for regulatory compliance. And you need all of this before you write a single line of clinical logic.
Data Fragmentation
Clinical data lives in dozens of systems: EHR structured fields, unstructured narratives in notes, coded claims, imaging reports. Building a single agent requires integrating all of these, often with incompatible formats and terminologies.
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Repeated Infrastructure Work
Organizations spend 80% of AI development effort on data preparation, security controls, and compliance infrastructure. This work gets repeated for every new use case instead of building on a shared foundation.
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FDA Guidance for Real-World Data
Regulators won't approve systems that can't explain their outputs. Clinicians won't act on recommendations they can't verify. Every clinical AI decision must be traceable to source data with clear provenance.
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No Standardized APIs
Every clinical data source exposes different interfaces, authentication methods, and query languages. Without standardized APIs, agents must be custom-built for each data source, making them brittle and expensive to maintain.
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Security and Privacy
Healthcare AI handles the most sensitive data that exists: protected health information. A single breach can result in millions in fines, loss of patient trust, and regulatory action. General-purpose AI tools weren't built for this.
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Building vs. Buying Agents
Organizations face a choice: build custom agents from scratch or adapt pre-built solutions. Most need both, but lack the infrastructure to support either. Custom agents require months of development; pre-built agents require customization.
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Now multiply that by every AI application your organization wants to build. Each one faces the same infrastructure requirements. Each one needs the same foundational capabilities. Without a shared platform, teams end up solving these problems independently, often inconsistently, and always at great cost.
Why Building Separate Systems for Each Use Case Fails
Many organizations attempt to build each AI application independently. A custom pipeline for registries. A separate workflow for surveillance. Another system for regulatory submissions. This approach seems logical, each use case has unique requirements, so build a unique solution.
But it leads to predictable problems that compound over time (read more here). The registry team extracts diagnoses one way. The surveillance team extracts them another way. When both systems analyze the same patient population, they produce different results, and no one can explain why. Worse, when regulators ask for provenance, the registry team can trace their results but the surveillance team cannot, because they used different data pipelines with different logging infrastructure.
This isn't a hypothetical failure mode. It's the lived experience of most healthcare organizations that have attempted to scale clinical AI beyond pilot projects. The pattern repeats across institutions, across use cases, and across technology stacks.
The Point Solution Trap
- Multiple applications produce conflicting patient facts
- Clinical logic gets re-implemented inconsistently across systems
- Provenance chains break at system boundaries
- Regulatory explanations require manual reconstruction
- Security and compliance controls vary by application
- Every new use case starts from zero
The Platform Foundation Approach
- Single source of truth for all patient data
- Shared definitions ensure consistent clinical logic
- End-to-end provenance preserved automatically
- Built-in explainability for every extracted fact
- Uniform security and compliance across applications
- New use cases inherit existing infrastructure
Consider a concrete example: a health system builds a registry for lung cancer patients. Six months later, they need to build a clinical trial matching system for the same population. Without a shared platform, the trial matching team must re-extract patient data, re-implement cancer staging logic, and re-build provenance tracking. When the two systems inevitably disagree about which patients have Stage III disease, no one can explain why, because the logic lives in different codebases with different assumptions.
This approach does not scale. And critically, it does not meet modern regulatory expectations for transparency and reproducibility.
FDA Requirements for Real-World Evidence: Data Accuracy and Full Provenance
The regulatory landscape for Real-World Evidence (RWE) has evolved rapidly over the past five years. The U.S. Food and Drug Administration has issued clear guidance on what's required for medical devices and regulatory decision-making using real-world data. Organizations hoping to use clinical AI for regulatory purposes need to understand two non-negotiable expectations.
Capture Complete Clinical Information, Not Just Claims Data
Claims data alone is insufficient for regulatory-grade evidence. Critical clinical signals like diagnoses, outcomes, adverse events, and treatment response are often missing or incomplete in structured fields. The FDA's guidance on Real-World Evidence to Support Regulatory Decision-Making for Medical Devices emphasizes that data quality and completeness are foundational requirements.
This isn't abstract guidance. It means that cohorts built from claims data will be rejected if critical clinical details exist in unstructured notes that weren't captured. It means that registries missing 40% of diagnoses will not meet regulatory scrutiny. It means that safety surveillance systems that rely only on structured ICD codes will miss the adverse events documented in clinical narratives.
FDA Data Quality Requirements for Real-World Evidence
Real-world data used for regulatory submissions must demonstrate:
- Relevance: Data elements capture the clinical concepts needed for the regulatory question
- Reliability: Data is collected consistently and accurately across sources
- Completeness: Critical information is not systematically missing
- Traceability: Each data point can be traced to its source
Claims data and structured EHR fields often fail these tests because 40%+ of clinical information exists only in unstructured notes.
Trace Every Result Back to Its Source Documentation
When submitting evidence to regulators, aggregates are not enough. Every number must be explainable, reproducible, and auditable. Regulators must be able to trace results back to:
- Individual patients included in cohorts
- Source documents supporting each clinical fact
- Dates, forms, and clinical context
- Transformations and versions used in processing
This requirement fundamentally changes what's needed from clinical AI systems. A model that produces accurate predictions but can't explain its inputs is insufficient. A cohort builder that identifies patients but can't cite source documentation is insufficient. A registry that extracts data elements but can't trace them to original notes is insufficient.
The days when healthcare AI could operate as a black box are ending. Regulatory expectations now require the same transparency and reproducibility that traditional clinical research has always demanded, now applied to AI-assisted workflows.
Regulatory Expectations Are Increasing, Not Decreasing
As noted in December 2025 final FDA guidance, the regulatory expectations for RWE are becoming more stringent. Organizations that cannot demonstrate data accuracy, provenance, and reproducibility will face increasing challenges in regulatory submissions. Building these capabilities after the fact is far more expensive than designing them in from the start.
Meeting these expectations requires more than analytics tools. It requires a platform designed from the ground up for regulatory-grade clinical AI.
Infrastructure Blocker for Production Ready Clinical AI Agents
Even with high-quality OMOP data, teams building clinical AI agents still need to solve three fundamental infrastructure challenges. These aren't optional nice-to-haves. They're blockers that prevent pilot projects from becoming production systems.
1. Standardized APIs and Agent Infrastructure for Clinical Data
Building reliable AI agents requires more than access to data. Agents need to interact with terminology services, cohort builders, clinical measures, patient timelines, and document repositories. Without standardized interfaces, every agent becomes a custom integration project, and every integration becomes a maintenance burden.
The challenge isn't technical complexity. Modern software engineering can build integrations to anything. The challenge is that healthcare organizations need to build many agents over time, and each one shouldn't require months of integration work. When a new agent needs access to patient timelines, it should call a standard API, not reverse-engineer database schemas. When an agent needs to look up SNOMED CT codes, it should use a terminology service, not embed clinical knowledge in application code.
Expose Consistent APIs Across All Platform Capabilities
Provide standardized interfaces for patient data, cohort operations, terminology lookups, clinical NLP, and document access. Agents connect once and work everywhere.
Support Open Standards (MCP) for AI Agent Interoperability
Implement Model Context Protocol (MCP) so agents can dynamically discover available tools, use standardized schemas, and chain operations without hardcoded integrations.
Without this infrastructure, every AI project becomes a bespoke engineering effort. Teams spend months on integration plumbing before they can focus on clinical logic. And when they finally ship, maintenance costs grow linearly with the number of applications.
2. Enterprise Security and PHI Protection for AI Models Training
Clinical AI operates on protected health information (PHI), the most regulated data category in healthcare. General-purpose AI platforms weren't designed for this environment. They assume data can flow to cloud services, that third-party APIs are acceptable, and that security can be added later. None of these assumptions hold for production healthcare AI.
The security challenge isn't theoretical. A single PHI breach can trigger regulatory investigation, mandatory disclosure to affected patients, potential fines in the millions, and most importantly, loss of patient trust that took decades to build. Healthcare organizations cannot accept "good enough" security for AI systems that touch patient data.
Production clinical AI requires:
- Private, Secure Deployment: The entire platform, including AI models, must run within institutional infrastructure. PHI cannot flow to external services or shared cloud environments. Air-gapped deployments must be fully supported.
- Fine-Grained Access Control: Different users, roles, and agents need different permissions. A clinical trial coordinator should see eligibility-relevant data without access to billing or mental health records. Access policies must be enforceable at the data level, not just the application level.
- Comprehensive Audit Logging: Every data access, tool invocation, and agent action must be logged with user identity, timestamp, and context for compliance review. Audit logs must be tamper-evident and retained according to institutional policy.
- Local Model Inference: Medical LLMs and VLMs must run locally. No PHI can be transmitted to third-party model providers. This isn't a preference; it's a compliance requirement for most healthcare organizations.
Security Is a Foundation, Not a Feature
A single PHI breach can result in millions in fines, loss of patient trust, and regulatory action. Healthcare AI platforms must be designed for HIPAA, GDPR, and institutional policy compliance from the foundation, not retrofitted after deployment. Organizations that try to add security later find that it requires re-architecting systems that are already in production.
3. Provenance, Versioning, and Explainability for Every AI Generated Result
Regulatory-grade AI requires built-in governance. When a regulator, clinician, or auditor asks "how did you get this result?", the answer must be complete, verifiable, and reproducible. This isn't about generating documentation after the fact. It's about designing systems that capture provenance automatically as data flows through them.
The provenance requirement touches every layer of clinical AI infrastructure. When a patient is included in a cohort, the system must record which criteria matched and which source documents supported each criterion. When a clinical fact is extracted from a note, the system must record the extraction method, model version, confidence score, and precise text location. When an AI agent makes a recommendation, the system must capture the reasoning chain and evidence that led to that recommendation.
Capture Source Document Origin
Record the original clinical note, lab report, or EHR field where each fact was documented, including document type, date, and author.
Track Extraction Method and Model Version
Document whether each fact came from structured import or NLP extraction, which model version was used, and what confidence score was assigned.
Log All Transformation Steps
Record terminology normalization, OMOP mapping, confidence adjustments, and quality corrections so any transformation can be audited.
Preserve Agent Reasoning Chains
When AI agents use clinical facts, capture the full reasoning chain: which facts were retrieved, how they were combined, and what decision logic was applied.
This provenance chain must support:
- Link Every Clinical Fact to Its Source Document: Provide precise location (document, section, sentence) for every extracted fact, enabling one-click verification by reviewers.
- Model Versioning for Reproducibility: Enable exact recreation of results from any point in time, supporting regulatory submission and retrospective audit.
- Quantify and Communicate Uncertainty: Not all extracted facts are equally certain. Provide confidence scores that help users understand which results require human verification.
- Cite Evidence When Agents Make Recommendations: When AI agents suggest actions or answer questions, require explicit citation of the clinical facts and reasoning that support each response.
This is not optional. It is foundational to regulatory-grade clinical AI. Organizations that try to add provenance after building their AI systems find that the architecture doesn't support it.
Close the AI Readiness Gap with Purpose-Built Platform Infrastructure
Solving the AI Readiness Gap manually means building and maintaining all of the above (standardized APIs, enterprise security, and comprehensive provenance) application by application, use case by use case. Most organizations don't have the engineering capacity, clinical informatics expertise, or regulatory experience to do this well. And even those that do would rather invest their resources in clinical AI applications than infrastructure plumbing.
Patient Journey Intelligence by John Snow Labs takes a different approach. Instead of asking every organization to solve the same infrastructure problems independently, the platform provides a shared foundation purpose-built for clinical AI. Organizations get production-ready capabilities on day one, then build applications on top of them.
The platform starts by ingesting and integrating all available multimodal data into a unified OMOP database, including structured EHR fields, unstructured clinical notes, scanned documents, lab results, imaging metadata, and claims. From there, it exposes this data through standardized MCP-compatible APIs that any LLM can use, and ships with pre-built agents for common use cases like patient journey visualization, cohort building, and registry abstraction. Your clinical AI applications start with complete, standardized patient data and proven infrastructure rather than months of data engineering.
Complete Patient Journeys
Query complete longitudinal timelines constructed from multimodal data (structured, unstructured, and multimodal) with full provenance preserved automatically.
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Production-Ready Disease Registries
Launch automated registry abstraction for cancer, cardiovascular, and custom disease registries with human-in-the-loop review and regulatory compliance built in.
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Pre-Built Clinical AI Agents
Start with working agents for cohort building, trial matching, patient co-pilot, and more. Customize them for institutional requirements.
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Built-In Governance and Compliance
Get security, audit trails, provenance tracking, and explainability designed into every layer, without building governance infrastructure from scratch.
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Whether you're building a research prototype, a clinical registry, or regulatory-grade evidence for FDA submission, the same platform foundations apply. New applications inherit the data accuracy, provenance tracking, security controls, and compliance infrastructure that already exist. Teams focus on clinical logic and user experience instead of data engineering and security architecture.
Access Platform Tools Through Model Context Protocol (MCP)
Patient Journey Intelligence provides the infrastructure for both pre-built agents and custom clinical AI applications. All platform capabilities are exposed through the Model Context Protocol (MCP), an open standard for AI agent tool access. This means agents can dynamically discover available tools, use standardized schemas, and chain operations without custom integrations.
The practical impact is that teams building clinical AI agents spend their time on clinical logic (eligibility criteria, extraction rules, decision thresholds) rather than integration plumbing. When a new capability is added to the platform, existing agents can use it without code changes.
| Tool Category | Capabilities Available to Agents |
|---|---|
| Patient Data | Query demographics, conditions, medications, procedures, labs, and visits across OMOP CDM with full provenance |
| Cohort Operations | Build cohorts with inclusion/exclusion criteria, temporal logic, and unstructured data filters |
| Terminology Services | Look up SNOMED CT, RxNorm, LOINC, ICD-10 codes; navigate concept hierarchies; map between vocabularies |
| Clinical NLP | Extract entities, relations, and assertions from clinical text; detect negation, uncertainty, and clinical context |
| Clinical Measures | Compute standardized measures (BMI, eGFR, MELD, CHA₂DS₂-VASc) and custom organizational calculations |
| Document Access | Retrieve clinical notes, reports, and documents with full provenance metadata and precise text locations |
Deploy Pre-Built Clinical AI Agents
Building custom agents from scratch takes time. You need to design workflows, integrate data sources, handle edge cases, and validate outputs. For many common use cases, this work has already been done.
The platform ships with production-ready agents that address the challenges most healthcare organizations face. A research coordinator trying to identify patients for a clinical trial doesn't need to build a matching algorithm from scratch. A tumor registrar abstracting cancer cases doesn't need to train a custom NLP model. A quality officer tracking outcomes across a population doesn't need to write SQL queries against raw EHR tables.
Visualize Complete Patient Journeys
Explore interactive timelines that assemble longitudinal patient histories from all data sources, with drill-down to source documents for every clinical fact.
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Build Cohorts Without Writing Code
Define patient populations using visual interfaces for diagnoses, procedures, medications, labs, temporal logic, and clinical context. Full provenance for every match.
Build Cohorts →
Query Patients and Populations Conversationally
Ask questions about individual patients or populations and receive evidence-grounded responses with confidence scores and citations to source documentation.
Try Clinical Co-Pilot →
Automate Registry Abstraction Across Cancer Sites
Deploy automated abstraction for all cancer sites with NAACCR compliance, human-in-the-loop review workflows, and complete audit trails.
Explore Registries →
Each pre-built agent connects to the same unified data layer and MCP infrastructure, so governance, provenance tracking, and audit capabilities work consistently across all applications. You can use them as-is, customize them for institutional requirements, or use them as starting points for building something entirely new.
Build Custom Agents Using Three Access Patterns
Beyond pre-built agents, Patient Journey Intelligence provides the infrastructure for building custom clinical AI applications. Organizations can configure custom agents to connect to the platform through whichever interface best fits their use case. All three access patterns provide the same underlying data with the same provenance and security controls.
Connect Agents via MCP Endpoints
Use agent-friendly interfaces optimized for natural language interaction. Ideal for conversational agents, clinical decision support, and LLM-based applications.
Integrate Applications via REST APIs
Call programmatic endpoints for integration with existing applications, EHR workflows, analytics platforms, and custom software systems.
Query OMOP Datasets via Direct SQL
Access AI-ready OMOP datasets directly for ML model training, BI dashboards, custom analytics, and integration with existing data science tooling.
Custom agents automatically inherit the platform's security infrastructure, compliance controls, provenance tracking, and AI-ready data foundation. Teams building custom applications don't need to re-implement any of these capabilities.
Provenance, Versioning, and Explainability
Clinical AI agents must be trustworthy. Clinicians won't act on recommendations they can't verify, and regulators won't approve systems that can't explain their outputs.
Full Provenance Tracking
Every clinical fact in Patient Journey Intelligence includes complete lineage:
Source Document
Original clinical note, lab report, or EHR field where the fact was documented
Extraction Method
Whether the fact came from structured data import or NLP extraction, with model version and parameters
Transformation Steps
Terminology normalization, OMOP mapping, and any data quality corrections applied
Agent Reasoning
When an AI agent uses this fact, the reasoning chain and decision logic are captured
This provenance chain means every AI output can be traced back to its source documentation. When an agent recommends a patient for a clinical trial, reviewers can see exactly which diagnoses, labs, and medications qualified them, with links to the original clinical notes.
Confidence Scoring
Not all extracted facts are equally certain. The platform attaches confidence scores to every clinical concept:
- High Confidence (>0.95): Explicitly stated, unambiguous clinical facts suitable for automated workflows
- Medium Confidence (0.80–0.95): Likely correct but may benefit from human verification for high-stakes decisions
- Low Confidence (<0.80): Flagged for mandatory human review before clinical use
Agents can filter by confidence level, ensuring automated workflows only act on high-certainty data while flagging uncertain cases for human review.
Versioning and Reproducibility
Clinical AI must be reproducible. Regulatory submissions, research publications, and quality audits require the ability to recreate exact results:
- Time-Travel Queries: Query patient data as it existed at any historical point in time
- Cohort Versioning: Save and restore exact cohort definitions and membership lists
- Model Versioning: Track which NLP model versions extracted each fact
- Audit Snapshots: Create point-in-time snapshots for regulatory submission or research publication
Explainable Outputs
When AI agents generate recommendations or findings, they include supporting evidence:
- Evidence Citations: Direct links to source documents supporting each conclusion
- Reasoning Chains: Step-by-step explanation of how the agent reached its conclusion
- Alternative Interpretations: When data is ambiguous, surface competing interpretations for human resolution
- Uncertainty Flags: Clearly indicate when confidence is low or information is missing
Summary: The Platform Requirements Checklist
Deploying clinical AI agents in production requires infrastructure that general-purpose AI platforms don't provide. Patient Journey Intelligence delivers purpose-built capabilities across four critical dimensions:
| Requirement | What's Needed | How Patient Journey Intelligence Delivers |
|---|---|---|
| Standardized APIs | Consistent tool access across data, terminology, cohorts, measures | Model Context Protocol (MCP) with pre-built medical tools |
| Security & Privacy | PHI protection, access control, private runtime | Enterprise auth, RBAC, on-premises deployment, local model inference |
| Provenance & Explainability | Traceable outputs, confidence scoring, reproducibility | Full lineage tracking, versioning, evidence citations |
| Pre-built Agents | Immediate value, reference implementations | Production-ready agents for common clinical workflows |
With these requirements met, organizations can focus on what matters: building AI agents that improve patient care, accelerate research, and streamline operations.
Transform AI Readiness into Regulatory Readiness
The AI Readiness Gap is not just a technical challenge. It is a regulatory one. As FDA and EU guidance continues to evolve, successful organizations will be those that can demonstrate compliance while moving faster than competitors. The organizations that built strong infrastructure early will have sustainable advantages over those that tried to retrofit compliance later.
Prove Data Accuracy Beyond Claims
Capture the 40%+ of clinical information that exists only in unstructured notes. Demonstrate complete multimodal integration that satisfies FDA data quality requirements.
Trace Every Result to Source Documents
Link every cohort member, every extracted fact, and every AI recommendation back to source documents with precise citations and confidence scores.
Reproduce Any Analysis at Any Point in Time
Recreate exact results using versioned models, and patient cohorts. Support regulatory submission, retrospective audit, and scientific reproducibility.
Submit Regulatory Evidence with Confidence
Deliver regulatory-grade real-world evidence with the provenance, transparency, and audit trails that FDA and EMA expect, built into every workflow.
Closing this gap requires more than tools. It requires a purpose-built clinical AI platform that was designed for regulatory-grade healthcare applications from the beginning.
The Bottom Line
Healthcare organizations are ready to build clinical AI agents. The technology exists. But deploying AI that meets regulatory expectations for data accuracy, provenance, security, and explainability requires platform infrastructure that most organizations don't have and shouldn't need to build from scratch.
Patient Journey Intelligence provides that infrastructure: pre-built patient journeys, production-ready agents, MCP-based tool access, and governance built into every layer. Build clinical AI applications on a foundation designed for healthcare, and meet regulatory expectations from day one.
FAQ
The AI Readiness Gap is the distance between having clean data and having a platform that can support production clinical AI agents with regulatory-grade accuracy, provenance, and compliance. Even with OMOP-standardized data, organizations still need standardized AI APIs, security infrastructure for PHI, comprehensive provenance tracking, and pre-built agent capabilities. These are requirements that most data platforms don't provide.
FDA guidance on Real-World Evidence requires data quality (relevance, reliability, completeness), full traceability from results to source documents, and the ability to reproduce analyses. Claims data alone is typically insufficient because 40%+ of clinical information exists only in unstructured notes. Patient Journey Intelligence addresses these requirements with multimodal data integration, complete provenance tracking, and versioned reproducibility.
Point solutions (building separate systems for registries, surveillance, trial matching, etc.) fail because they create inconsistent patient data, duplicated logic, broken provenance chains, and manual regulatory documentation. When two systems disagree on patient facts, no one can explain why. Patient Journey Intelligence provides a shared platform foundation that all applications build on, ensuring consistency and traceability.
Model Context Protocol (MCP) is an open standard for AI agent tool access. Patient Journey Intelligence exposes all platform capabilities through MCP, enabling agents to dynamically discover tools, use standardized schemas, and chain operations without custom integrations. This means teams focus on clinical logic rather than integration plumbing, and new agents automatically work with existing infrastructure.
The platform deploys entirely within institutional infrastructure: on-premises, private cloud, or air-gapped environments. All medical LLMs run locally without transmitting PHI to external services. Fine-grained access controls, comprehensive audit logging, and encryption (AES-256 at rest, TLS 1.3 in transit) ensure HIPAA and GDPR compliance. Security is designed into the platform foundation, not bolted on afterward.
Every clinical fact includes complete lineage: the original source document, extraction method and model version, transformation steps (terminology normalization, OMOP mapping), and agent reasoning when AI uses the fact. Results can be traced back to individual patients, specific documents, and precise text locations. Versioned snapshots enable exact reproduction for regulatory submission or audit.
The platform includes Patient Journey Viewer for longitudinal timeline visualization, Cohort Builder for no-code population definition with temporal logic, Clinical Co-Pilot for evidence-grounded conversational queries, and Patient Registry for automated abstraction across all cancer sites with NAACCR compliance. Each agent includes security controls, audit logging, and provenance tracking built in.
The platform supports patient registries with automated abstraction, external control arms for regulatory submissions, safety and outcomes surveillance, risk adjustment and HCC coding, patient journey analysis with explainable evidence, and clinical trial matching with temporal eligibility logic. All use cases share the same patient data foundation, ensuring consistency across applications.
Custom agents can access platform capabilities through MCP endpoints (for conversational agents), REST APIs (for integration with existing applications), or direct SQL (for ML training and analytics). All custom agents automatically inherit the platform's AI-ready data, security infrastructure, compliance controls, and provenance tracking without additional development.
Clinical data warehouses store structured data but don't provide AI agent infrastructure, provenance tracking, clinical NLP, or pre-built agents. Patient Journey Intelligence is a complete platform for clinical AI: multimodal data integration, MCP-based tool access, production-ready agents, built-in governance, and regulatory-grade provenance. It's designed for building AI applications, not just storing and querying data.
The platform provides the data accuracy, provenance, and reproducibility that FDA guidance requires for Real-World Evidence. Complete lineage tracking links every result to source documents. Versioned cohorts and snapshots enable exact reproduction. Confidence scores and explainable outputs support regulatory review. Organizations can submit RWE with full transparency and audit trails.
Patient Journey Intelligence is designed for life sciences companies building regulatory-grade evidence, health systems deploying clinical AI agents, research organizations automating registries and cohorts, and any organization that needs production clinical AI with PHI security, provenance tracking, and regulatory compliance. It's built for teams that need more than prototype-level AI tooling.