Data Readiness for Healthcare AI
Why AI-Ready Patient Journeys Matter
Healthcare AI has reached an inflection point. Large language models can reason about clinical scenarios, computer vision systems interpret medical images, and predictive algorithms identify at-risk patients before symptoms emerge. But there's a critical bottleneck: the data foundation.
Most healthcare organizations spend 80% of their AI development effort on data preparation—cleaning, normalizing, linking records, extracting facts from clinical notes, and resolving conflicts across systems. Worse, this work gets repeated for every new use case: clinical trial matching, risk stratification, registry automation, care gap identification. Teams rebuild similar pipelines over and over, each time losing months to data engineering before delivering value.
Patient Journey Intelligence takes a different approach. Instead of building isolated data pipelines for each AI application, it creates a single, continuously updated foundation of AI-ready patient journeys—complete, multimodal, standardized longitudinal records that every downstream AI agent can trust. Build the data foundation once, innovate endlessly on top.
The Foundation: AI-Ready Patient Journeys
Before exploring how to build AI agents, it's essential to understand what makes patient data truly "AI-ready."
Complete and Multimodal
AI agents need access to the full clinical picture—not just structured fields from the EHR, but also the 40% of critical clinical information buried in unstructured notes, reports, and documents. Patient Journey Intelligence ingests data across all modalities:
- Clinical notes: Progress notes, discharge summaries, pathology reports, radiology interpretations
- Structured EHR data: Problem lists, medication orders, lab results, vitals, procedures
- Scanned documents: Historical records, referral letters, consent forms
- Imaging metadata: DICOM headers with findings and context
- Claims and billing: ICD-10, CPT codes with diagnosis and procedure details
- FHIR resources: Standards-compliant data from interoperable health systems
This multimodal foundation ensures AI agents reason over complete clinical context, not fragmented snapshots.
Standardized and Normalized
Healthcare data arrives in hundreds of local dialects—"MI" at one hospital, "myocardial infarction" at another, "heart attack" in patient portals. AI models trained on one institution's terminology fail when deployed elsewhere.
Patient Journey Intelligence normalizes all clinical facts to OMOP Common Data Model v5.4 with standard medical vocabularies:
- SNOMED CT for conditions, procedures, and clinical findings
- RxNorm for medications and drug ingredients
- LOINC for laboratory tests and measurements
- ICD-10-CM for diagnosis codes
This standardization means AI agents built on one dataset generalize across institutions, and models trained on research cohorts deploy directly into production without rewriting feature engineering logic.
Longitudinal and Temporal
AI agents need to understand how patient conditions evolve over time—not just isolated observations, but sequences, progressions, and temporal relationships. Did the diabetes diagnosis precede the cardiac event? Has the patient's renal function been declining steadily or suddenly?
Patient Journey Intelligence constructs true longitudinal timelines, organizing every clinical event—visits, diagnoses, medications, procedures, lab results—into chronological patient journeys with precise temporal context. Agents can reason about disease progression, treatment response, and care gaps with full awareness of what happened when.
Provenance and Confidence
The most critical requirement for healthcare AI is trustworthiness. Clinicians won't act on AI recommendations without understanding where the information came from and how certain the system is.
Every clinical fact in Patient Journey Intelligence includes:
- Full provenance: Source documents, extraction timestamps, transformation lineage
- Confidence scores: ML model certainty for extracted facts, enabling quality control
- Versioning: Time-stamped updates preserving historical states for reproducibility
- Clinical context: Assertion status (confirmed vs. ruled out), family history flags, temporal qualifiers
This metadata allows AI agents to surface supporting evidence, explain their reasoning, and flag low-confidence outputs for human review—transforming opaque "black box" systems into transparent, auditable decision support tools.
Pre-Built AI Agents: Demonstrating What's Possible
Once AI-ready patient journeys are in place, the platform enables a rich layer of AI agents that operationalize these data assets for clinical, research, and operational use. These agents inherit the same governance, provenance, and auditability as the underlying data—no separate compliance engineering required.
Cohort Builder
A flexible, query-driven agent for identifying patient populations across the full multimodal record. Users define complex inclusion and exclusion criteria combining diagnoses, procedures, medications, labs, temporal constraints, and clinical context extracted from unstructured data.
Why it matters: Traditional cohort queries miss patients whose conditions are documented only in clinical notes. The Cohort Builder searches the complete patient journey—structured and unstructured—producing more complete, accurate cohorts for research, quality improvement, and clinical trials.
Key capabilities:
- Natural language and SQL query interfaces
- Temporal logic (e.g., "diabetes diagnosed before first MI")
- Unstructured data inclusion (e.g., "mentions 'family history of breast cancer'")
- Reproducible, versioned cohort definitions
- Real-time updates as new patient data arrives
Cohorts are reusable across studies, registries, and downstream applications, eliminating redundant cohort-building work.
Patient Journey
A longitudinal agent that constructs a unified, chronological view of each patient's journey. Events from structured data, clinical narratives, reports, and documents are aligned over time with clear links back to supporting evidence.
Why it matters: Clinicians and care coordinators need to understand the "story" of a patient's care—not just isolated data points. Patient Journey exposes progression, treatment changes, outcomes, and care gaps in an intuitive, human-readable format while remaining machine-queryable for downstream agents.
Key capabilities:
- Chronological event sequencing across all data sources
- Visual timeline representation with drill-down to source documents
- Care gap identification (e.g., overdue screenings, missing follow-ups)
- Treatment response tracking (e.g., medication changes after adverse events)
- Exportable summaries for referrals or care transitions
Journeys are used both directly by end users and indirectly by other agents for clinical reasoning.
Clinical Co-Pilot
An interactive agent that allows users to ask natural language questions about cohorts or individual patients—eligibility checks, longitudinal summaries, or data quality questions.
Why it matters: Most healthcare AI systems produce opaque outputs with no explanation. Clinical Co-Pilot grounds every response in curated data assets, surfaces supporting evidence, and exposes confidence and provenance rather than hallucinating answers.
Key capabilities:
- Natural language question answering ("Has this patient ever had a stroke?")
- Eligibility screening ("Which patients meet criteria for the diabetes prevention trial?")
- Data quality checks ("How many patients have missing smoking status?")
- Evidence retrieval with source document links
- Confidence scores flagging uncertain answers for human review
Clinical Co-Pilot demonstrates how LLMs can augment—not replace—clinical judgment when built on trustworthy data foundations.
Patient Registry
A specialized agent for building and maintaining disease-specific registries, including oncology. Leveraging the same abstraction, reasoning, and review workflows demonstrated in regulatory-grade cancer registry automation, this agent automatically populates hundreds of registry fields, surfaces conflicting evidence, supports human review, and maintains versioned, audit-ready records.
Why it matters: Registry abstraction is labor-intensive, error-prone, and inconsistent across abstractors. Automating this process while maintaining human oversight improves completeness, accuracy, and timeliness—critical for surveillance, research, and regulatory reporting.
Key capabilities:
- Automated field population from multimodal patient journeys
- NAACCR compliance for cancer registries
- Conflict detection and resolution workflows
- Human-in-the-loop review and approval
- Versioned submissions with full audit trails
Together, these agents demonstrate how a shared patient journey foundation supports both exploratory analysis and high-stakes, production workflows.
Building Your Own AI Agents
Beyond pre-built functionality, Patient Journey Intelligence is explicitly designed as an open, extensible agent platform. Healthcare organizations, academic medical centers, and healthcare IT vendors can build their own AI agents and applications without re-implementing data preparation, governance, or compliance.
Why This Matters
Traditional healthcare AI development follows a painful pattern:
- Data wrangling (4-6 months): Extract data from EHRs, parse clinical notes, normalize terminologies, link patient records
- Model development (2-3 months): Train and validate AI models
- Deployment (2-4 months): Build production infrastructure, implement security controls, create audit logging
- Repeat for next use case: Start over with similar but slightly different data pipelines
This approach is unsustainable. Organizations need 10+ FTE-years annually just to maintain overlapping data pipelines for different use cases.
Patient Journey Intelligence inverts this model. Build the data foundation once—comprehensive, standardized, continuously updated AI-ready patient journeys—and let every AI agent inherit that foundation. Teams spend their time on innovation (what questions to ask, what insights to surface) rather than data plumbing.
Key Principles
Open Standards
AI-ready datasets use open, widely adopted models like OMOP CDM and standard medical terminologies (SNOMED CT, RxNorm, LOINC). This avoids proprietary lock-in and ensures models generalize across institutions.
Deep Infrastructure Integration
Agents run within your existing cloud, analytics, and data platforms—AWS, Azure, Google Cloud, Snowflake, Databricks. They inherit your security policies, scaling configurations, and operational tooling without requiring separate infrastructure.
Separation of Concerns
Data curation and governance are handled once, at the platform layer. Agent developers focus on domain logic—clinical reasoning, user experience, workflow integration—without worrying about data quality, de-identification, or audit logging.
Three Ways to Access Platform Capabilities
The platform exposes its capabilities through multiple access patterns to support different teams and use cases:
1. RESTful APIs
Programmatic endpoints to activate and manage data curation jobs, interact with terminology services, manage cohorts, and retrieve curated patient facts. These APIs enable tight integration with existing applications and workflows.
Use cases:
- Integrating cohort-building into EHR workflows
- Triggering automated registry abstraction on new cancer diagnoses
- Syncing patient journey updates to external analytics platforms
- Building custom dashboards in internal portals
2. Model Context Protocol (MCP) Endpoints
Agent-friendly interfaces that allow natural language interaction with the platform—building cohorts, querying patient timelines, or asking contextual questions about curated data. These endpoints are optimized for AI agents that reason over patient journeys rather than raw tables.
Use cases:
- Conversational agents answering clinical questions
- Trial matching assistants screening patient eligibility
- Care coordination bots identifying patients needing outreach
- Documentation assistants summarizing longitudinal records
3. Direct Data Access via SQL
Because AI-ready datasets use industry-standard, open data models, users can directly query them using SQL. This enables data scientists to train new ML models, analysts to connect BI tools like Tableau or Power BI, and engineers to build custom analytics pipelines—all on top of the same complete, normalized, always-current patient journeys.
Use cases:
- Training predictive models for readmission risk
- Building executive dashboards for population health metrics
- Exporting research cohorts to R or Python for statistical analysis
- Creating custom reports for quality improvement programs
This combination makes Patient Journey Intelligence a durable foundation not just for today's use cases, but for continuous AI innovation over time.
Real-World AI Agent Examples
To make this concrete, here are examples of AI agents organizations build on Patient Journey Intelligence:
Clinical Trial Matching
The Challenge: Clinical trials fail to accrue patients because recruiters can't efficiently identify eligible candidates across thousands of patients. Eligibility criteria reference diagnoses mentioned in clinical notes, prior treatments buried in medication histories, and lab trends requiring temporal reasoning.
The Agent: A trial matching agent that continuously screens all patients against active trial eligibility criteria, flagging candidates and surfacing supporting evidence (e.g., "Patient meets inclusion criteria: HbA1c >7.5% on three consecutive tests, diagnosed with Type 2 diabetes 2018-03-15, currently on metformin monotherapy").
Why Patient Journey Intelligence Enables This:
- Complete multimodal patient records capture eligibility factors from structured and unstructured data
- Temporal reasoning capabilities evaluate sequences and progressions
- Real-time updates identify newly eligible patients as data arrives
- Provenance links surface supporting evidence for coordinator review
Risk Adjustment and Care Gap Identification
The Challenge: Health plans and ACOs miss significant revenue and quality incentives because diagnoses documented in clinical narratives aren't coded on problem lists. Traditional NLP tools extract mentions but don't reason about assertion status, temporality, or conflicts.
The Agent: A risk adjustment agent that identifies patients with documented diagnoses present in clinical narratives but missing from problem lists, supporting improved HCC scoring and HEDIS measures with audit-ready recommendations including full provenance for each finding.
Why Patient Journey Intelligence Enables This:
- Clinical reasoning layer distinguishes confirmed diagnoses from ruled-out conditions and family history
- Confidence scores flag uncertain extractions for coder review
- Provenance tracking enables audit-ready documentation of every recommended code
- OMOP standardization ensures consistent diagnosis mapping
Oncology Surveillance and Outcomes
The Challenge: Cancer registries struggle to maintain complete, timely data for surveillance and research. Abstractors manually review hundreds of documents per case, leading to inconsistencies and delays.
The Agent: An oncology surveillance agent that automatically extracts staging, treatment regimens, biomarker results, and outcomes from pathology reports, radiology studies, treatment plans, and follow-up notes—populating registry fields and tracking outcomes longitudinally.
Why Patient Journey Intelligence Enables This:
- Multimodal extraction captures cancer-specific facts from reports, notes, and structured data
- Terminology normalization maps local biomarker names to standard ontologies
- Longitudinal tracking monitors treatment response and progression over time
- Human-in-the-loop review maintains quality while accelerating abstraction