Real-World Data Screening

RWD screening operates on historical patient data rather than point-of-care encounters. The system analyzes longitudinal records—often spanning years—to identify patients who meet trial criteria at a specific moment in time.

• Queries structured fields (ICD-10-CM, LOINC, RxNorm) and unstructured clinical notes simultaneously
• Applies temporal reasoning to validate event sequences (e.g., diagnosis followed by progression within 6 months)
• Generates a pre-screened cohort for feasibility analysis before site activation

Unlike prospective EHR-based screening, RWD approaches can assess population-level eligibility without interrupting clinical workflows.

RWD screening ingests heterogeneous data from claims databases, EHR systems, and specialty registries, each with distinct schemas and coding conventions. The screening engine must normalize these sources into a unified patient model.

• Maps proprietary lab codes to LOINC for cross-site comparability
• Reconciles medication records from pharmacy claims with EHR prescription orders
• Aligns diagnosis codes across ICD-9-CM and ICD-10-CM transitions
• Handles duplicate and conflicting records through entity resolution

This harmonization layer is the prerequisite for applying consistent eligibility logic across disparate real-world datasets.

A defining characteristic of RWD screening is the ability to extract eligibility-relevant information from free-text clinical narratives that exist outside structured fields. Critical inclusion criteria—such as ECOG performance status or specific pathology findings—often reside only in notes.

• Deploys medical named entity recognition to identify diseases, procedures, and measurements
• Applies negation and uncertainty detection to distinguish affirmed findings from ruled-out conditions
• Uses clinical entity linking to ground ambiguous mentions to standard ontologies (SNOMED CT, RxNorm)
• Processes millions of documents across patient populations using distributed NLP pipelines

Without unstructured data parsing, RWD screening misses the majority of granular eligibility evidence.

RWD screening operates on sensitive patient data governed by HIPAA, GDPR, and contractual data use agreements. The screening architecture must enforce privacy controls while enabling computationally intensive analysis.

• Executes screening algorithms within secure, audited environments rather than exporting raw data
• Supports de-identified cohort reporting where only aggregate eligibility counts are exposed
• Implements role-based access controls that limit which users can view patient-level results
• Maintains comprehensive audit trails documenting every data access and screening execution

This privacy-by-design approach enables pharmaceutical sponsors to assess trial feasibility without assuming custody of protected health information.

RWD screening translates trial eligibility criteria into executable computable phenotypes—machine-processable definitions expressed as logical expressions over standardized data elements.

• Decomposes complex criteria into atomic, evaluable components
• Encodes temporal constraints (e.g., "no myocardial infarction within 90 days") as time-window predicates
• Applies criteria weighting to prioritize patients based on the criticality of each requirement
• Generates eligibility scores that rank patients by match quality rather than binary pass/fail

The phenotype execution engine applies these definitions across the harmonized data layer to produce ranked, auditable patient lists.

Unlike point-of-care screening that identifies individual patients during clinical encounters, RWD screening often serves a feasibility assessment function—answering "how many eligible patients exist in this population?" before sites are activated.

• Produces aggregate eligibility counts stratified by site, geography, or demographic characteristics
• Enables protocol optimization by revealing overly restrictive criteria that eliminate large patient segments
• Supports site selection by ranking locations based on potential enrollment yield
• Feeds recruitment timeline projections with data-driven enrollment rate estimates

This paradigm shift from reactive to proactive screening fundamentally changes how sponsors plan and execute clinical trials.

An AI-driven computational process that compares structured and unstructured patient data against a trial's inclusion and exclusion criteria. These algorithms ingest data from EHRs and claims databases, transforming raw clinical narratives into structured profiles. The core function is to compute an eligibility score by evaluating logical expressions against a patient's longitudinal record, enabling the rapid identification of potential participants from vast real-world datasets.

A machine-processable definition of a clinical condition, expressed as a set of logical expressions and data queries. Used to identify patient cohorts from electronic health records, a computable phenotype translates narrative descriptions like 'severe asthma' into executable code. It combines ICD-10-CM codes, lab values, medication orders, and temporal constraints to create a deterministic algorithm that can be run against structured and unstructured data in a phenotype execution engine.

The AI capability to interpret and validate time-dependent clinical constraints against a patient's longitudinal record. This involves clinical event sequencing to verify that events occurred in the correct order and within specified time windows. For example, confirming that a patient received a specific therapy after a diagnosis but before disease progression. It requires sophisticated patient timeline reconstruction from disparate, timestamped data points across EHRs and claims systems.

A clinical trial screening system design that combines deterministic rule-based filtering with probabilistic semantic matching. The deterministic layer applies strict computable phenotype logic to structured data (labs, demographics). The probabilistic layer uses patient vector embeddings to compare unstructured clinical narratives against trial criteria, identifying patients who may not have exact coded matches but are semantically similar. This architecture maximizes both precision and recall in real-world data screening.

An automated, privacy-preserving initial assessment of a patient's broad suitability for a clinical trial. Using minimal demographic and diagnostic data, often accessed via a Trial Pre-Screening API, this process filters large patient populations before full record review. It serves as a high-recall, low-friction gate that dramatically reduces the computational and privacy burden of deep screening, enabling rapid site feasibility assessments and recruitment forecasting.