Patient Recruitment Acceleration

Deploy privacy-preserving algorithms that perform an initial assessment of patient suitability using minimal demographic and diagnostic data before full record review.

• Reduces manual chart review time by up to 90%
• Operates on de-identified data to maintain HIPAA compliance
• Integrates directly with EHR systems via Trial Pre-Screening APIs
• Flags potentially eligible patients within seconds, not weeks

Utilize patient vector embeddings to transform clinical profiles into dense numerical representations, enabling semantic similarity comparisons against trial requirements.

• Goes beyond keyword matching to understand clinical context
• Identifies patients with atypical presentations that still meet criteria
• Enables ranked candidate lists through eligibility scoring
• Supports hybrid matching architectures combining deterministic rules with probabilistic semantic search

Apply clinical trial matching algorithms to non-interventional data sources including electronic health records, claims databases, and patient registries.

• Taps into vast pools of existing clinical data
• Identifies patients who may not be actively seeking trials
• Supports site feasibility assessments by estimating eligible populations
• Enables biomarker-driven screening for precision medicine trials

Convert parsed, structured eligibility criteria into executable database queries such as SQL or FHIR API calls to screen patient repositories at scale.

• Automates the translation of free-text protocol criteria into machine-readable logic
• Handles temporal reasoning for time-dependent constraints like washout periods
• Supports concomitant medication checking against prohibited drug lists
• Adapts to protocol amendments through automated logic updates

Systematically analyze why pre-screened patients failed to meet trial eligibility, creating a feedback loop that refines recruitment strategies.

• Identifies overly restrictive inclusion/exclusion criteria
• Reveals patterns in criteria weighting that may need adjustment
• Informs protocol design for future trials
• Reduces costly recruitment delays through continuous improvement

Automate the comparison of structured genomic variant data against molecular biomarker requirements such as EGFR mutation status or PD-L1 expression levels.

• Accelerates precision medicine trial enrollment
• Supports master protocol screening across basket and umbrella trials
• Integrates with clinical event sequencing to validate treatment history
• Enables simultaneous evaluation against multiple sub-study arms

An AI-driven computational process that compares structured and unstructured patient data against a clinical trial's inclusion and exclusion criteria to determine eligibility. Modern algorithms combine deterministic rule-based filtering with probabilistic semantic matching to maximize both precision and recall. The algorithm ingests parsed criteria and patient profiles, then outputs a ranked list of candidate matches with detailed justification for each eligibility decision.

The automated extraction and structuring of complex free-text inclusion and exclusion requirements from clinical trial protocols into a machine-readable format. This process uses medical named entity recognition and relation extraction to identify clinical concepts like lab values, disease stages, and temporal constraints. The output is a structured representation that can be directly consumed by a matching engine, eliminating manual protocol review.

An automated, privacy-preserving initial assessment of a patient's broad suitability for a clinical trial using minimal demographic and diagnostic data before full record review. Pre-screening acts as a high-recall filter that rapidly eliminates clearly ineligible patients while flagging potential candidates for deeper analysis. This two-stage approach dramatically reduces the computational and operational burden of full eligibility screening.

The application of clinical trial matching algorithms to non-interventional data sources such as EHRs, claims databases, and patient registries to identify potential trial participants. This approach leverages existing clinical data to find patients who may not be actively seeking trials, expanding the recruitment funnel beyond traditional site-based methods. Privacy-preserving architectures ensure compliance with data governance regulations.

The systematic review of reasons why pre-screened patients failed to meet trial eligibility, used to optimize recruitment strategies and refine protocol inclusion criteria. By analyzing patterns in screen failures, sponsors can identify overly restrictive criteria that may be unnecessarily excluding viable patients. This feedback loop between recruitment operations and protocol design is critical for accelerating enrollment timelines.

An analysis that uses automated patient screening to estimate the number of potentially eligible subjects at a specific research site to determine its viability for a trial. By running matching algorithms against a site's patient population data, sponsors can predict enrollment rates and allocate resources to high-yield locations. This data-driven approach replaces traditional intuition-based site selection.