Site Feasibility Assessment is the quantitative process of evaluating a specific research site's ability to recruit qualified participants for a clinical trial by analyzing its patient population against the protocol's inclusion and exclusion criteria. This assessment moves beyond subjective investigator surveys by applying automated patient screening algorithms to real-world data, such as electronic health records, to generate an objective, verifiable estimate of the eligible patient pool.
Glossary
Site Feasibility Assessment
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.
CLINICAL TRIAL OPERATIONS
What is Site Feasibility Assessment?
A data-driven analysis that estimates the number of potentially eligible subjects at a specific research site to determine its viability for a clinical trial.
The core mechanism involves executing a computable phenotype against a site's de-identified patient registry to produce a precise enrollment projection, replacing traditional guesswork with statistical evidence. By integrating temporal reasoning and biomarker-driven screening, the assessment validates not just the presence of a condition but the alignment of complex clinical timelines and molecular profiles, enabling sponsors to rank sites by predicted performance and avoid costly low-enrolling locations.
SITE FEASIBILITY
Key Components of an AI-Driven Assessment
An AI-driven site feasibility assessment automates the analysis of a research site's patient population against complex trial protocols, replacing manual chart reviews with rapid, data-driven viability scoring.
01
Automated Patient Pre-Screening
The foundational process of applying inclusion and exclusion criteria against a site's de-identified patient data warehouse. AI parses unstructured clinical notes to identify potential subjects without manual chart review.
- Reduces screening time from weeks to hours
- Identifies candidates missed by manual ICD-10 code searches
- Preserves patient privacy through de-identification before analysis
02
Computable Phenotype Execution
A machine-processable definition of a clinical condition expressed as logical expressions and data queries. The engine resolves these against structured and unstructured data to return a precise patient count.
- Combines diagnosis codes, lab values, and narrative text
- Handles temporal constraints like 'HbA1c > 7.0 in the last 6 months'
- Provides a realistic denominator for enrollment projections
03
Eligibility Criteria Parsing
The automated extraction and structuring of complex free-text protocol requirements into machine-readable logical components. This eliminates manual interpretation errors when translating a PDF protocol into database queries.
- Decomposes 'adequate organ function' into specific lab thresholds
- Normalizes synonymous terms to standard ontologies like SNOMED CT
- Handles nested boolean logic (AND/OR/NOT) within criteria
04
Patient Vector Embedding
A technique that transforms a patient's entire clinical profile into a dense numerical vector. This enables semantic similarity comparisons against trial requirements, going beyond simple keyword matching.
- Captures latent clinical context from unstructured notes
- Enables ranking of candidates by overall fit score
- Supports fuzzy matching for complex phenotypic descriptions
05
Screen Failure Analysis
The systematic, AI-driven review of reasons why pre-screened patients failed to meet eligibility. This analysis identifies protocol design barriers and refines site selection strategy.
- Categorizes failures by specific criterion (e.g., washout period, lab value)
- Informs protocol amendments to improve enrollment feasibility
- Provides data-driven feedback to sponsors on site selection
06
Temporal Reasoning Engine
The AI capability to interpret and validate time-dependent clinical constraints against a patient's longitudinal record. This is critical for validating sequences like 'progression after first-line therapy.'
- Reconstructs patient timelines from disparate timestamped data
- Validates washout periods and disease progression timelines
- Ensures correct chronological ordering of clinical events
SITE FEASIBILITY ASSESSMENT
Frequently Asked Questions
Explore the core concepts behind using automated patient screening to evaluate a research site's potential for successful clinical trial recruitment.
A site feasibility assessment is a data-driven analysis that estimates the number of potentially eligible subjects at a specific research site to determine its viability for a clinical trial. This process moves beyond anecdotal investigator estimates by applying computable phenotype algorithms and patient pre-screening logic directly against a site's electronic health record (EHR) data. The goal is to provide a realistic projection of recruitment yield before site activation, minimizing costly screen failure analysis and non-enrolling sites. The assessment typically involves parsing the trial's eligibility criteria and executing a cohort identification query against the site's patient registry to generate a quantitative enrollment forecast.
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About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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