AI USE CASE
AI Patient Matching for Clinical Trials
Match eligible patients to clinical trials automatically by parsing medical records against study criteria.
What it is
NLP and ML models extract structured eligibility signals from unstructured EHR data, diagnoses, lab values, medications, comorbidities, and score each patient against trial inclusion/exclusion criteria in real time. Faster enrolment can shorten trial timelines by weeks to months, directly reducing per-patient acquisition costs.
Data you need
Structured and unstructured electronic health records (EHR/EMR) including diagnoses, lab results, medications, and clinical notes, linked to a library of trial eligibility criteria.
Required systems
- data warehouse
Why it works
- Establish a de-identification and data governance framework before model development begins.
- Involve clinical research associates and physicians in defining and validating eligibility criteria mappings.
- Run a retrospective validation on historical trial enrolments to measure recall before going live.
- Build a feedback loop so screeners can flag incorrect matches, enabling continuous model improvement.
How this goes wrong
- Poor EHR data quality or inconsistent coding (ICD, SNOMED) causes high false-negative rates, missing eligible patients.
- Overly rigid NLP pipelines fail to interpret free-text clinical notes accurately, especially non-standard terminology or abbreviations.
- Clinical and IT teams resist integration due to GDPR or HIPAA compliance concerns around model access to patient data.
- Model trained on one hospital's data generalises poorly when deployed across multiple sites with different documentation practices.
When NOT to do this
Do not attempt this if your EHR data is fragmented across incompatible systems with no unified patient identifier, as matching accuracy will be too low to trust for regulatory-grade trial enrolment.
Example vendors
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