Lesson 251 of 1550
AI for Clinical Trial Recruitment: Patient Matching at Scale
Trials fail to recruit. AI matching systems can scan EHRs against eligibility criteria across an entire health system — finding candidates that would never have been identified manually.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2AI and Clinical Trial Eligibility Pre-Filter: Saving Coordinator Time
- 3The premise
- 4AI and Clinical Trial Screening: Eligibility Checklists
Concept cluster
Terms to connect while reading
Section 1
The premise
Manual trial recruitment misses eligible patients; AI EHR scanning surfaces candidates so research coordinators focus on consent and enrollment.
What AI does well here
- Index EHR data against trial eligibility criteria (inclusion + exclusion + protocol-specific)
- Surface partial-match candidates so research coordinators can verify what the AI couldn't
- Generate clinician-friendly summaries explaining why the patient was matched
- Track conversion (matched → contacted → consented → enrolled) to measure system value
What AI cannot do
- Substitute for the formal eligibility verification by the trial team
- Replace informed consent (a clinician-led conversation)
- Catch every nuance of complex eligibility criteria (especially imaging or pathology criteria)
Key terms in this lesson
Section 2
AI and Clinical Trial Eligibility Pre-Filter: Saving Coordinator Time
Section 3
The premise
AI can scan a structured patient list against a trial's inclusion and exclusion criteria and produce a triaged shortlist for coordinator review.
What AI does well here
- Apply structured criteria across a list to surface plausible matches
- Cite which criterion each patient appears to meet
What AI cannot do
- Confirm a match against unstructured chart notes
- Replace the IRB-required eligibility verification step
Section 4
AI and Clinical Trial Screening: Eligibility Checklists
Section 5
The premise
AI can take inclusion and exclusion criteria from a trial protocol and produce a checklist that a study coordinator can apply to charts.
What AI does well here
- Convert protocol prose into a yes/no checklist
- Flag criteria that require lab values versus chart review
What AI cannot do
- Make the final eligibility determination
- Substitute for IRB-approved screening procedures
Section 6
AI and Clinical Trial Matching: Screening Eligibility Criteria Against a De-Identified Chart
Section 7
The premise
A phase 3 oncology trial has 47 inclusion and 31 exclusion criteria. Manually screening one chart takes 90 minutes. An LLM does first-pass screening in 4 minutes — but it will say 'eligible' to a patient with a hidden disqualifier.
What AI does well here
- Compare a structured criteria list against a de-identified problem list and labs.
- Flag missing data points the screener needs to confirm (echo, tumor markers).
- Rank multiple trials by best fit when several are open.
- Generate the screening note for the regulatory binder.
What AI cannot do
- Replace IRB-required eligibility verification by the PI.
- Read between the lines of an old consult note to infer prior treatment.
- Decide if the patient actually wants to enroll — that's a conversation.
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “AI for Clinical Trial Recruitment: Patient Matching at Scale”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Adults & Professionals · 9 min
AI and a clinical trial eligibility skim
Use AI to compare a patient summary against trial inclusion and exclusion criteria, then surface a likely-fit list.
Adults & Professionals · 10 min
Clinical Trial Patient Matching: AI-Assisted Eligibility Screening
Clinical trials enroll only 3-5% of eligible patients, partly because eligibility screening is time-intensive. AI can assist in matching patients to trials by comparing patient profiles to eligibility criteria — expanding research participation and patient access to cutting-edge treatments.
Adults & Professionals · 10 min
AI Pharmacy Dispense Verification: Catching Errors Pre-Patient
Medication errors at dispense are a major source of patient harm. AI verification catches more than human checks alone.