The Problem in Dollar Terms
The enrollment gap
80% of clinical trials fail to meet enrollment timelines. A health system with 10 active trials, each needing 50 patients at $25K per enrollment, faces $12.5M in potential revenue—most of which goes unrealized because eligible patients aren't identified. Manual screening catches perhaps 20% of matches.
Clinical trial enrollment is a paradox: sponsors desperately need patients, patients often want access to cutting-edge treatments, and health systems want the revenue—yet trials consistently under-enroll. The bottleneck is identification. Eligible patients exist in your EHR; they just aren't being found.
Research coordinators screen patients manually—reviewing clinic schedules, checking problem lists, asking physicians for referrals. It's labor-intensive, error-prone, and systematically misses patients. The coordinator reviews charts in the oncology clinic but misses the eligible patient in cardiology. The patient who meets criteria arrives after hours when no one's screening.
What AI trial matching actually does
An AI matching system continuously screens your entire patient population against active trial eligibility criteria:
- Eligibility parsing: Converts complex inclusion/exclusion criteria into computable rules
- Deep EHR analysis: Scans diagnoses, labs, medications, procedures, notes—not just problem lists
- Continuous monitoring: Re-evaluates patients as new data arrives (new labs, new diagnoses)
- Match confidence: Ranks matches by likelihood of true eligibility, not just criteria met
- Coordinator workflow: Surfaces matches in actionable format for verification and outreach
Research coordinators shift from chart hunting to match verification—reviewing AI-surfaced candidates rather than manually screening schedules. The system catches patients they would have missed.
Why this requires deep integration
Trial criteria are often buried in clinical details: "LVEF < 40%," "no prior treatment with checkpoint inhibitors," "hemoglobin > 9.0 within 28 days." Matching requires reading echocardiogram reports, medication histories, and lab values—data scattered across the EHR. This is the most integration-intensive project in this framework series.
The 5-Minute Fit Assessment
This is the most complex framework in the series. Check the boxes carefully.
What You Need to Have Ready
✓ Required
- • Comprehensive EHR access (notes, labs, meds, problems, procedures)
- • Active trial eligibility criteria
- • Research coordinator team
- • IRB framework for AI-assisted screening
- • Patient population of sufficient size
● Significantly enhances value
- • CTMS integration for trial management
- • Structured eligibility criteria (vs. PDF protocols)
- • Historical enrollment data for validation
- • Patient consent preferences
- • Physician notification workflow
The criteria ingestion challenge
Trial eligibility criteria are often complex and nested:
Inclusion: Age ≥ 18 AND confirmed diagnosis of [condition] AND ECOG ≤ 2 AND (prior treatment with [drug class] OR treatment-naive)
Exclusion: Prior checkpoint inhibitor within 6 months OR active autoimmune disease OR LVEF < 40% OR creatinine clearance < 30
Converting these criteria into computable queries against your EHR—especially when data lives in unstructured notes—is the core technical challenge. This is why implementation takes 6-8 months.
Build vs. Buy vs. Partner
Build internally when:
- • You have clinical NLP and EHR integration expertise
- • You have 18-24 months for development
- • You want complete IP ownership
- • Your trial portfolio is specialized and stable
Buy off-the-shelf when:
- • Your EHR vendor offers trial matching
- • Your trials are common (oncology Phase III)
- • You want faster deployment
- • You're okay with limited customization
Partner for custom when:
- • Your trials have complex/unique criteria
- • You need NLP trained on YOUR documentation
- • Integration requirements exceed vendor limits
- • Match accuracy matters more than speed
The documentation interpretation challenge
Trial criteria often require interpreting clinical nuance: "no significant cardiac history" isn't a checkbox—it requires understanding what your cardiologists document and how. Generic matching systems miss these patterns. Custom NLP training on your documentation is the difference between noise and signal.
Red Flags: When to Wait
You have a small research program
With fewer than 10 active trials, the ROI calculation changes dramatically. Manual screening may be more cost-effective. This solution shines at scale.
Research coordinators are already overwhelmed
AI surfaces more matches—which is only valuable if someone can follow up. If your team can't handle current volume, adding matches creates frustration, not enrollment.
Your EHR data quality is poor
If problem lists aren't maintained, labs aren't flowing, or clinical notes are sparse—matching accuracy suffers. Fix data quality first.
You haven't done simpler AI projects
This is the most complex project in the framework series. If your organization is new to AI, start with quick wins to build muscle and confidence first.
Questions to Ask Any Vendor
On their matching approach:
- "How do you handle criteria that require interpreting clinical notes, not just structured data?"
- "What's your false positive rate? How many non-eligible patients will be surfaced?"
- "How do you handle complex nested criteria with AND/OR logic?"
On integration:
- "What EHR data elements do you require? How deep is the integration?"
- "How do you ingest trial eligibility criteria? Structured input or PDF parsing?"
- "How do matches surface for research coordinators?"
On results:
- "What enrollment improvement should we expect? Show me comparable sites."
- "How do you measure success—matches generated or patients enrolled?"
- "What's your typical time from match surfaced to patient approached?"
Quick ROI Estimate
Estimated annual value:
$2,250,000 - $5,625,000
Based on 15 trials, 30 patients each, $25K per patient, 30-50% enrollment improvement
Ready to explore clinical trial matching?
This is our most complex framework. Let's assess your research program and estimate the potential impact.
