Independent reviews put the share of trials that miss their original enrollment timeline above 80% — and a Tufts CSDD analysis of 16,000+ sites found roughly half either under-enroll or enroll no patients at all. TrialMatch Nav is the 74-page, 46-chapter blueprint for building the AI-assisted matching layer that closes that gap — explainable by design, human-confirmed by design, and built to never diagnose or confirm eligibility on its own.
"TrialMatch Nav never diagnoses and never confirms eligibility. It shows its work — every satisfied criterion, every gap, every unknown — and a qualified human always confirms before anything happens next."
The gap isn't a shortage of trials or a shortage of patients — it's the missing connective layer between dense eligibility criteria and fragmented patient information.
Most patients who hear about a relevant trial hear about it online — not from their own doctor, even though most say they'd prefer to.
Eligibility criteria are written for clinicians, not for comparison — dense free text a coordinator has to interpret candidate by candidate.
Manual chart review caps how many candidates a site can screen, regardless of how many eligible patients actually exist.
Every "AI matching" tool that exists today is either a black box or scoped to one side of the match — never both, and rarely explainable.
A structured, explainable matching layer between what a trial requires and what's known about a specific patient — never a diagnosis, never a confirmed-eligibility claim.
Not a diagnostic tool · Not an eligibility determination · Not a CRO or coordinator replacement · Not medical, legal, or regulatory advice.
46 chapters across 11 parts, mirroring the ebook's actual table of contents — from the recruitment problem to a 12-slide investor pitch.
Ch. 1–9 — The recruitment problem with sourced data, six detailed ICPs, the ten-step user journey, all four product interfaces, and a deliberately narrow MVP scope.
Ch. 10–14 — Data ingestion, criteria extraction, patient normalization, the four-category scoring model, explainability, and the permanent human-in-the-loop architecture.
Ch. 15–20 — Full system architecture, database design, API strategy, a complete privacy & security control set, a regulatory issue-map, and the AI failure safety system.
Ch. 21–25 — Revenue model, a four-tier pricing strategy, three startup-budget scenarios, unit economics, and a three-year, three-scenario financial model.
Ch. 26–37 — A 10-sprint build plan, GTM strategy, sales scripts, pilot-program design, brand strategy, a 90-day content plan, onboarding, KPIs, and the competitive landscape.
Ch. 38–46 — Risk register, ethical business model, funding roadmap, investor pitch, the first 10 customers, a worked case study, plus the AI Prompt Library, worksheets, and appendices.
TrialMatch Nav's blueprint is written for a founder building a healthtech SaaS, and it stays grounded in the real organizations that product would serve.
Someone seriously considering building a clinical-trial-matching or recruitment-support SaaS and wanting a complete, realistic operating plan.
CRO, sponsor, and site professionals who want a fully worked example of ICPs, product architecture, and a go-to-market plan for this exact category.
Engineers and AI/ML builders who want the exact architecture pattern — LLM for structuring, deterministic rules engine for scoring, human review as a permanent gate.
Anyone evaluating a clinical-trial-matching pitch who wants a rigorous, honest reference for what a well-built, well-governed one should cover.
TrialMatch Nav: The AI Clinical Trial Matching Blueprint
One-time payment · Instant PDF download · 74 pages
Get Instant Access — $12.99This is a one-time ebook purchase, not a subscription. Chapter 22 inside covers the recommended product's own subscription-pricing strategy for TrialMatch Nav the platform.
TrialMatch Nav is a brand-new release. We're not fabricating reviews — here's honest placeholder space until real readers weigh in.
Awaiting first reader review.
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Get the complete TrialMatch Nav blueprint — the research, the AI architecture, the compliance map, the pricing model, and the launch plan — for one low one-time price.
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