Clinical Trial Enrollment Thinking
Protocol design, site operations, EHR data matching, and the specific mechanics of enrollment failure. What we have learned building Enrollvue from inside clinical research.
Why Chart Review Still Kills Enrollment in 2026
Manual chart review accounts for a substantial portion of coordinator time at most investigator sites. We walk through what the bottleneck actually looks like at the workflow level, and why existing tools have not solved it.
The NLP Gap in Inclusion/Exclusion Criteria Matching
Most rule-extraction pipelines collapse on negated criteria, temporal qualifiers, and compound conditions. Here is how we approach this problem differently.
Site Activation Timeline Reality: Where the Months Go
Sponsors budget 4 months for site activation. Reality is closer to 7. We break down the actual distribution of delays by phase and what teams can control.
CRO-Sponsor Data Handoff Friction: A Quiet Enrollment Tax
Every time a CRO hands patient lists back to a sponsor for eligibility review, the process slows. We quantify this friction and discuss how to remove it.
Protocol Complexity and Enrollment Rate Correlation
We analyzed 60-plus trial protocols across therapeutic areas to map how the number of exclusion criteria correlates with actual enrollment pace at the sites we work with.
Rare Disease Enrollment Is a Different Problem
Prevalence of 1 in 10,000 means patient-matching math is fundamentally different from oncology. Distributed EHR querying, patient registries, and social reach all matter differently.
Structured vs Unstructured EHR Data for Patient Matching
Structured fields cover maybe 30 percent of what the protocol actually asks. The rest lives in free-text notes. Here is what that means for matching accuracy.
PI Burden in Pre-Screening: What Sites Actually Do
We interviewed coordinators at 12 investigator sites about their actual pre-screening workflows. The variation is wider than sponsors assume.
Oncology Trial Enrollment Benchmarks Worth Knowing
Enrollment rates vary by 3x to 5x across oncology trials with comparable protocols. We look at what the high-performing outliers do differently.
HIPAA-Aligned Patient Matching Architecture
Patient data does not leave the site. Here is the architecture pattern we use to run matching computations while keeping identifiable records local.
Decentralized Trials and the Site Model Tension
DCTs promise broader reach. But site-based enrollment infrastructure is still how most Phase 2 and 3 trials run. We discuss the tension and what hybrid looks like.
Building Enrollvue: What We Learned in Year One
From first clinical partner conversations to a working protocol-matching engine, this is the honest version of what took longer than expected and what clicked faster.
Clinical Trial Enrollment: Why 80 Percent of Trials Run Late
Enrollment delay is the single most cited reason trials miss timelines. We walk through the structural causes: protocol design, site selection, and pre-screening gaps.
What Protocol Criteria Matching Actually Requires
Eligibility criteria look like simple logic statements. In practice they are layered with temporal conditions, lab value ranges, prior therapy windows, and natural language ambiguity.