How Trially.ai Solves the Patient Recruitment Paradox Defined by Tufts Center for the Study of Drug Development
Executive Summary
The clinical research industry is paralyzed by a costly "Field of Dreams" fallacy: the assumption that "the mere decision to invest and begin adopting an innovation will automatically attract broad support and usage". As Ken Getz, Executive Director at the Tufts Center for the Study of Drug Development, explains in his interview with Applied Clinical Trials ("If You Build It ... Will They Come?"), this passive mindset leads to failure because it ignores the operational realities of study execution.
This operational misalignment exacts a heavy toll on clinical trial performance. While the industry struggles through a six-to-seven-year cycle to fully implement new tools, sites remain unsupported and underperforming, ultimately missing their enrollment benchmarks 68% of the time.
Getz argues that without active change management and decentralized buy-in, the industry’s default outcome is the opposite of the dream: "If you build it, they will not come".
This white paper analyzes the structural barriers to recruitment and technology adoption identified by Ken Getz and demonstrates how Trially.ai systematically dismantles them. By moving beyond the passive "build it" mindset to an active, AI-driven engagement model, Trially bridges the critical gap between "idealized" protocol design and the reality of patient availability.
I. The "Build It" Fallacy vs. Active AI Identification
The Paradox: Ken Getz argues that the "centralized, well-funded approach creates early momentum, but it also plants the seeds of future failure" because it assumes that availability equals utilization. He notes that simply opening a site or deploying a tool does not guarantee patients will appear. The disconnect lies in the fact that centralized teams often lack a "deep understanding of how functions operate separately and interdependently" regarding therapeutic nuances. Consequently, "deployments of the innovation reflect an idealized version of how clinical operations should work, not how it actually works".
The Trially Solution: Trially Match Trially.ai addresses the "Build It" fallacy by eliminating the passive wait for patients. Instead of relying on traditional advertising or manual chart reviews—which assume patients will identify themselves—Trially employs Trially Match, a proprietary AI engine that actively interrogates the "haystack" of Electronic Health Records (EHR) to find the needles.
Solving the Idealized vs. Actual Gap: Getz warns against tools that don't fit the reality of clinical workflows. Trially solves this by ingesting structured and unstructured data (notes, imaging, labs) directly from the EHR, achieving ~95% accuracy in reading medical records.
Operational Reality: Rather than an "idealized" protocol match, Trially’s AI performs a Multi-Protocol Match, stacking candidates based on actual eligibility across multiple studies simultaneously. This replaces the "guesswork" Getz critiques with data-driven precision, resulting in a 2-6X increase in monthly enrollment for complex studies.
II. Overcoming the "Trough of Disillusionment" via Automation
The Paradox: Getz describes a critical failure point in the adoption cycle known as the "Trough of Disillusionment". This occurs when "the burden and disruptive nature of accommodating that innovation are felt acutely" by site staff. When new solutions threaten "short-term performance," clinical teams logically respond by "minimizing use and reverting to familiar legacy practices".
The Trially Solution: Margo AI Agent Trially specifically targets this friction point by automating the most burdensome aspect of trial execution: the 250+ hours per month sites spend on manual screening.
Removing the Burden: Trially introduces Margo, an AI agent that acts as an extension of the clinical team. Margo handles the labor-intensive "grunt work" of prescreening, scheduling, and patient follow-up via SMS and voice.
Reversing the Downward Spiral: Getz notes that "when adoption threatens short-term performance," tools are abandoned. By offloading prescreening to Margo, Trially reduces screen failure rates by 73%, directly improving the site's short-term performance metrics and revenue without adding to the staff's workload. This prevents the "Trough of Disillusionment" by delivering immediate, visible efficiency gains.
III. The Infrastructure Disconnect: "Plug Into What You Use"
The Paradox: A major barrier identified by Getz is the friction caused by "change management ineffectiveness" and the complexity of the operating environment. He argues that innovation adoption is "painfully slow and highly inefficient" because it often requires dismantling existing workflows to accommodate new, standalone "panaceas".
The Trially Solution: Frictionless Interoperability Trially’s architecture is designed to answer Getz’s call for tools that fit how clinical operations actually work.
Integration vs. Disruption: Trially utilizes APIs built by engineers from Zapier—experts in connectivity—to "plug into what you already use". Whether it is an EHR, CTMS (like CRIO), or a CRM, Trially integrates without requiring a "rip and replace" strategy.
Rapid Implementation: Contrasting with the "six to seven years" for full implementation cited by Getz, Trially offers 1-day site onboarding and EHR integration in as little as one week. This speed addresses Getz’s concern regarding "protracted adoption processes".
IV. Aligning Incentives: From "Mandate" to "Grassroots Usage"
The Paradox: Getz emphasizes that sustainable adoption occurs only when "decentralization permits adoption decisions to be customized" and integrated into project-level activity. He states, "Clinical teams and individual functions are more likely to invest in adoption when they can capture and monitor visible benefits... within their span of control".
The Trially Solution: Pipeline Radar & Intelligence Trially shifts the value proposition from a sponsor-imposed mandate to a site-centric revenue generator.
Visible Benefits: Through Trially Intelligence and the Pipeline Radar, sites can proactively identify best-fit studies for their specific patient populations.
Incentive Alignment: By providing "instant feasibility counts" and projecting potential revenue (e.g., "$2.4M Potential Revenue" visible on the dashboard), Trially aligns the technology with the site's economic incentives.
Empowering the Site: As Getz suggests, this creates "function-level demand and support for the innovation," replacing compliance with genuine "grassroots usage". Sites use Trially not because they are told to, but because it helps them win awards and secure approximately $440K in direct study revenue from enrollees.
Conclusion: Reaching the "Slope of Enlightenment"
Ken Getz concludes that for innovation to succeed, it must not be seen as a "disruptive cure-all," but as an "enabler of multifunctional performance". He argues that decentralization and local value realization are essential to move from the "Trough of Disillusionment" to the "Slope of Enlightenment".
Trially.ai functions as this precise enabler. By automating the search for patients (Trially Match), removing the administrative burden from staff (Margo AI), and aligning technology with site revenue goals (Pipeline Radar), Trially solves the operational paradoxes that have historically stalled clinical trial recruitment.
The verdict is clear: If you build it, they may not come—unless you have the intelligence to find them, the automation to engage them, and the infrastructure to enroll them. Trially provides all three.
References
Getz, K. "If You Build It, Will They Come?" Applied Clinical Trials, Jan 23, 2026.
Getz, K. Audio Interview Transcript.
