Conventional trial recruitment optimizes for speed.It does not reveal whether patients can sustain participation.PRISM governs the difference between movement and durable progression.
Every conventional approach accelerates funnel movement. None measure whether patients stay ready.
DIY relies on fragmented data. Site-based is locked to institutional databases. Digital optimizes for audience targeting with limited clinical depth. AI vendors ingest Claims and EHR but skip behavioral and social signals. None predict completion probability.
Integrates Claims, EMR, SDOH, BDOH, and live conversational signals. Prioritizes patients by completion probability, not just eligibility. Pre-screening identifies durable candidates before recruitment spend.
All define "ready" as eligible (criteria met), interested (clicks), engaged (activity), or converted (action). Education is static content or templates. Comprehension is never measured. Hesitation signals are manual or invisible.
Ready means cognitively, emotionally, and logistically capable of completing. AI conversational education adapts to signals. We measure comprehension before enrollment. We detect hesitation through language and behavior. We intervene on clarity gaps in real time.
DIY is custom-built but operationally heavy with long timelines. Site-based carries high burden and can't scale. Digital drives volume without durable outcomes. AI vendors accelerate speed but produce unstable cohorts.
Readiness builds before a patient is ever referred. Sites receive patients prepared to complete, and automated workflows reduce administrative burden. Fewer referrals, higher readiness. Sites get fewer screen failures, lower rescue recruitment dependency, and predictable enrollment.
Enrollment is an operational step or conversion milestone. No decision quality measurement. Screen failures are normal. Early dropout is not forecasted. Enrollment curves are volatile.
Enrollment is a behavioral commitment shaped by comprehension and expectation. We measure understanding through assessment, not acknowledgment. Progressive expectation setting reduces shock. We predict early dropout and stabilize curves for sponsor confidence.
Digital targets audiences and optimizes for clicks. PRISM measures readiness through behavioral signals, comprehension testing, and durability prediction. We build readiness and verify understanding before enrollment.
PRISM complements CRO recruitment by improving quality and predictability. Readiness pre-screening strengthens patient understanding. Sites get durable referrals instead of volume, cutting screen failures and rescue recruitment.
Most AI vendors accelerate funnel speed but skip retention-risk prediction. PRISM's readiness framework spans Claims, EHR, SDOH, BDOH, and live signals. We measure comprehension, predict dropout, and predict post-enrollment retention risk as core, feeding the sponsor's retention strategy rather than treating it as an afterthought.
Speed without readiness creates volatility. Faster funnels fill screens with unstable patients. Readiness-optimized curves are predictable and durable. Sites prefer durable referrals over noise. Predictability reduces cost per completer.
Yes. Dropout isn't random; it's predictable. PRISM identifies hesitation and comprehension gaps before enrollment, predicts and flags retention risk post-enrollment for the sponsor's team to act on, and predicts early dropout. This cuts rescue recruitment dependency and stabilizes curves.
In two days, you get three execution diagnostics on your program.
How likely each eligible patient cluster is to activate, persist, and complete, before any intervention.
The barriers most likely to suppress activation, enrollment, and completion in each cluster, and how to mitigate them.
Which clusters to target, which barriers to address, which interventions to deploy, and what completion lift to expect.