Our platform helps clinical teams identify likely screen failures earlier, improve enrollment predictability, and support audit-ready eligibility workflows across EHRs, spreadsheets, email, and site operations—without replacing the clinical coordinator.
Signal likely mismatches before they become screen failures
Predictable enrollment milestones and operational planning
Administrative burden for coordinators and QA teams
Workflow snapshot
EHRs, spreadsheets, email threads, site notes
Flags possible eligibility mismatches early
Clinical teams remain final decision-makers
Structured documentation for QA and IRB processes
In complex chronic disease studies, eligibility review often happens across disconnected systems and handoffs. Teams juggle clinical records, screening spreadsheets, site emails, and sponsor oversight with limited time to reconcile details before a candidate reaches the door.
Eligibility mismatches surface too late, after staff time, patient effort, and scheduling resources are already spent.
Manual checks across EHRs, spreadsheets, email, and site workflows create avoidable bottlenecks and inconsistency.
Screen-failure volatility makes enrollment forecasting difficult and can affect milestone confidence and study execution.
When eligibility rationale is spread across multiple tools, documentation takes longer and audits become harder to support.
The platform uses AI-assisted pre-screening to highlight likely eligibility concerns earlier, helping teams focus clinical review where it matters most. It is built to integrate into existing operations and support—not replace—the judgment of coordinators, investigators, and study teams.
Protocol criteria, site notes, and operational inputs are organized into a single review surface.
AI-assisted logic helps identify probable eligibility mismatches and missing evidence before a candidate advances.
Coordinators and clinical teams review the recommendation, document rationale, and make the final call.
Structured outputs support QA review, IRB inquiries, and consistency across trials and sites.
A single operational layer can improve screening speed, reduce unnecessary back-and-forth, and give stakeholders better visibility into enrollment risk without changing the clinical decision ownership model.
Flag likely eligibility mismatches earlier so coordinators can focus on the right patients first.
Better visibility into likely pass/fail patterns supports forecasting, milestone planning, and study oversight.
Cut down repetitive administrative review and chasing missing details across fragmented systems.
Generate structured documentation that helps QA, monitoring, and IRB-related review processes.
The platform is useful wherever trial enrollment slows down due to review complexity, inconsistent evidence collection, or repeated handoffs.
Gain better enrollment forecast visibility and reduce timeline risk across programs.
Standardize pre-screening support and reduce variation across studies and sites.
Reduce administrative burden and support more efficient patient intake and review.
Benefit from more structured rationale, traceability, and documentation quality.
Built for programs with nuanced inclusion/exclusion criteria, frequent amendments, and a higher risk of delayed screening decisions.
Designed to fit current operational patterns rather than requiring a full process overhaul.
This is not an autonomous eligibility decision system. It is a review support layer that helps teams work faster, more consistently, and with better documentation.
We are inviting teams that manage complex chronic disease studies and want to improve screening efficiency, operational confidence, and documentation quality while preserving clinical oversight.
See how a clinician-led eligibility support layer can reduce delays, strengthen operational predictability, and make review workflows more auditable for your team.