A leading research network needed to overcome the bottleneck of manual chart review for clinical trials. Using AI Agents, we developed healthcare software—combining NLP, genomic matching, and governed automation so teams could seamlessly identify and enroll the right patients in record time.
95%
Reduction in per-file screening time
AI agents autonomously parsed unstructured clinical notes, genomic data, and lab histories in seconds compared to manual chart reviews.
45%
Reduction in enrollment timelines
Near real-time candidate identification and automated outreach allowed sites to meet their enrollment quotas significantly faster.
3.2×
Increase in precision matching
Cross-referencing complex inclusion/exclusion criteria against multi-modal patient data prevented false positives and broadened the eligible pool.
Finding the right patient for a highly specific oncology or rare disease trial was like finding a needle in a haystack. Highly trained clinical staff were spending hours each day manually combing through PDF pathology reports and free-text notes just to rule out candidates based on complex inclusion/exclusion criteria.
The organization required a scalable platform. Using AI Agents, we automated the parsing of these documents, matched them against trial requirements, and presented nurse navigators with pre-screened, highly qualified candidate shortlists.
Using AI Agents, we developed and deployed a solution across data ingestion pipelines, natural language processing, and workflow orchestration. The platform treated patient recruitment as an intelligent funnel: continuously scanning for new patients, evaluating against active trials, and initiating outreach workflows.
Autonomous Agents continuously reason over free-text clinical notes, pathology reports, and discharge summaries, extracting complex medical concepts mapped directly to trial eligibility criteria.
Specialized Agents autonomously interpret dense trial protocol PDFs, transforming them into structured, computable inclusion/exclusion rules that continuously run against the patient population.
A multi-agent swarm unifies EHR clinical history with genomic sequencing profiles to execute high-precision, multi-modal matching for targeted oncology and rare disease trials.
Upon identifying a match, outreach Agents autonomously generate personalized, clinician-approved pre-screening questionnaires and intelligently manage initial patient outreach workflows.
Dashboards provided principal investigators with real-time visibility into the candidate funnel, from initial AI screening to successful enrollment.
All agentic operations were deployed within a secure, governed perimeter, ensuring patient privacy and maintaining immutable audit trails of every algorithmic match.
Principal investigators and trial managers gained unprecedented visibility into the enrollment funnel. They could see exactly why a patient was matched or excluded, track outreach conversions, and ensure that trial quotas were on pace. Navigators reclaimed their time, focusing entirely on patient interaction rather than chart review.
Outcomes were measured by acceleration and scale: faster identification of candidates, dramatic reductions in administrative overhead, and ultimately, faster advancement of life-saving therapies.
5×
More eligible candidates identified per week
By removing the bottleneck of manual chart review, the system continuously surfaced candidates that would have otherwise been missed.
−60%
Administrative burden on nurse navigators
Navigators transitioned from data entry and screening to high-value patient counseling and trial education.
99.9%
Accuracy in criteria application
Automated logic checks drastically reduced human error in interpreting complex eligibility requirements.
Partner with ideyaLabs and AiLabs Agents to build intelligent screening automation, integrate safely with multi-modal clinical systems, and ship measurable improvements your research teams can trust.
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