cognAIzant dx offers an AI‑driven clinical decision support platform that analyzes electronic health record metadata to generate accurate, personalized recommendations for adolescent mental and behavioral health. By integrating directly with EHR systems, it streamlines care coordination and enables providers to proactively profile patients, automate resource deployment, and enhance traditional screening methods. The tool is designed to improve outcomes for adolescents and support clinicians with actionable insights.
Funding
Funding not disclosed
Founders
Product
Problem
Adolescents are experiencing a rising incidence of mental health and substance use disorders, yet early detection is hampered by limited screening tools and fragmented care coordination within existing electronic health record (EHR) workflows.
Solution
cognAIzant dx provides an AI‑driven clinical decision support platform that analyzes EHR metadata and patient‑reported symptomology to generate personalized mental‑health risk assessments for adolescents. The system integrates directly with EHRs, allowing providers to profile patients proactively, receive evidence‑based treatment recommendations, and trigger automated resource deployment for youths and their caregivers. By augmenting traditional screening with predictive analytics, the platform aims to identify at‑risk adolescents earlier and streamline care coordination across multidisciplinary teams.
Target Audience
Primary users are pediatricians, adolescent medicine specialists, and mental‑health clinicians who manage teen patients within integrated health systems.
Features
- AI algorithms that ingest structured EHR data and symptom inputs to produce individualized risk scores for mental and behavioral health conditions
- Real‑time integration with major EHR systems, delivering decision support alerts within the clinician’s existing workflow
- Automated care pathways that recommend specific interventions, referrals, and caregiver resources based on the risk profile
- Dashboard visualizations that track patient risk trajectories and highlight changes over time
- Compliance with healthcare data security standards to ensure patient privacy during data processing