MediGuard AI

Real-time medication safety alerts personalized to your prescriptions, using live FDA, PubMed, and medical web signals with source-linked, plain-language guidance.

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Inspiration

Medication safety information exists, but it rarely reaches patients in a personalized and timely way.

Every year, millions of people are affected by medication-related harm, while important safety updates are buried across FDA communications, research publications, and fragmented medical web sources. As a result, patients and caregivers often miss the one update that actually matters to their own prescription list.

We built MediGuard AI to close that gap: real-time medication safety intelligence that is personal, source-linked, and understandable.

What it does

MediGuard AI monitors live safety signals and turns them into actionable, plain-language alerts tailored to each user’s medications.

Users add medications once, then MediGuard:

  • Crawls live sources (FDA, PubMed, and medical web signals)
  • Matches findings to the user’s medication list
  • Scores relevance/confidence and filters low-signal noise
  • Deduplicates repeated alerts
  • Pushes updates in real-time to the dashboard
  • Sends critical alerts via email
  • Supports caregiver read-only access via secure link

Every alert includes a traceable source URL for transparency.

How we built it

We built MediGuard AI with:

  • Next.js 14 + TypeScript (App Router)
  • Supabase (Auth, PostgreSQL, RLS, Realtime)
  • Nimble API (live web extraction/search)
  • Anthropic Claude (safety analysis + plain-language summarization)
  • Resend (critical email notifications)
  • Tailwind + Recharts (UI + visualization)
  • Three.js (health-themed animated background experience)

Core pipeline

  1. Collect live health content from external sources
  2. Analyze relevance against user medication profile
  3. Filter by confidence threshold
  4. Deduplicate recurring signals
  5. Persist alerts to database
  6. Push realtime updates and send critical notifications

Challenges we ran into

  • Balancing live-data freshness with demo reliability
  • Reducing irrelevant/noisy alerts while preserving important signals
  • Keeping medical communication understandable without sounding diagnostic
  • Making a rich UI visually engaging while maintaining readability and trust

What we learned

  • In healthcare UX, clarity and trust matter as much as model quality
  • Source transparency is essential for user confidence in AI-assisted outputs
  • Real-time systems need strong fallback strategies for stable demos
  • Product storytelling strongly affects how technical work is evaluated

Accomplishments that we're proud of

  • End-to-end production workflow from live signals to personalized alerts
  • Realtime alert dashboard with caregiver sharing support
  • Critical notification channel (email) integrated into workflow
  • Polished, health-themed UI and demo-ready experience

What's next

  • Better risk stratification and explainability per alert
  • Expanded caregiver and provider collaboration workflows
  • Trend and longitudinal monitoring features
  • More integrations and compliance-ready roadmap

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