GenoScript

GenoScript leverages pharmacogenomic data and CPIC guidelines to support evidence-based medication selection, reducing trial-and-error and improving clinical outcomes.

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Inspiration

Pharmacogenomic (PGx) reports contain valuable genetic insights, but they are often delivered as dense, unstructured documents that are difficult for clinicians to quickly interpret. This creates a gap between available genetic data and real-world clinical decision-making. We were inspired to bridge this gap by building a tool that transforms complex PGx reports into actionable, evidence-based guidance. Our goal was to reduce trial-and-error prescribing and improve patient outcomes through personalized medicine. Genoscript is able to reduce the time and complexity for clinicians when looking at Pgx reports and prescribing medicine.

What it does

GenoScript is a clinical decision-support tool that:

  • Parses pharmacogenomic (PGx) reports into structured gene-phenotype data plain english
  • Stores patient data securely for future reference
  • Allows clinicians to input a medication (e.g., sertraline)
  • Uses CPIC guidelines to generate evidence-based drug recommendations
  • Displays risk levels using intuitive severity flags (red, yellow, green) This enables providers to make faster, more informed prescribing decisions tailored to each patient’s genetic profile.
  • Shows other alternative drugs that may be a better option that are effected by the same gene

How we built it

We built GenoScript using:

  • Next.js + TypeScript for the frontend and application logic
  • MongoDB for storing patient data and PGx results
  • CPIC Pharmacogenomics API to retrieve clinical guidelines
  • Gemini to parse unstructured PGx reports into structured gene-phenotype mappings

Challenges we ran into

  • Parsing unstructured PGx reports: Reports vary widely in format, requiring robust AI-based extraction prompts to standardize gene-phenotype data
  • Mapping to clinical guidelines: Ensuring accurate matching between patient phenotypes and CPIC recommendations required careful data handling
  • Balancing usability and clinical accuracy: We needed to present complex medical information in a way that is both precise and easy to interpret
  • Time constraints: Integrating multiple systems (AI parsing, database, API lookup, UI) within a hackathon timeframe required prioritization and rapid iteration

Accomplishments that we're proud of

  • Successfully built an end-to-end pharmacogenomics decision-support tool within a hackathon timeframe
  • Transformed unstructured PGx reports into structured, usable gene-phenotype data using AI
  • Integrated real clinical guidelines (CPIC) to generate evidence-based drug recommendations and alternatives
  • Designed an intuitive, color-coded system (red/yellow/green) to clearly communicate clinical risk
  • Created a scalable architecture combining frontend, database, AI parsing, and external APIs
  • Bridged the gap between complex genetic data and practical clinical decision-making
  • Delivered a polished, functional prototype that demonstrates real-world healthcare impact

What we learned

  • How to integrate AI-driven data extraction into a real clinical workflow
  • The importance of standardized medical data structures for interoperability
  • How to design user interfaces for healthcare professionals, where clarity and trust are critical
  • The potential of combining genomics + AI + decision support systems to improve patient care

What's next for GenoScript

  • Integration with electronic health records (EHR systems)
  • Real-time clinical validation and provider feedback loops
  • integrating real patient charts In for hospitals to be able to use the application

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