Caregap Navigator

AI-powered MCP server that identifies missed care and generates personalized outreach based on real patient barriers live in the Prompt Opinion Marketplace.

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CareGap Navigator — Project Story

Preventive care gaps remain one of the most persistent and costly problems in healthcare. Many patients miss essential screenings—not because they are unwilling, but because outreach systems fail to account for real-world barriers such as language, transportation, and prior engagement patterns.

CareGap Navigator was built to address this gap by moving beyond detection and into personalized, actionable outreach.

Inspiration

Most existing systems stop at identifying that a patient is overdue.

However, the more important question is:

How do you reach this specific patient in a way that actually works?

For example, a Spanish-speaking patient with transportation challenges requires a completely different approach than a patient with no barriers. This gap between detection and effective engagement inspired the project.

How It Works

CareGap Navigator is implemented as an MCP (Model Context Protocol) server integrated into the Prompt Opinion platform.

It exposes three core tools:

  • get_care_gaps — identifies overdue preventive screenings
  • score_barrier_risk — evaluates barriers such as language, transport, and missed appointments
  • generate_outreach_draft — generates:
    • personalized SMS (in the patient’s preferred language)
    • a short phone script
    • reasoning behind the outreach strategy

The system uses structured patient data and generative AI to produce context-aware communication tailored to each patient.

Why AI Matters

Traditional systems detect problems but cannot adapt to human context.

CareGap Navigator uses AI to optimize outreach effectiveness:

$$ \text{Effective Outreach} = f(\text{Care Gap}, \text{Patient Barriers}, \text{Channel}) $$

This transforms generic reminders into personalized engagement strategies.

Challenges

  • Debugging MCP integration and aligning request formats
  • Resolving deployment issues caused by file-based storage in a cloud environment
  • Ensuring AI-generated outputs were structured, realistic, and consistent

What I Learned

  • MCP enables modular, reusable AI capabilities
  • Generative AI is most effective when combined with structured logic
  • Reliability and simplicity are critical for real-world systems

Impact

By generating barrier-aware outreach, CareGap Navigator can improve patient engagement and help close preventive care gaps at scale.

Because it is built as an MCP server, it can be reused across multiple agents and healthcare workflows.

Data Safety

All patient data used in this project is fully synthetic. No real patient data (PHI) was used.

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