NearHealth is an early-stage patient platform that helps people navigate care, medications, insurance coverage, claims, and other healthcare needs through an AI-powered assistant.
Over a three-month engagement, I transformed a broad product vision into a cohesive patient experience, defined how conversation and structured interfaces should work together, and created a foundation for continued development.
Healthcare information is fragmented across portals, insurers, providers, pharmacies, and plan documents. Patients often know what they need help with, but not where to find it or which healthcare terminology to use.
NearHealth envisioned an AI assistant that could become a central starting point for these needs.
How might we make healthcare navigation feel conversational while preserving the clarity, control, and reliability patients need?
Strategizing the MVP
I consolidated 30+ proposed workflows across several healthcare domains, removing outdated and duplicate flows, and reorganized the experience around patient intent, reusable behaviors, and priority MVP journeys.
I inherited:
Mid-fidelity screens and partial flows created at different stages of the product
An assistant-first vision without fully defined clarification, fallback, escalation, and status behavior
A broad product scope that needed to be organized into prioritized patient journeys and an achievable MVP
Working closely with the stakeholders, I refined the MVP feature list given their timeline and scope.
This work established repeatable interaction models:
Patient intent → clarification when needed → relevant information or action → structured result → follow-up or fallback
Continuing to apply my pattern thinking so that engineering would have replicable models for each flow, I defined models that each feature could be categorized into:
Scheduling, Information Retrieval, Request/Submission, and Troubleshooting.
Conversation for guidance
The assistant helps patients express their needs, understand unfamiliar information, and reach the right part of the product.
Structured UI for action
Cards, details, and status views make healthcare information easier to compare, verify, and act on.
Consistent behavior
Shared patterns define how the assistant clarifies requests, communicates missing information, shows progress, confirms actions, and recovers when it cannot help.
Connected product experience
The same model extends across care, medications, coverage, claims, insurance information, pharmacy details, notifications, and settings.
From concept to implementation
An interactive Figma Make prototype demonstrated how conversation, structured results, voice, and manual navigation worked together. User stories, edge cases, MVP recommendations, and engineering guidance documented how the experience should behave.
By the end of the engagement, NearHealth had a cohesive experience framework spanning multiple healthcare needs.
I translated the initial AI vision into prioritized patient journeys, defined how conversation and structured interfaces should work together, and created reusable patterns for system behavior and recovery.
An interactive prototype, MVP recommendations, and implementation guidance gave the team a clear foundation for continued development.
This project reinforced that conversational AI cannot be handed off as a series of chat screens.
The quality of the experience depends on shared definitions for system behavior, confidence, data availability, recovery, and ownership of unresolved product decisions.
My role was not simply to visualize an AI assistant. It was to define how the product should respond, guide, clarify, recover, and earn trust across complex healthcare tasks.