A virtual cardiometabolic care platform combining patient experience design, recurring revenue infrastructure, and clinically grounded AI. I led key parts of the patient journey across onboarding, engagement, billing, and Ask FlyteHealth — an EMR-integrated assistant designed for real clinical use.
FlyteHealth sat at the intersection of consumer product expectations and real clinical complexity. Patients needed a clear, supportive experience. Care teams needed workflows that actually fit longitudinal care. The business needed a platform that could support recurring revenue and measurable outcomes.
The challenge was not simply building a better app. It was designing a connected system that could support onboarding, engagement, billing, and care guidance in a way that felt coherent to patients and operationally durable to the organization.
That same systems mindset shaped the AI work. In a clinical setting, generic chat behavior is not enough. Ask FlyteHealth had to be grounded in real patient context, constrained appropriately, and designed to earn trust rather than improvise through it.
Rather than treat engagement, billing, and AI as separate initiatives, the work was to make them reinforce one another inside a single patient and care-team experience.
Ask FlyteHealth was not positioned as a generic chatbot. It was built as an EMR-integrated patient support layer that could respond using chart context, longitudinal data, and curated content rather than broad, unbounded health advice.
The assistant supported use cases across onboarding, nutrition coaching, exercise guidance, medication adherence, blood pressure monitoring, and personalized care plan generation. That breadth made architecture and controls central to the product strategy.
The core product challenge was balancing usefulness with discipline: keeping responses specific enough to help, while ensuring the system stayed grounded in approved sources and appropriate framing.
Responses were grounded in medications, vitals, provider instructions, longitudinal patient context, and curated clinical content — not just generic health knowledge.
The system was framed as a production capability, not a demo artifact: architecture, auditability, and safe iteration were treated as product requirements from the start.
The Ask FlyteHealth architecture pulled from three sources: the EMR, the FlyteHealth data lake, and a curated content library. Those inputs were routed through a retrieval-grounded generation pipeline designed to make patient-facing responses more specific and less fragile.
The point was not to showcase AI sophistication for its own sake. It was to build a system whose outputs could be reasoned about, improved safely, and deployed inside a real healthcare experience.
Available reported outcomes include 86% adherence, 75 NPS, 13–16% average weight loss in one year, 12% decrease in A1c, 6% decrease in LDL cholesterol, and support across 200+ health conditions.
This was not a narrow feature ownership story. It reflects end-to-end product leadership across patient workflows, payment architecture, and the emerging AI layer inside a healthcare platform.
Ask FlyteHealth shows experience moving beyond generic AI claims toward grounded productization: architecture choices, risk controls, review processes, and a clear relationship to the underlying care model.
The best healthcare experiences do not avoid operational and regulatory complexity. They absorb it and turn it into clarity for patients.
Recurring revenue systems are not back-office details. In care delivery, they are part of the emotional and practical first impression of the product.
Reliable healthcare AI depends on retrieval, validation, and explicit constraints — not on hoping the model behaves. Product strategy has to include those controls from the beginning.