Former ICU nurse. Product leader. Builder. I bring clinical fluency, strategic depth, and an AI-native approach to the hardest problems in healthcare technology — where workflows, policy, and product decisions actually intersect.
I'm a former ICU nurse turned product leader who builds at the intersection of healthcare workflows, technology, and policy. I'm AI-native — using LLMs and rapid prototyping to move from problem to solution fast — and I bring a clinical perspective that most product leaders simply can't offer.
Whether I'm shaping product strategy, aligning executive stakeholders, designing workflows that actually reflect how care is delivered, or standing up something new from scratch — I operate best at the edge where hard problems need someone who can think across clinical, technical, and business dimensions at once.
Navigating the intersection of strategy, regulation, and the realities of clinical and operational workflow is where the hardest product problems live. I've spent my career in that space — where a policy decision changes a clinical process, where a workflow assumption breaks an integration, and where the difference between a product that gets adopted and one that doesn't comes down to whether someone actually understood how care gets delivered before they started building.
ICU nursing shaped how I think about risk, urgency, and what actually matters at the point of care — instincts I bring to every product decision.
I use a range of AI tools for prototyping, research, ideation, and coding — and I know which ones to reach for when. This lets me compress discovery cycles and move from problem to validated direction faster than traditional product processes allow.
I translate operational pain into roadmaps, align stakeholders across levels, and deliver outcomes that are measurable and scalable — whether working with a founding team or a large enterprise.
The areas where I do my best work — across product, strategy, and the clinical-technical intersection.
Defining what to build and why — from 0-to-1 roadmaps to portfolio decisions grounded in clinical and operational reality, including how market intelligence can help health organizations make smarter decisions about where to grow and where to pull back.
Using LLMs, rapid prototyping, and AI-assisted workflows to compress discovery and move from problem to validated direction faster — without sacrificing clinical accuracy.
Bridging the gap between how care teams actually work and what gets built — turning clinical context into requirements, user stories, and product decisions that hold up in the real world.
Translating HL7, FHIR, and clinical data standards into product decisions — helping teams build integrations that actually work in complex health system environments.
Making sense of HIPAA, ONC requirements, CMS rules, and certification pathways — so product teams can build confidently without getting blindsided by compliance late in the cycle.
A snapshot of the work I've done to improve how health organizations use technology to deliver better care.
Most clinical registries were built around a single condition and never meant to talk to each other. The result: the same patient gets abstracted three different times by three different people. The vision here was a single longitudinal record that follows patients across the continuum — pulling from EHR and EMS data, eliminating redundant abstraction, and giving health systems a real picture of how specialty care is actually performing across trauma, stroke, cardiac, burn, and beyond.
When a trauma patient rolls into the ED, the care team is often working blind — no field data, no pre-hospital context. This platform changes that handoff. Pre-hospital ePCR data flows directly into hospital workflows before the patient arrives, and outcomes feed back to EMS so paramedics finally learn what happened after they left. At the system level, it turns arrival patterns, service-line volume, and referral flow into intelligence health systems can actually act on.
Clinicians are drowning in documentation — and most AI scribe tools solve the wrong half of the problem. Getting words on a page is easy. Getting a note a physician will actually sign without editing it into something unrecognizable is hard. This was 0-to-1 work on the full arc: what the output needed to look like to earn clinical trust, how to get providers past the skepticism fast enough to stick, and how to use LLMs to iterate on templates at the speed real clinical environments demand.
Enterprise EHRs are often a collection of products that were never designed to cohere. This was the work of making one actually hold together — aligning nursing, pharmacy, reporting, and interoperability into a platform with consistent clinical logic underneath. That meant real depth in FHIR, HL7, and USCDI, and taking a controlled substance prescribing module through a full DEA and NCPDP certification rewrite. Less about shipping features, more about building something clinical teams could stake patient care on.
Physician scheduling is genuinely hard — attendings, residents, and ED on-call coverage across multiple hospitals and clinics, all with different rules, constraints, and downstream dependencies. Getting it wrong means coverage gaps; getting it right means the schedule has to connect cleanly to payroll and reporting without manual reconciliation. Took this platform from a single-site deployment to 2,000 providers across a multi-facility health system, while building the integrations that made the schedule actually mean something operationally.
Whether you're building something new, solving a hard problem in healthcare tech, or looking to brainstorm with someone who thinks across clinical, product, and strategy — I'd love to connect.