I'm a senior UX and product design consultant with over twenty years designing software for expert users in complex, high-stakes domains — healthcare revenue cycle, clinical applications, biomedical research data platforms, and scientific instrumentation. The work changes; the constraint doesn't: imprecision has a cost, and the users know exactly when they're absorbing it.
Most of my recent work has been in healthcare and health tech — RCM, clinical applications, behavioral health — where the design problems are genuinely hard and the user populations demanding. The same was true earlier at Flywheel, designing for neuroscience researchers managing multi-site clinical trials, and at Keysight, designing scientific software for RF and test engineers. The domain varies; the underlying condition doesn't: expert users with high tolerance for complexity, low tolerance for ambiguity, and real consequences when the software fails them.
The practical value of that depth is knowing what I'm looking at when I'm in a room with a billing coordinator, a research coordinator, or a clinical informaticist. Domain familiarity is what allows research with expert populations to be genuinely productive rather than polite — and it's what distinguishes direction given to AI tooling that produces domain-specific results from direction that produces a generic dashboard.