Local Dental Hub
Closing the "in-network" trust gap between patients and dental clinics
Most patients search for a dentist the same way: check their insurance portal for "in-network" providers, pick one nearby, and book. The catch — insurance portals and clinic websites are frequently out of date. A clinic can drop out of network while the insurance company's own directory still lists them as covered, and patients don't find out until they're in the waiting room, or after the bill arrives.
Existing tools — Google Maps, generic directories, even the insurance company's own site — can tell you where a clinic is. None of them can tell you, reliably, whether you can actually afford to walk in.
Why this Isn't Just a Directory App
Early on, the plan was a filterable directory — sort by insurance type, specialty, distance. But a key constraint reshaped the approach: Google Places API, the data source for clinic locations, has no reliable field for insurance participation. No API does, because network status changes clinic by clinic and isn't public structured data anywhere.
That constraint became the actual design insight: don't automate what can't be automated. Design around the one verification method that's always accurate — a direct phone call — and make that step as fast as possible.
The Core Flow
Discover — Directory & Map
Patients browse a searchable directory or a full map view, sortable by relevance or distance.
Verify — Call to Verify
Every clinic profile leads with distinct actions — Call, Call to Verify, Request Appointment — deliberately separated so patients know exactly why they're dialing.
Trust — Claim This Clinic
Clinics can claim their listing and update their own information, keeping data fresher than a static directory could manage alone.
Save — Favorites
Once a patient calls and confirms a clinic is in-network, they save it — turning a search result into a verified shortlist.





Design Decisions
Separating "Call" from "Call to Verify" — a single generic contact button would have been simpler to build, but it erases the point. Naming the action tells the patient what to ask before they dial.
Favorites sits after the verify step, not before — so a saved clinic means something confirmed, not just something noticed.
Letting clinics claim their own listing shifts data upkeep to the party with the most accurate information and the clearest reason to keep it current.
Known Gaps & Next Steps
The incentive for clinics to claim a listing is still weak. Yelp and ZocDoc solve this with visibility and booking volume; this app has no equivalent hook yet. It's the main open question before launch.
The Google Places data pull needs a cleanup pass — some non-dental businesses surfaced in early results.
Favorites has no "last verified" date. Network status changes, so a clinic confirmed six months ago isn't the same as one confirmed yesterday. That's the next feature I'd build.

Hart Studio - Identity, self-initiated
Low-poly anatomical heart paired with a gradient wordmark; the color transition
carries from the mark into the letterforms so the two read as one object.

Selected through an open competition and adopted as the organization’s official mark. Each figure is formed from the letters M and M — MMGH — repeated around a circle, so the whole reads as one community before it resolves into twelve individuals. In use at mmgh.org.

Hepatology textbook, Elsevier — published figure
Gut–liver axis across four stages of disease progression, built from source material worked through
with the authoring physicians, then revised through the publisher’s review. The challenge was sequencing:
one frame has to read left-to-right as a timeline and top-to-bottom as an anatomy,
at a glance, for readers who won’t stop to decode it.
Heart (BMJ) — published figure
Drawn for a peer-reviewed review article on congenitally corrected transposition of the great arteries.
Two coronary arrangements compared side by side with a shared orientation key, so the reader holds both in the same spatial frame.
The vessels under discussion carry color while the adjacent valve stays grey — the reader knows where to look before reading a label.
Modelled and annotated in Maya, published as a live viewer. Rotate and zoom freely;
the numbered points open short descriptions of each structure — so the reader explores the anatomy
at their own pace rather than being walked through it in a fixed order.
About Me
Research-driven design, grounded in real-world experience
I bring a design-and-research background developed across some of the top academic and research institutions in the US.
Design & Illustration
I've designed visual materials for websites, research recruitment campaigns, and journal publications, including creating the logo for CVAi2 at Johns Hopkins. This work sharpened my eye for clarity, hierarchy, and communicating complex information visually — skills that carry directly into interface design.
Research
For four years, I worked in healthcare and behavioral research roles at UTMB, Baylor College of Medicine, and Johns Hopkins Medicine — work centered on interviewing people, interpreting behavioral data, and translating findings into clear, actionable insights. That process — understanding how people actually think and behave, then designing around it — is the same core discipline UX research is built on.
Education
MFA, Medical Illustration & Biomedical Visualization — Rochester Institute of Technology
Graduate coursework in Human-Computer Interaction Design (~40 credits) — Harrisburg University of Science and Technology
Coursework in Graphic Design — Monroe Community College, Rochester, NY
I'm currently based in Tampa, FL, looking for UX roles where research and design sit close together.
Get In Touch!
fuadzain@gmail.com
Tampa, FL 33647


