Cubie Patient Lookup

In some cases, Cubie had no direct relationship with a patient's provider, so their health history was effectively a black box. In others, a relationship existed, but EMR access was inconsistent and often unreliable, with real connectivity issues just getting into the systems at all.

Either way, the care team was left relying on patient recall during limited appointment time, for an older cardiovascular population that often couldn't accurately remember their own medications, procedures or diagnoses. Time spent re-collecting information patients had already given a doctor somewhere else was time not spent on the actual care plan.

I worked closely with Cubie's Chief Medical Officer and clinical and ops staff to figure out what actually mattered. That input shaped both the pipeline and the interface: fast access to what's actionable now, without losing the full historical record that tells a patient's longer health story.

The users were Cubie's own clinical staff, and the fastest path to a useful tool was working directly with the people who set clinical priorities and the people who'd use it every day.

The obvious concern — storing real patient health data outside a purpose-built system — was already handled. Cubie's Google Workspace is HIPAA-compliant, so gating the app behind Workspace login delivered real access control for free. There was no login system to build and no auth to secure; that risk was covered by infrastructure Cubie already trusted.

Owning the front end meant owning the information hierarchy — what gets grouped, what stays collapsed, what a user sees first — none of which a spreadsheet would have let me control. And because the UI was decoupled from the data underneath it, the interface could be redesigned week to week without touching the pipeline.

Identification comes first, because acting on the wrong patient is the one unrecoverable error. Provider contact comes second, because it's the escape hatch when something doesn't add up.

Then the clinical record runs in descending order of how changeable it is during a call: medications can be adjusted today, diagnoses provide context, procedures are history. A reader moves from "who is this" to "what can I do about it" without ever backtracking.

The clearest proof that the information architecture worked came from the days it wasn't supposed to. When appointments ran back to back and there was no prep time at all, care team members pulled the app up mid-call and read from it live — unrehearsed, with a patient already on the line — and the calls still went well.

The tool kept evolving through direct, daily feedback from the people using it. The NPI lookup scrapes and caches provider info, so future lookups are instant and the corpus of known providers keeps growing.

Smaller changes mattered too. Medications, diagnoses and procedures were reordered newest-first, since a five-year-old diagnosis matters less than one from last month. Medication entries gained a computed doses-per-day, calculated from day supply and units prescribed — directly supporting adherence assessment for a population where missing a dose schedule has real cardiovascular consequences. And diagnoses, often cluttered with near-duplicate codes as conditions like hypertension or diabetes progress, got grouped under umbrella categories, with individual codes still available on expand.