Biomarker Dashboard

Private practice and concierge medicine have been growing steadily as patients look for more personalized, preventative care. Rather than treating illness after it appears, this shift — sometimes called Medicine 3.0 — is focused on optimizing health before problems develop.

The practice was already ordering far more comprehensive panels than a standard annual physical — the kind of testing that produces dozens of markers per draw rather than a handful. Interpreting these results, patient by patient, was time-consuming and was ultimately reduced to focusing on critical issues, rather than the healthspan and performance optimization problem that the clinic set out to solve.

More data made the underlying gap worse, not better: every additional biomarker was one more number reported against a range that wasn't built for the question being asked.

The doctor persona was important because a feature that helped patients but cost the clinician time in an appointment that was already too short wasn't going to work.

Mapping emotional states across each touchpoint found the low point: a patient has numbers, no interpretation, and a year until anyone looks at them again. That got the team aligned on where the product's value actually had to sit.

Results focused on optimization

Display biomarkers with four distinct ranges (abnormal, borderline, normal, optimal) instead of a binary normal/abnormal, with personalized explanations of what each result means for the patient's demographics, health goals and risk factors.

Integrate educational content

Explanations that scale in complexity by user type, from basic explanations for busy parents to detailed correlations for performance optimizers.

Actionable recommendations

Specific, prioritized recommendations (lifestyle, supplement, medication, exercise) based on individual biomarker gaps, with clear rationale for why each will move specific lab values and overall health goals.

Doctor's notes

On top of the generated recommendations, the doctor and clinical team can add and edit their point of view for that patient.

Data in the right context

A single result is nearly meaningless on its own; the same value can be good or bad news depending on where the patient came from. Every biomarker carries its history, banded by the same four categories, so a patient who has moved from borderline to normal can see that they're winning — most of what keeps someone doing the work between appointments.

Predictive health analytics

Trend analysis that shows projected trajectories as well as history, enabling early intervention before biomarkers move into concerning ranges.

Visualization on multiple time horizons

Flexible views — three months for hormonal cycles, six for lifestyle interventions — so each persona sees change in the context that fits their goals.

Four categories, not two

Abnormal, borderline, normal, optimal. Two categories answer a yes/no question about disease. Four answer a directional one: where am I, which way am I moving, and how far is the target. That's the question a preventative practice is actually asking, and the software had never been built to answer it.

Where the ranges come from

Abnormal is easy: it's the lab's own reference range, and the product doesn't second-guess it. Everything inside that range is where the product makes a claim the lab isn't. Those boundaries had to be defined per biomarker — some markers are two-sided, some one-sided, and several vary by sex, age and a patient's goals — which made this a clinical design problem long before it was a UI.

Clinician stays in the loop

Drawing a narrower band than the lab does is a real claim, and the product had to be honest about whose claim it was. The categorization is presented as the practice's position — not an independent diagnosis and not a replacement for the clinician's read. That's why the doctor keeps edit rights everywhere downstream.

That rule is unglamorous and it settles arguments fast. Digestible reports, the four-category system, trends and improvement plans all cleared it: every archetype, patient and doctor alike, was blocked by their absence. AI coaching, interface personalization and a branded Health Score didn't. They were good ideas that served some personas much more than others, which makes them second-release ideas, not bad ones.

The Health Score is the interesting deferral. It's the most marketable feature in the set and the most dangerous to ship early: a single composite number is exactly the kind of reductive summary the product exists to argue against, and rolling it out before the four-category model had earned patients' trust would have undercut the thing that made the product different.

Wireframes and an IA diagram came first, and the IA earned its keep more than the wireframes did: agreeing on how results, reports, biomarkers and plans related to one another settled naming and hierarchy questions that would have been expensive to discover later in visual design.

Interactive prototypes covered the core workflows before any of it was committed to final visual design. A fresh testing pool would have told us whether the interface was usable. This pool could tell us whether it was the right product.