Coverage Compass
Decision-support engine for Medicare tradeoffs. Built to surface uncertainty, switching asymmetry, and consequences.
Why traditional tools fail
Most Medicare comparison tools frame plan selection like shopping: list benefits, compare premiums and check a few boxes.
That framing fails because the real decision is about total cost, provider access, and drug risk across time — not just the monthly premium.
A University of Michigan analysis on Medicare plan selection (reported by ScienceDaily) notes that consumers need to compare total costs, provider networks and drug coverage rather than just premiums. Yet only 33% of Medicare beneficiaries use online tools to explore their options, despite having more than ten Medicare Advantage plans plus multiple Part D and Medigap options.
When beneficiaries choose plans based solely on low premiums, they often underestimate future out‑of‑pocket costs or network restrictions. Switching later is difficult: in all but four states, beneficiaries who try to switch from a Medicare Advantage plan back to traditional Medicare may be denied a Medigap policy if they have a pre‑existing condition. Researchers from the Center for Retirement Research describe this as the “one‑way‑street” problem: it’s easy to switch from Medigap to Medicare Advantage, but hard to go back because insurers can reject applicants after the initial enrollment period.
Reframing the decision
Coverage Compass reframes the choice from “Which plan has the lowest premium?” to “What happens if I’m wrong?”
It surfaces downside risk: lock‑in, switching asymmetry, network constraints, and the administrative friction that appears when health needs change (specialists, prior authorizations, out‑of‑network surprises).
By highlighting these asymmetric consequences and encouraging counter‑factual thinking, the tool helps users make more resilient decisions instead of optimizing only the happy path.
System design choices
- Failure modes as UX: the interface shows uncertainty, risk ranges, and what could go wrong instead of hiding complexity.
- Drivers‑first explanation: recommendations are paired with the handful of variables that drive the outcome (e.g., drug burden, specialist needs, network dependence).
- Designed for high‑stakes users: plain language, mobile‑first scanning, and local‑only processing to protect privacy.
Hiring signal
This case demonstrates rethinking problem framing and building explainable decision‑support systems for high‑stakes, messy decisions.
It’s directly relevant anywhere users face uncertainty, asymmetric risk, and irreversible downside: healthcare, finance, compliance, safety, and policy‑heavy tools.
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