Lumos is a free-to-use tool designed to help you find the ACA health insurance plan that's right for you. Our product finds plans, personalized to you, with the lowest "true-cost" in a matter of minutes. Shop smarter with Lumos and save hundreds of dollars each year.
@shushant_lakhyani
Thanks for the comment and appreciate the support!
When people shop for insurance, they care about many different factors but the primary ones are cost and coverage. While existing solutions do a decent job of helping users understand what doctors / drugs a certain plan will cover (although there's room for improvement), they don't do a good job clarifying the true-cost of an insurance plan.
What sets us apart is that our recommendations are built on data-driven estimates of the true-cost of the plan (premiums + out_of_pocket). This lets the user shop with a clear picture of how much they can expect to pay, something that doesn't exist today.
Most of the existing solutions let you browse and compare the ~200 plans that a typical user is eligible for on the marketplace, but a side-by-side comparison of the 15+ cost dimensions of a plan doesn't do much towards helping you determine how much you could actually end up paying and the associated risk.
We use data to help users estimate the true-cost of the plan (i.e. premiums and out-of-pocket expenses). Premiums are fixed but out-of-pocket costs are unknown ahead of time. In order to estimate out-of-pocket costs, you need to know how much in healthcare you could end up using in the coming year, how much each of the services cost, and how likely each scenario is.
We use the healthcare usage experience of hundreds of Americans like you (along basic demographic dimensions) and simulate cost outcomes on all your eligible plans to generate a probabilistic cost distribution and the associated expected value as our true-cost estimate.
The experience boils down all of this computation and data into a single number and couple of charts giving you a clear picture of the true cost of the plans that we show / recommend.
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