I used a Monte Carlo simulation to pick my health insurance plan
We have to pick a new health insurance plan this month, and we've had a tough time making the decision.
You can't just add up what you'll spend – what each thing costs depends on how much you've already spent!
And some things are inherently probabilistic – will I go through procedure X this year? How many visits will I need for condition Y? How many urgent care visits?
So complex and uncertain!
Inspired by vaguely recalling that I read Lucas F. Costa's blog post some time ago, I applied the Monte Carlo method to my health insurance decision.
I have a simplistic understanding of Monte Carlo simulations:
- Assign probabilities to everything that can happen in your scenario
- Randomly selecting outcomes for each possible event, then repeat the calculations a gazillion times
- Measure how things typically play out
It can get much fancier (hello, MCMC!) but I think that's the gist of it.
I put together a simple TypeScript file with some arithmetic operations and calls to
Math.random() and ran it with Bun. I punched in all the reasons my wife and I will or might spend on healthcare, added in the premiums, and took the average result.
Surprisingly, the expensive plan will save us a couple thousand dollars this year, even accounting for the higher premiums.
I feel better about the decision since I did something resembling rigorous calculation of which plan is best. Usually I just guesstimate and anxiously hope for the best.