Advanced Probability - Bayes Theorem (I think)

Hello -
I am working on a statistics and modeling course where I am modeling the number of people that go bankrupt due to medical costs that have health insurance. I am seeking to answer the question what is the probability that I go bankrupt due to medical costs given that I have medical insurance.

Here is what I know (and these are functions, but for simplicity's sake I will use numbers)

P(B) = probability of going bankrupt 0.0027
P(I) = probability of being insured 0.667
P(not I) = probability of not being insured 0.154
P(M|B) = probability of declaring bankruptcy due to medical costs given that I have declared bankruptcy) = 0.621 (therefore not due to medical costs would be 1 - 0.621)
P(I | M) = probability of having insurance given that I have declared bankruptcy due to medical costs) = 0.78 (therefore not having insurance given that I have declared bankruptcy due to medical costs would be 1 - 0.78)

What I'd like to find is P(B | I). So, using Bayes Theorem, I setup:
P(B|I) = P(I|B)*P(B)/P(I)

But this is where I get stuck... because I can't seem to get rid of the M

Any help would be greatly appreciated!!