Ok, so I am running a classic linear regression where betahat = (X'X)^-1X'y

Due to performance issues, I would like to estimate betahat with an additional data point (x1,x2,x3,x4,...,y) without recalculating based on the whole history. 

Could I do some sort of multiplication based on Xmu or std deviation of the variables? And then what is the probability that betahat is predicting the true beta from this point? This would be used so that I can judge when I need to do a full recalculation.

Or am I thinking about this entirely wrong?

Thank you for any help or suggestions,
Jeremy