I tend to think of doctors as mechanics. Often when something breaks on my car, the explanation is "it's an old car, that happens eventually." And when they talk about it after fixing it, it's usually "Toyota's a good brand. Take care of it and you'll get another 100k miles from that engine." If I took my car to my mechanic with nothing wrong and asked what I should be doing to make sure the transmission lasts as long as possible, they will give me some generic intuitive advice, but they have no real insight because they are not in the business of maximizing the lifetime of healthy cars.

Doctors are kind of the same way. They see patients who have problems and need them fixed. If you walk into a doctor's office and say your family has a history of heart disease and you're concerned, they'll run a standard diagnostic to see if there is a problem. If there isn't, they'll give you generic advice. Outside of that, they are not really likely to have much insight into how to prevent heart disease in your specific case. Maybe you'll get lucky and the doctor will have recently read a relevant paper.

This seems to me like a problem that you're better off having answered by a medical researcher rather than a physician in the trenches. Researchers are the ones who are taking the long view on outcomes in patients. Keeping with the same analogy, you're probably better off talking to a mechanical engineer at a car company about maximizing the life of your currently healthy transmission.

Also, I generally see this as a problem that will be best addressed by machine learning researchers collaborating with medical researchers. Then again, I'm an ML PhD student working on health applications, so I'm biased. :)