Most of this makes sense to me, except "We will provide the respondents up to 2 options to choose from 14 available drug profiles." I don't know what that sentence means.

So your attributes and levels describe patient variables, right?

And you plan to show 12 patient profiles per respondent? So far so good.

And there are 14 levels in your dependent variable? If so that spreads the sample quite thin, because you'll be estimating 11 x 13 =143 parameters on just 12 x 150 = 1,800 observations. The standard reference for sample size in logit models (Peduzzi et al 1996) recommends a minimum number of observations for models like this is 10 times the number of parameters divided by the (minimum) likelihood of choice. Likelihood of choice is, AT BEST 100% divided by the number of levels of the dependent variable, so it's 1/14 or 0.0714. Plugging that into the equation would suggest that you need 10 x 143 / .0714 = 20,020 observations, not the 1,800 that you have. With such a small number of observations relative to what you need, you may have utilities that do all sorts of strange things. If you can cut down on the number of levels in your DV (perhaps by collapsing across categories of drugs into a smaller number of levels) that could help quite a bit.

That's just for an aggregate unconditional logit model. Because of the sparse data problem these models are usually NOT run at the individual level and you have more reason than most not to do so here.