I've already used my entire dataset in a regression, should I not use that as a prediction model? At the hospital I work at we were writing a paper on what variables about a patient predict whether they'll return for a follow-up visit.  We included variables such as age, gender, distance from their home to the hospital, mechanism of injury and other things like that. We had about 600 patients to examine and so we ran a multiple logistic regression with yes/no return as the outcome, and we did this with everyone in our dataset (everyone with that condition at our hospital).
Well we wrote the paper and then someone decided we should try to create an online prediction tool. You could put in variables about a patient, and it would return a guess about whether or not the patient would return, based on our previous regression model. To help me create an online prediction tool I've used this tutorial using R and Shiny and I noticed the author split his data into training and testing sets
Problem is: I never did that. Reading comments such as this I think I understand why someone would split their data, but my question now is:
What can/should I do about it?

*

*I've already used all my data.  Would it be best to delete everything I've done, go back, split the data and start over?  (We didn't publish the paper or anything)

*Should I just proceed? Can an argument be made for NOT splitting the dataset?


With so few cases, train/test splits aren't helpful. You then lose power in training the model and precision in testing it.
What you've done so far is fine. You could go on to estimate how well the model is likely to work for prediction by repeating the modeling on multiple bootstrap samples of the data and evaluating performance of those models on the full data set. That's an accepted way to evaluate the performance of your modeling process.
One caution: "whether they'll return for a follow-up visit" might not be an all-or-none result. If you deliberately restricted consideration to returning during a fixed period of time like 1 year that could be OK, but in general you might also be interested in how soon they return and you might also want to take advantage of information from individuals who haven't yet been followed up for that fixed period of time. For those sorts of things you would need to use a survival model instead of logistic regression.
