Your post is so relevant for my problem and your observations are highly appreciated and I will make sure to go through each one for my thesis (which is the project level for now)
Upon implementation, the clinic is very careful.
The model will be trained with new data each month and for with data from individual months to learn more about the seasonal behavior of the patients. Also, we are just testing this first on a small ambulatory where the business case is just for patients with booked operations with surgeons, nurses and cleaning personal not being able to use their time differently. Right now their intervention is calling every single patient 5 days before treatment to bring down the number (which works). It, unfortunately, costs them one nurse working full time on this. The idea is to work towards prediction that would make it worthwhile to just call patients that are
- Either NOT predicted as show-ups (which makes me think about your first point )
- Predicted as no-shows and live with the cost of missing a few patients.
If you have any other ideas for this, I would love to hear it
In other ambulatories, the no-shows is actually important for them to be able to not over-work, get stress and be able to meet their time schedule (Danish problem i guess)
The hope is that this can be implemented in other ambulatories(if successful) of the same hospital but fitted to their specific case and use.
The article you shared looks like something I need to learn about.
Thank you so much for your time and wonderful feedback!