From digitization to automatization
In the first section we started off with just a slight improvement of a fully physical experience in the real world by transferring it to the digital sphere. However, this was still a fully manual setup in which the doctor still had to address each and every patient request.
Ideally, we want to automate as much of this process as possible to help relieve the doctor's workload. For example, we can add a smart machine learning model to handle all the straightforward cases, so that the doctor only needs to handle edge cases.
The idea might seem obvious, but there are issues to address first: we don’t have the input data for training the model, and we need to choose the appropriate machine learning (ML) algorithm for this type of problem. The first issue we tackle by taking the "evolution" approach to product development: we start off with all the workload handled by the doctor, and have the model learn to replicate the doctor’s reasoning until its sample data size is large enough to train properly and make valid predictions. As for the second problem, we have good news: you don’t need to be an expert on machine learning to get started with it. You will simply install our Model Trainer service that uses Automated Machine Learning (AutoML) to kick-start your journey in the world of ML.
So let's get this thing going!