Although many patients require continuous-use medication, there are many reasons why this fails to happen and patients stop taking the medication prescribed by their doctor.
We developed a model that measures and predicts the patients’ adherence degree to treatments for different drug classes. The data were treated anonymously, followed by the identification of segments and probability of remaining adherent to the treatment. In this way, the company can understand the factors that impact on the correct treatment prescribed by doctors and act in a preventive way, guiding doctors and their patients.
