Predicting Patient Behaviour and Adherence

Understanding an individual's likely adherence, engagement, and behaviour is essential for achieving optimal treatment and intervention outcomes, in addition to knowing their health status and disease risks. This often requires analysing quickly growing and heterogeneous health-relevant data, particularly real-world data like electronic health records (EHRs).

Quantum AI and machine learning (ML) algorithms are particularly suitable for treatment and intervention use-cases, including predicting behaviour. For example, Quantum Support Vector Classifiers (QSVCs) were used to predict the medication persistence of individuals with rheumatoid arthritis based on their EHRs.

Understanding patient behaviour such as medication persistence, could support tailored treatments and interventions, and help individuals achieve optimal treatment outcomes.