Abstract
In this poster, we will present the BEAMER model, an emerging disease-agnostic model to improve adherence behaviour based on actionable factors and promote optimal health outcomes for all. Data on behavioural and structural factors affecting adherence to treatment have been collected from several sources and are being used to build the BEAMER model, where we aim to apply Machine Learning Modelling to forecast and diversify support towards more adherent behaviour. The BEAMER model can assist healthcare providers in eliciting patient needs to adopt targeted actions to support patients in improving their adherence. The collected data indicate that BEAMER must conform to standardised approaches to patient adherence to treatment. We anticipate that the BEAMER model will contribute to provide personalised patient support to drive adherence behaviour.
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