Heart failure (HF) is a severe and costly clinical syndrome associated with increased healthcare costs and a high burden of mortality and morbidity. Although drug therapy is the mainstay of treatment for heart failure, non-adherence to prescribed therapies is common and is associated with worse health outcomes and increased hospitalizations. In this study, we propose a novel approach using Latent Markov models to analyze drug adherence to polytherapy over time using a secondary database. Our methodology enables us to evaluate patients' drug utilization behaviour, identify complex behavioural patterns, and incorporate them into predictive models to improve clinical outcomes. The significance of adhering to prescribed therapies for patients' prognosis has been highlighted in this study. Our findings show that adherent patients gained an additional ten months of life over a seven-year follow-up period compared to non-adherent patients. This underscores the importance of secondary prevention and continuous monitoring of heart failure patients. These procedures are crucial for identifying areas of improvement and promoting better adherence to prescribed therapies.

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