Methods and computational techniques for predicting adherence to treatment: a scoping review

Abstract Background Treatment non-adherence of patients stands as a major barrier to effectively manage chronic conditions. Treatment adherence can be described as the extent to which a patient’s behavior of taking medications follows the agreed recommendations from the healthcare provider. However, non-adherent behavior is estimated to affect up to 50% of patients with chronic conditions, leading to poorer health outcomes among patients, higher rates of hospitalization, and increased mortality. In fact, 200.000 premature deaths each year in the European Union are related to non-adherence. A promising approach to understand adherence behavior of patients represent artificial intelligence and computational techniques. These techniques can be especially useful in analyzing large amounts of heterogeneous patient data, identifying both inter and intra-relationships between factors and patterns associated with non-adherence. Objective This study offers a provision of a structured overview of the computational methods and techniques used to build predictive models of treatment adherence of patients. Methodology A scoping review was conducted, and the following databases were searched to identify relevant publications: PubMed, IEEE and Web of Science. The screening of publications consisted of two steps. First, the hits obtained from the search were independently screened and selected using an open-source machine learning (ML)-aided pipeline applying active learning: ASReview, Active learning for Systematic Reviews. Publications selected for further review were those highly prioritized by ASReview. Results 45 papers were selected into the second round of screening were reviewers performed the full-text screening. The final review included 29 papers. The findings suggest supervised learning (regression and classification) to be the most used analytical approach. Over 54% of adherence topics being related to chronic metabolic conditions such as diabetes, hypertension, and hyperlipidemia. Most assessed predictors were both treatment and socio-demographic and economic-related factors followed by condition-related factors. The selection of a particular computational technique was based on the research question, the type of data available and the desired outcome. A limitation of the reviewed studies is the lack of accountancy for interrelationships between different determinants of adherence behavior. Adherence behavior is a complex phenomenon that is influenced by multiple factors, and it is likely that these factors interact with one another in complex ways. Conclusion The creation of systems to accurately predict treatment adherence can pave the way for improved therapeutic outcomes, reduced healthcare costs and enabling personalized treatment plans. This paper can support to understand the efforts made in the field of modeling adherence-related factors. In particular, the results provide a structured overview of the computational methods and techniques used to build predictive models of treatment adherence of patients in order to guide future advancements in healthcare.