Using AI to increase medication adherence

Abstract

Although different measures have been taken to increase medication adherence, it still remains a significant challenge with research indicating that the rates of non-adherence remain as high as 40 to 50%. Increasing medication adherence because non-adherence has a direct impact on patient outcomes. non-adherence contributes significantly to treatment failure. It also increases the rates of hospitalizations, mortality, and morbidity. Non-adherence also adds to healthcare costs affecting the ability of healthcare systems to provide the needed quality of care. Despite the implementation of traditional measures to increase adherence, these measures have led to mixed results. Most of these measures are limited because they rely on patient self-reports to measure adherence. They also do not verify whether a patient takes medication or not. Without verifying or confirming a patient has taken medication, it becomes significantly challenging to measure the rate of adherence. This necessitates the need for additional technologies to increase medication adherence. Leveraging technologies such as AI can help to address the limitations of traditional approaches to ensuring medication adherence. AI can be used to both predict adherence and improve adherence. However, to gain the full benefits offered by AI, it is important to address the challenges these technologies present such as ethical issues with regard to patient privacy and confidentiality of their data. The use of AI to increase medication adherence is also limited by limited knowledge and skills on how to use these technologies effectively and the type of technologies available. Therefore, this review explores how AI-based technologies can be used to increase medication adherence. Keywords: Medication adherence, non-adherence, Artificial Intelligence, patient outcomes, machine learning