Improving adherence to medication in an intelligent environment using reinforcement learning

Correct and timely medication plays an important role in the treatment and recovery of a patient. Poor health outcomes are associated with the nonadherence to medication which also increases health care costs for patients and society. The medication process can be difficult for many patients such as dementia patients who face the challenge of forgetfulness in medication intake. Therefore, an intelligent and efficient patient engagement environment ensures enduring health and positive clinical outcomes. Such an intelligent system can be realized employing reinforcement learning (RL) which brought significant impacts in many areas, and has useful applications in healthcare as well. This work introduces an RL-based intelligent environment that can engage the patient to improve adherence, through proper engagement alerts based on user adherence reports, and suggest him/her to improve the adherence by sending the engagement message to the user. The intelligent RL agent decides the optimal decision to send the engagement message to the user based on a patient’s behavior. The proposed system could be useful to improve adherence to medication and assist the patient in accurately following his/her medication schedule. However, the proposed system would require integration with actual patient data and electronic health records before real-world deployment.