Smart medication management system and multiple interventions for medication adherence
To keep healthcare costs under control, a high-level of medication adherence, or compliance with medication regimen, must be achieved. The multifaceted nature of medication adherence, due to a large number of underlying factors, presents several critical challenges including how to evaluate the current level of adherence, how to improve and how to maintain the required level of medication adherence, especially for long-term chronic conditions. Several interventions to improve adherence have been proposed in the healthcare literature, however these are complex, costly and difficult to implement. It is also not clear which ones would be effective at what levels of adherence in what conditions and types of patients. Therefore, there is a need to model, evaluate and compare the interventions individually as well as in combinations for their impact on medication adherence. To address this, the design and evaluation of smart medication management system (SMMS) for improving medication adherence are presented in this paper. We also present an analytical model for evaluating the medication adherence using multiple interventions that are supported from SMMS, namely context-aware reminders, improved scheduling of medications, and support from healthcare professionals. The performance results show that very high medication adherence is achievable by SMMS for single and multiple medications even for patients with mild cognitive deficiency. Several powerful composite interventions are also proposed and evaluated for medication adherence. It is also shown that the total healthcare savings are significant even for slightly improved medication adherence. With higher hospitalization cost, the savings due to improved medication adherence become even more significant. The proposed work forms the basis to design personalized interventions to patients for improving medication adherence in different surroundings.
interventions medication adherence smart systems modeling and performance health monitoring