The Medication Adherence Score: A Predictive Analytic Tool
Abstract Background Poor medication adherence is wide-spread and associated with poor clinical outcomes. Herein, we introduce the Medication Adherence Score, a predictive analytic tool designed to provide clinicians insight into adherence behavior over the subsequent twelve months. The aim of the study was to demonstrate the feasibility of such scoring of patients at the individual level. Methods This is a single arm, non-randomized, 2-center, retrospective cohort study conducted among patients diagnosed with atrial fibrillation. The model, developed by Fair Isaac Corporation on pharmacy refill data, predicts adherence behavior to cardiovascular drugs using demographic, geographic and socio-economic predictors. The primary outcome was the number of patients that could be scored at the individual level without reliance on past individual refill behavior. The score was normalized between zero (lowest adherence score) and one (highest adherence score) and patients were grouped: low adherence < 0.6, intermediate adherence between 0.6 and 0.8, high adherence > 0.8. The institutional review board approved the study. Results A total of 1110 patients were included in the study with a median age of 71 (IQR 63, 79). Most patients (807, 73%) could be scored at the patient level, and the remaining patients (303, 27%) were scored based on characteristics associated with the geography of their home address. There were 488 patients (44%) with a high adherence score (score > 0.8), 382 (34%) with an intermediate adherence score (score between 0.6 and 0.8) and 240 patients (22%) with a low score. Younger patients had on average lower scores than older patients, and males also had higher scores. Conclusions The Medication Adherence Score was successfully applied to an unselected group of atrial fibrillation patients: nearly a quarter of the cohort were identified as at risk for non-adherence. Future studies are necessary to assess the association of this predictive analytic model with clinical outcomes.