Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance. Issue 12 (December 2017) - Chang, Hsien-Yen; Richards, Thomas M.; Shermock, Kenneth M.; Elder Dalpoas, Stacy; J. Kan, Hong; Alexander, G. Caleb; Weiner, Jonathan P.; Kharrazi, Hadi
Abstract : Background: Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates—extracted by comparing electronic health record prescriptions and pharmacy claims fills—represent a novel measure of medication adherence and may improve the performance of risk adjustment models. Objective: We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization. Methods: We conducted a retrospective cohort study of 43, 097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0–7 days, primary 0–30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders. Results: The overall, primary 0–7, and 0–30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R 2 by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates. Conclusions: Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited. Abstract : Supplemental Digital Content is available in the text.