Adding medication adherence indices to demographic models improved next-year hospitalization forecasting (AUC increased from 0.605 to 0.656), though improvements were minimal for advanced models.
Cohort
Does adding medication adherence indices improve predictive models of cost and hospitalization in non-elderly patients?
Medication adherence measures can modestly improve EHR- and claims-derived predictive models of cost and hospitalization, though benefits are minimal when added to advanced diagnosis-based models.
Absolute Event Rate: 0.656% vs 0.605%
Multiple indices are available to measure medication adherence behaviors. Medication adherence measures, however, have rarely been extracted from electronic health records (EHRs) for population-level risk predictions. This study assessed the value of medication adherence indices in improving predictive models of cost and hospitalization. This study included a 2-year retrospective cohort of patients younger than age 65 years with linked EHR and insurance claims data. Three medication adherence measures were calculated: medication regimen complexity index (MRCI), medication possession ratio (MPR), and prescription fill rate (PFR). The authors examined the effects of adding these measures to 3 predictive models of utilization: a demographics model, a conventional model (Charlson index), and an advanced diagnosis-based model. Models were trained using EHR and claims data. The study population had an overall MRCI, MPR, and PFR of 14.6 ± 17.8, .624 ± .310, and .810 ± .270, respectively. Adding MRCI and MPR to the demographic and the morbidity models using claims data improved forecasting of next-year hospitalization substantially (eg, AUC of the demographic model increased from .605 to .656 using MRCI). Nonetheless, such boosting effects were attenuated for the advanced diagnosis-based models. Although EHR models performed inferior to claims models, adding adherence indices improved EHR model performances at a larger scale (eg, adding MRCI increased AUC by 4.4% for the Charlson model using EHR data compared to 3.8% using claims). This study shows that medication adherence measures can modestly improve EHR- and claims-derived predictive models of cost and hospitalization in non-elderly patients; however, the improvements are minimal for advanced diagnosis-based models.
Kharrazi et al. (Fri,) reported a cohort. Medication adherence indices (MRCI, MPR, PFR) vs. Predictive models without medication adherence indices was evaluated on Next-year hospitalization forecasting (AUC). Adding medication adherence indices to demographic models improved next-year hospitalization forecasting (AUC increased from 0.605 to 0.656), though improvements were minimal for advanced models.