Not There Yet: Using Data‐Driven Methods to Predict Who Becomes Costly Among Low‐Cost Patients with Type 2 Diabetes

Abstract

Research ObjectiveDiabetes is a leading cause of Medicare spending, and predicting which individuals are likely to have high health care costs is essential for targeting interventions. Current approaches to predicting spending generally focus on composite measures, short time horizons, or patients who are already high utilizers and, therefore, whose costs may be harder to modify. In contrast, dynamic approaches that model monthly spending over time may help discriminate between low‐cost patients with diabetes who do and do not subsequently become costly.Study DesignWe identified Medicare beneficiaries with type 2 diabetes whose spending was in the bottom 90% of spending on diabetes care during a one‐year baseline period, a threshold for low‐cost patients used in prior studies. We used group‐based trajectory modeling, a data‐driven method, to classify unique clusters of these low‐cost patients by their diabetes spending patterns over the subsequent two‐year follow‐up period. Prediction models for these spending groups were then estimated with generalized boosted regression, a nonparametric machine learning method, using sets of all baseline predictors, diabetes predictors, and predictors that are potentially‐modifiable through interventions. For clinical context, we explored the relative influence of each predictor from the regression models to provide insight into baseline factors that may help distinguish patients who may become costly later. We evaluated each model through discrimination (C‐statistic) measures and split‐sample validation.Population StudiedWe identified Medicare fee‐for‐service beneficiaries with type 2 diabetes from a random, nationwide sample from 2011 to 2014 and used their enrollment, medical, and prescription claims data.Principal FindingsBaseline median diabetes spending for the 33 789 Medicare beneficiaries classified as low cost was $4153. We identified five distinct diabetes spending patterns among these individuals during the two‐year follow‐up period, including those in three groups with different, yet consistent, spending levels (68.1% of beneficiaries), a group with spending that rose quickly (25.3%), and a group with spending that rose progressively (6.6%). The ability to predict these spending groups was moderate to strong, with validated C‐statistics ranging from 0.63 to 0.87. The most influential factors for those in the group with progressively rising spending, in particular, were their age, generosity of Medicare insurance coverage, prior spending on diabetes care, and average adherence to medications.ConclusionsUsing data‐driven approaches, we identified distinct diabetes spending patterns among a nationally‐representative cohort of Medicare patients who were initially low utilizers. These patterns could be predicted using baseline characteristics, including factors that may be potentially modifiable.Implications for Policy or PracticeMany health care organizations use claims data to identify and predict patients for cost‐containment interventions. The approach we describe could inform the design and timing of cost‐containment interventions and target them to those at greatest need among patients with type 2 diabetes or other costly, chronic diseases.Primary Funding SourceNational Institute for Health Care Management (NIHCM) Foundation.