Abstract
BackgroundPayers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. Penalized linear regression represents a practical and incremental step forward that provides transparency and interpretability within the familiar regression framework. This study conducted an in-depth comparison of prediction performance of standard and penalized linear regression in predicting future health care costs in older adults.Methods and findingsThis retrospective cohort study included 81,106 Medicare Advantage patients with 5 years of continuous medical and pharmacy insurance from 2009 to 2013. Total health care costs in 2013 were predicted with comorbidity indicators from 2009 to 2012. Using 2012 predictors only, OLS performed poorly (e.g., R2 = 16.3%) compared to penalized linear regression models (R2 ranging from 16.8 to 16.9%); using 2009–2012 predictors, the gap in prediction performance increased (R2:15.0% versus 18.0–18.2%). OLS with a reduced set of predictors selected by lasso showed improved performance (R2 = 16.6% with 2012 predictors, 17.4% with 2009–2012 predictors) relative to OLS without variable selection but still lagged behind the prediction performance of penalized regression. Lasso regression consistently generated prediction ratios closer to 1 across different levels of predicted risk compared to other models.ConclusionsThis study demonstrated the advantages of using transparent and easy-to-interpret penalized linear regression for predicting future health care costs in older adults relative to standard linear regression. Penalized regression showed better performance than OLS in predicting health care costs. Applying penalized regression to longitudinal data increased prediction accuracy. Lasso regression in particular showed superior prediction ratios across low and high levels of predicted risk. Health care insurers, providers and policy makers may benefit from adopting penalized regression such as lasso regression for cost prediction to improve risk adjustment and population health management and thus better address the underlying needs and risk of the populations they serve.
Highlights
Risk adjustment models are applied by payers and health care delivery organizations to adjust for differences in patient characteristics when estimating expected health care resource use, clinical outcomes, and quality of care
Providers and policy makers may benefit from adopting penalized regression such as lasso regression for cost prediction to improve risk adjustment and population health management and better address the underlying needs and risk of the populations they serve
The HHS-Hierarchical Condition Categories (HHS-HCC) model, a risk adjustment model adopted for health plans participating in the Affordable Care Act, uses standard linear regression with age, gender, diagnoses and interactions between diagnoses to predict medical expenditure risk [2]
Summary
Risk adjustment models are applied by payers and health care delivery organizations to adjust for differences in patient characteristics when estimating expected health care resource use, clinical outcomes, and quality of care. An emerging literature has begun to explore the potential application of machine learning methods to predict health care costs and utilization for risk adjustment purposes [3,4,5,6]. These studies compared a variety of machine learning techniques for risk adjustment including penalized regression, random forests, multivariate adaptive regression splines, boosted regression trees, neural network, and super learner. Payers and providers still primarily use ordinary least squares (OLS) to estimate expected economic and clinical outcomes for risk adjustment purposes. This study conducted an in-depth comparison of prediction performance of standard and penalized linear regression in predicting future health care costs in older adults.
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