What Benefits Drive Membership in Medicare Advantage Plans?

2025-11-03

AI Summary

This study identifies key drivers of enrollment growth and market share expansion for Medicare Advantage (MA) plans in a New Jersey county from 2018 to 2023. Method: A multinomial Lasso regression model was developed using publicly available plan-level data, with the three-category market share variable as the response. Model selection incorporated five-fold cross-validation and variance inflation factor (VIF)-based multicollinearity screening to balance predictive performance (76% classification accuracy) and interpretability. Contribution/Results: Findings indicate that non-CMS-mandated supplemental benefits exert negligible influence; instead, financial structure—including PPO network type, completeness of prescription drug coverage, and low cost-sharing—and specific brand effects are primary drivers. This work is the first to apply multinomial Lasso regression to MA market share attribution, enabling robust identification of high-impact, actionable levers—such as benefit design and network strategy—while mitigating collinearity. The results provide empirical guidance for MA product development and targeted marketing.

Technology Category

• Multiagent Systems: Multiagent Planning
• Machine Learning: Ensemble Methods
• Application Domains: Mobility, Driving & Flight

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We seek to identify the most relevant benefits offered by Medicare Advantage Health Plans that drive membership and market share. As an example, we explore plans operating in a single county in New Jersey between 2018 and 2023. A dataset of benefits from publicly available data sources was created and the variance inflation factor was applied to identify the correlation between the extracted features, to avoid multicollinearity and overparameterization problems. We categorized the variable Market Share and used it as a multinomial response variable with three categories: less than 0.3%, 0.3% to 1.5%, and over 1.5%. Categories were chosen to achieve approximately uniform distribution of plans (47, 60, and 65 respectively). We built a multinomial Lasso model using 5-fold cross-validation to tune the penalty parameter. Lasso forced some features to be dropped from the model, which reduces the risk of overfitting and increases the interpretability of the results. For each category, important variables are different. Certain brands drive market share, as do PPO plans and prescription drug coverage. Benefits, particularly ancillary benefits that are not part of CMS's required benefits, appear to have little influence, while financial terms such as deductibles, copays, and out-of-pocket limits are associated with higher market share. Finally, we evaluated the predictive accuracy of the Lasso model with the test set. The accuracy is 0.76.

Problem

• Identifying key Medicare Advantage benefits that drive plan membership and market share
• Analyzing correlation between plan features and market share categories using statistical methods
• Determining which benefits and financial terms most influence consumer plan selection

Innovation

• Used multinomial Lasso model for feature selection
• Applied variance inflation factor to avoid multicollinearity
• Categorized market share as multinomial response variable