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Updated: Mar 25, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A Machine Learning Framework for Plan Payment Risk Adjustment.

¹Department of Health Care Policy, Harvard Medical School, Boston, MA.

Health Services Research

|February 20, 2016

Summary

This summary is machine-generated.

Cross-validation and machine learning offer a new way to improve risk adjustment. This approach can simplify formulas, reducing upcoding incentives for health insurers.

Keywords:

Risk adjustment machine learning regression

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Last Updated: Mar 25, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Health economics
Data science
Health services research

Background:

Risk adjustment is crucial for fair health plan payments.
Current methods may incentivize upcoding and administrative complexity.

Purpose of the Study:

Introduce cross-validation and a nonparametric machine learning framework for risk adjustment.
Assess the potential of these methods to improve risk adjustment accuracy and reduce gaming.

Main Methods:

Utilized the 2011-2012 Truven MarketScan database.
Compared various machine learning algorithms (regression, penalized regression, decision trees, neural networks, ensemble super learner) for predicting total annual expenditure.
Employed screening algorithms to reduce variables and cross-validation for performance comparison (R²).

Main Results:

A simplified risk adjustment formula derived from the nonparametric framework retained significant predictive efficiency compared to traditional formulas.
The ensemble super learner approach demonstrated superior performance over classical regression and other individual algorithms.

Conclusions:

Cross-validated machine learning techniques offer novel insights for risk adjustment.
Potential exists for simplified formulas that reduce incentives for coding intensity and insurer upcoding.