Risk Adjustment Revisited using Machine Learning Techniques
Alvaro Riascos (),
Mauricio Romero () and
Natalia Serna ()
Documentos CEDE from Universidad de los Andes - CEDE
Risk adjustment is vital in health policy design. Risk adjustment defines the annual capitation payments to health insurers and is a key determinant of insolvency risk for health insurers. In this study we compare the current risk adjustment formula used by Colombia's Ministry of Health and Social Protection against alternative specifications that adjust for additional factors. We show that the current risk adjustment formula, which conditions on demographic factors and their interactions, can only predict 30% of total health expenditures in the upper quintile of the expenditure distribution. We also show the government's formula can improve significantly by conditioning ex ante on measures indicators of 29 long-term diseases. We contribute to the risk adjustment literature by estimating machine learning based models and showing non-parametric methodologies (e.g., boosted trees models) outperform linear regressions even when fitted in a smaller set of regressors.
Keywords: risk adjustment; Diagnostic Related Groups; risk selection; machine learning (search for similar items in EconPapers)
JEL-codes: I11 I13 I18 C45 C55 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:col:000089:015601
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