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Risk Adjustment Revisited using Machine Learning Techniques

Author

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  • Alvaro J. Riascos
  • Mauricio Romero
  • Natalia Serna

Abstract

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.

Suggested Citation

  • Alvaro J. Riascos & Mauricio Romero & Natalia Serna, 2017. "Risk Adjustment Revisited using Machine Learning Techniques," Documentos CEDE 15601, Universidad de los Andes, Facultad de Economía, CEDE.
    • Handle: RePEc:col:000089:015601
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    File URL: https://repositorio.uniandes.edu.co/bitstream/handle/1992/8676/dcede2017-27.pdf
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    References listed on IDEAS

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    1. Castano, Ramon & Zambrano, Andres, 2006. "Biased selection within the social health insurance market in Colombia," Health Policy, Elsevier, vol. 79(2-3), pages 313-324, December.
    2. Álvaro Riascos & Natalia Serna & Ramiro Guerrero, 2017. "Capital requirements of health insurers under different risk-adjusted capitation payments," Documentos CEDE 15292, Universidad de los Andes, Facultad de Economía, CEDE.
    3. Álvaro Riascos & Eduardo Alfonso & Mauricio Romero, 2014. "The Performance of Risk Adjustment Models in Colombian Competitive Health Insurance Market," Documentos CEDE 12062, Universidad de los Andes, Facultad de Economía, CEDE.
    4. Van de ven, Wynand P.M.M. & Ellis, Randall P., 2000. "Risk adjustment in competitive health plan markets," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 14, pages 755-845, Elsevier.
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    Cited by:

    1. Catalina Gutiérrez S. & Nicolás Gómez, 2018. "El sistema de salud colombiano en las próximas décadas: cómo avanzar hacia la sostenibilidad y la calidad en la atención," Cuadernos de Fedesarrollo 16251, Fedesarrollo.
    2. Simón Ramírez Amaya & Adolfo J. Quiroz & Álvaro José Riascos Villegas, 2019. "Regression by clustering using metropolis-hastings," Documentos de Trabajo 18180, Quantil.

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    More about this item

    Keywords

    risk adjustment; Diagnostic Related Groups; risk selection; machine learning;
    All these keywords.

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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