Risk adjustment revisited using machine learning techniques
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 Soc...
- Autores:
-
Riascos Villegas, Alvaro José
Romero, Mauricio
Serna, Natalia
- Tipo de recurso:
- Work document
- Fecha de publicación:
- 2017
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/8676
- Acceso en línea:
- http://hdl.handle.net/1992/8676
- Palabra clave:
- Risk adjustment
Diagnostic Related Groups
Risk selection
Machine learning
Seguros de salud - Colombia
Evaluación de riesgos contra la salud - Colombia
Política de salud - Colombia
I11, I13, I18, C45, C55
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.none.fl_str_mv |
Risk adjustment revisited using machine learning techniques |
dc.title.alternative.none.fl_str_mv |
Revisión del ajuste de riesgo con técnicas de aprendizaje de máquinas |
title |
Risk adjustment revisited using machine learning techniques |
spellingShingle |
Risk adjustment revisited using machine learning techniques Risk adjustment Diagnostic Related Groups Risk selection Machine learning Seguros de salud - Colombia Evaluación de riesgos contra la salud - Colombia Política de salud - Colombia I11, I13, I18, C45, C55 |
title_short |
Risk adjustment revisited using machine learning techniques |
title_full |
Risk adjustment revisited using machine learning techniques |
title_fullStr |
Risk adjustment revisited using machine learning techniques |
title_full_unstemmed |
Risk adjustment revisited using machine learning techniques |
title_sort |
Risk adjustment revisited using machine learning techniques |
dc.creator.fl_str_mv |
Riascos Villegas, Alvaro José Romero, Mauricio Serna, Natalia |
dc.contributor.author.none.fl_str_mv |
Riascos Villegas, Alvaro José Romero, Mauricio Serna, Natalia |
dc.subject.keyword.none.fl_str_mv |
Risk adjustment Diagnostic Related Groups Risk selection Machine learning |
topic |
Risk adjustment Diagnostic Related Groups Risk selection Machine learning Seguros de salud - Colombia Evaluación de riesgos contra la salud - Colombia Política de salud - Colombia I11, I13, I18, C45, C55 |
dc.subject.armarc.none.fl_str_mv |
Seguros de salud - Colombia Evaluación de riesgos contra la salud - Colombia Política de salud - Colombia |
dc.subject.jel.none.fl_str_mv |
I11, I13, I18, C45, C55 |
description |
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. |
publishDate |
2017 |
dc.date.issued.none.fl_str_mv |
2017 |
dc.date.accessioned.none.fl_str_mv |
2018-09-27T16:55:45Z |
dc.date.available.none.fl_str_mv |
2018-09-27T16:55:45Z |
dc.type.spa.fl_str_mv |
Documento de trabajo |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/workingPaper |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_8042 |
dc.type.content.spa.fl_str_mv |
Text |
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https://purl.org/redcol/resource_type/WP |
format |
http://purl.org/coar/resource_type/c_8042 |
dc.identifier.issn.none.fl_str_mv |
1657-5334 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/1992/8676 |
dc.identifier.eissn.none.fl_str_mv |
1657-7191 |
dc.identifier.doi.none.fl_str_mv |
10.57784/1992/8676 |
dc.identifier.instname.spa.fl_str_mv |
instname:Universidad de los Andes |
dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Séneca |
dc.identifier.repourl.spa.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
identifier_str_mv |
1657-5334 1657-7191 10.57784/1992/8676 instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
url |
http://hdl.handle.net/1992/8676 |
dc.language.iso.none.fl_str_mv |
spa |
language |
spa |
dc.relation.ispartofseries.none.fl_str_mv |
Documentos CEDE No. 27 Marzo de 2017 |
dc.relation.repec.spa.fl_str_mv |
https://ideas.repec.org/p/col/000089/015601.html |
dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.none.fl_str_mv |
19 páginas |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidad de los Andes, Facultad de Economía, CEDE |
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Universidad de los Andes, Facultad de Economía, CEDE |
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Universidad de los Andes |
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spelling |
Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Riascos Villegas, Alvaro José9d5edd1a-2800-42af-b5b7-6cd5baeac192500Romero, Mauriciod63643dd-3340-4e4c-a638-03fc8e702a58500Serna, Nataliadb5c992a-c714-4729-bbc2-587809d00acf5002018-09-27T16:55:45Z2018-09-27T16:55:45Z20171657-5334http://hdl.handle.net/1992/86761657-719110.57784/1992/8676instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/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.El ajuste de riesgo es un componente esencial en el diseño de la política del sector de la salud. El ajuste de riesgo define los pagos de capitación que se le hacen a las aseguradoras en salud y determina el riesgo de insolvencia de las mismas. En este estudio comparamos la formula usada actualmente por el Ministerio de Salud y Protección Social para determinar el ajuste de riesgo, contra especificaciones alternativas que controlan por factores de riesgo adicionales. Mostramos que la formula actual, la cual ajusta los pagos solamente a factores de riesgo demográficos, predice tan solo el 30 % del gasto en el quintil superior de la distribución del gasto. Nuestros resultados muestran que incorporar indicadores de 29 enfermedades de larga duración en un modelo lineal, como el que usa el gobierno, mejora su capacidad predictiva considerablemente. Finalmente, contribuimos a la literatura de ajuste de riesgo a través de la estimación de modelos de aprendizaje de máquinas y mostramos que modelos no paramétricos (e.g., boosting de arboles) tienen un mejor desempeño que los modelos lineales, incluso cuando se ajustan sobre un conjunto de regresores más pequeño.19 páginasapplication/pdfspaUniversidad de los Andes, Facultad de Economía, CEDEDocumentos CEDE No. 27 Marzo de 2017https://ideas.repec.org/p/col/000089/015601.htmlRisk adjustment revisited using machine learning techniquesRevisión del ajuste de riesgo con técnicas de aprendizaje de máquinasDocumento de trabajoinfo:eu-repo/semantics/workingPaperhttp://purl.org/coar/resource_type/c_8042http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttps://purl.org/redcol/resource_type/WPRisk adjustmentDiagnostic Related GroupsRisk selectionMachine learningSeguros de salud - ColombiaEvaluación de riesgos contra la salud - ColombiaPolítica de salud - ColombiaI11, I13, I18, C45, C55Facultad de EconomíaPublicationORIGINALdcede2017-27.pdfdcede2017-27.pdfapplication/pdf554715https://repositorio.uniandes.edu.co/bitstreams/bfcd0faf-23de-4f58-b915-8568643c3233/download6b5ced765e3d82f292a225affbc18ddbMD51THUMBNAILdcede2017-27.pdf.jpgdcede2017-27.pdf.jpgIM Thumbnailimage/jpeg10326https://repositorio.uniandes.edu.co/bitstreams/0a9080cf-c5b9-408c-99d8-5de8024bd2bd/download1c67554b832d579f299db94b105e805bMD55TEXTdcede2017-27.pdf.txtdcede2017-27.pdf.txtExtracted texttext/plain53617https://repositorio.uniandes.edu.co/bitstreams/82d474e0-da62-4e4d-8401-a0aeebfdbd95/download0741de651b4bdfbb9779fa4ea490be14MD541992/8676oai:repositorio.uniandes.edu.co:1992/86762024-06-04 15:16:43.71http://creativecommons.org/licenses/by-nc-nd/4.0/open.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co |