Literature DB >> 8656721

Multiyear diagnostic information from prior hospitalization as a risk-adjuster for capitation payments.

L M Lamers1, R C van Vliet.   

Abstract

As part of a move toward a more market-oriented health-care system, major changes have been implemented in the Dutch social health insurance system. The competing sickness funds now receive risk-adjusted capitation payments, currently based on the age-sex distribution of the insurance portfolios. These very crude health indicators do not reflect expected costs accurately. The authors examine whether the incorporation of inpatient diagnostic information over a multiyear period can increase the accuracy of the capitation model. Using a panel data set (n approximately 50,000) comprising annual costs and diagnostic information for 5 successive years, the authors compare demographic and diagnostic models in their ability to predict future health care costs. The predictive accuracy of an age-sex-based capitation formula improves substantially when diagnostic information from an individual's prior hospitalizations is used as an additional risk-adjuster. The longer the period over which diagnostic information is available, the better is the predictive accuracy. The expected loss in 1992 for insured persons with the highest costs in 1988 decreases from 88% (demographic model) to 62% (1-year diagnostic model) and to 43% (3-year diagnostic model). The use of diagnostic information from prior hospitalizations is a promising option for improving the capitation formulae. The authors' results are relevant not only for situations where competing insurers are capitated, as in the Netherlands, but also when providers (United Kingdom) or health maintenance organizations (United States) are capitated.

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Mesh:

Year:  1996        PMID: 8656721     DOI: 10.1097/00005650-199606000-00005

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  7 in total

1.  Ignoring small predictable profits and losses: a new approach for measuring incentives for cream skimming.

Authors:  E M van Barneveld; L M Lamers; R C van Vliet; W P van de Ven
Journal:  Health Care Manag Sci       Date:  2000-02

2.  The potential premium range of risk-rating in competitive markets for supplementary health insurance.

Authors:  Francesco Paolucci; Femmeke Prinsze; Pieter J A Stam; Wynand P M M van de Ven
Journal:  Int J Health Care Finance Econ       Date:  2009-01-06

3.  Improving the prediction model used in risk equalization: cost and diagnostic information from multiple prior years.

Authors:  S H C M van Veen; R C van Kleef; W P M M van de Ven; R C J A van Vliet
Journal:  Eur J Health Econ       Date:  2014-02-12

4.  Risk-adjusted capitation based on the Diagnostic Cost Group Model: an empirical evaluation with health survey information.

Authors:  L M Lamers
Journal:  Health Serv Res       Date:  1999-02       Impact factor: 3.402

5.  Comparing self-reported health status and diagnosis-based risk adjustment to predict 1- and 2 to 5-year mortality.

Authors:  Kenneth Pietz; Laura A Petersen
Journal:  Health Serv Res       Date:  2007-04       Impact factor: 3.402

6.  Applying a risk-adjustment framework to primary care: can we improve on existing measures?

Authors:  Amy K Rosen; Robert Reid; Anne-Marie Broemeling; Carter C Rakovski
Journal:  Ann Fam Med       Date:  2003 May-Jun       Impact factor: 5.166

7.  An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan.

Authors:  Hsien-Yen Chang; Jonathan P Weiner
Journal:  BMC Med       Date:  2010-01-18       Impact factor: 8.775

  7 in total

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