Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Risk-adjusted capitation payments: developing a diagnostic cost groups classification for the Dutch situation.

L M Lamers1

  • 1Erasmus University Rotterdam, The Netherlands.

Health Policy (Amsterdam, Netherlands)
|June 6, 1998
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Dutch tariff: results and arguments for an effective design for national EQ-5D valuation studies.

Health economics·2006
Same author

Comparison of EQ-5D and SF-6D utilities in mental health patients.

Health economics·2006
Same author

[Measuring the quality of life in economic evaluations: the Dutch EQ-5D tariff].

Nederlands tijdschrift voor geneeskunde·2005
Same author

Health-based risk adjustment Improving the pharmacy-based cost group model to reduce gaming possibilities.

The European journal of health economics : HEPAC : health economics in prevention and care·2004
Same author

Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient?

Inquiry : a journal of medical care organization, provision and financing·2002
Same author

Risk sharing as a supplement to imperfect capitation: a tradeoff between selection and efficiency.

Journal of health economics·2001
Same journal

A policy analysis of the three-month waiting period for new and returning residents to access provincial health insurance in Ontario, Canada.

Health policy (Amsterdam, Netherlands)·2026
Same journal

Policy priorities in palliative and end-of-life care: An exploration of issues reflected in public strategy.

Health policy (Amsterdam, Netherlands)·2026
Same journal

Advancing stakeholder analysis: Power and participation in health technology assessment policy development in the United Arab Emirates.

Health policy (Amsterdam, Netherlands)·2026
Same journal

Cultural sensitivity concerning the Gaeltacht people of Ireland - a concept analysis.

Health policy (Amsterdam, Netherlands)·2026
Same journal

Israel's 2010 capitation reform and the widening of regional health disparities: A difference-in-differences analysis.

Health policy (Amsterdam, Netherlands)·2026
Same journal

Does implementing strengths-based approaches improve adult social care outcomes in England? An econometric analysis of the Care Act 2014.

Health policy (Amsterdam, Netherlands)·2026
See all related articles

Improving health insurance risk adjustment is key for market-oriented reforms. Diagnostic Costs Groups (DCGs) using prior hospitalization data enhance capitation models, though high discretion diagnoses slightly reduce predictive accuracy.

Area of Science:

  • Health economics
  • Healthcare policy
  • Medical informatics

Background:

  • Market-oriented health care reforms are prevalent globally.
  • These reforms often involve consumer choice among competing health plans financed by capitation payments.
  • Dutch sickness funds use demographic factors for risk-adjusted capitation payments since 1993.

Purpose of the Study:

  • To develop a Diagnostic Costs Groups (DCG) classification using Dutch cost data.
  • To assess the impact of including diagnostic information from prior hospitalizations on capitation models.
  • To investigate the handling of high discretion diagnoses in risk adjustment.

Main Methods:

  • Developed a DCG classification based on Dutch sickness fund member cost data.
  • Analyzed the effect of including diagnostic information from prior hospitalizations.

Related Experiment Videos

  • Evaluated the predictive accuracy of the DCG model with and without high discretion diagnoses.

    • Main Results:

    • Extending demographic capitation models with prior hospitalization data (DCGs) improves predictive accuracy.
    • Grouping high discretion diagnoses together with non-admitted individuals slightly reduced model accuracy.
    • Diagnostic information from prior hospitalizations shows promise for enhancing capitation formulas.

    Conclusions:

    • Adequate risk adjustment is crucial for the success of market-oriented healthcare reforms.
    • Diagnostic Costs Groups (DCGs) derived from prior hospitalizations offer a promising method for improving risk adjustment in capitation payments.
    • Careful consideration of high discretion diagnoses is necessary when implementing DCG models.