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Resolving the Coordination Problem in Health Care: Limited Responsibility HMO:s

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  • Lundbäck, Mattias

    (Ratio)

Abstract

The underlying reason for the lack of coordination of health care production is often attributed to the lack of central cost accountability for each individual patient. The rather few health care producers that manages to coordinate health care all the way from primary care to tertiary care are often used as good examples, e g Veterans administration, Kaiser Permanente, Intermountain Health Care and The Mayo Clinics. However, due to large cost variability amongst patients (economic risk), information asymmetries and agency problems, the provision of health care is rarely coordinated. More commonly, the delivery of health care production is reimbursed in a non-coordinated way that creates incentives for sub-optimisation and holds back entrepreneurship among producers. In this paper we use data from the Medical Expenditure Panel Survey and computer simulations to illustrate that limiting a provider’s cost responsibility for each patient is a much more efficient way of reducing provider risk than to increase the number of patients. Our simulations illustrate that introducing an individual yearly cost ceiling of 20 000 US-dollars per patient reduces risk as much as increasing the number of patients from 5 000 to 100 000. The results indicate that it might be possible to create a favourable environment for coordination in managed care organisations, such as those mentioned above, without exposing providers to extensive risk. Reimbursements systems of the type used in Medicare Advantage might thus be slightly adjusted to reduce the barriers of entry (economic risk) and promote the entry of integrated care providers on the market.

Suggested Citation

  • Lundbäck, Mattias, 2013. "Resolving the Coordination Problem in Health Care: Limited Responsibility HMO:s," Ratio Working Papers 209, The Ratio Institute.
  • Handle: RePEc:hhs:ratioi:0209
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    References listed on IDEAS

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    1. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.
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    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

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