Provider Monitoring and Pay-for-Performance When Multiple Providers Affect Outcomes: An Application to Renal Dialysis

Authors

  • Richard A. Hirth,

    1. Department of Health Management and Policy, University of Michigan School of Public Health, 109 S. Observatory, Ann Arbor, MI 48109-2029,
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    • Address correspondence to Richard A. Hirth, Ph.D., Department of Health Management and Policy, University of Michigan School of Public Health, 109 S. Observatory, Ann Arbor, MI 48109-2029; e-mail: rhirth@umich.edu. Marc N. Turenne, Ph.D., is with Kidney Epidemiology and Cost Center, Ann Arbor, MI. John R.C. Wheeler, Ph.D., is with Department of Health Management and Policy, University of Michigan School of Public Health, 109 S. Observatory, Ann Arbor, MI. Qing Pan, Ph.D., is with George Washington University, Department of Statistics, Washington, DC. Yu Ma, M.S., Joseph M. Messana, M.D., are with Kidney Epidemiology and Cost Center, Ann Arbor, MI.

  • Marc N. Turenne,

    1. Kidney Epidemiology and Cost Center, Ann Arbor, MI,
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  • John R.C. Wheeler,

    1. Department of Health Management and Policy, University of Michigan School of Public Health, 109 S. Observatory, Ann Arbor, MI,
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  • Qing Pan,

    1. George Washington University, Department of Statistics, Washington, DC,
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  • Yu Ma,

    1. Kidney Epidemiology and Cost Center, Ann Arbor, MI.
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  • Joseph M. Messana

    1. Kidney Epidemiology and Cost Center, Ann Arbor, MI.
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Abstract

Objective. To characterize the influence of dialysis facilities and nephrologists on resource use and patient outcomes in the dialysis population and to illustrate how such information can be used to inform payment system design.

Data Sources. Medicare claims for all hemodialysis patients for whom Medicare was the primary payer in 2004, combined with the Medicare Enrollment Database and the CMS Medical Evidence Form (CMS Form 2728), which is completed at onset of renal replacement therapy.

Study Design. Resource use (mainly drugs and laboratory tests) per dialysis session and two clinical outcomes (achieving targets for anemia management and dose of dialysis) were modeled at the patient level with random effects for nephrologist and dialysis facility, controlling for patient characteristics.

Results. For each measure, both the physician and the facility had significant effects. However, facilities were more influential than physicians, as measured by the standard deviation of the random effects.

Conclusions. The success of tools such as P4P and provider profiling relies upon the identification of providers most able to enhance efficiency and quality. This paper demonstrates a method for determining the extent to which variation in health care costs and quality of care can be attributed to physicians and institutional providers. Because variation in quality and cost attributable to facilities is consistently larger than that attributable to physicians, if provider profiling or financial incentives are targeted to only one type of provider, the facility appears to be the appropriate locus.

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