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The profile inter‐unit reliability

Author

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  • Kevin He
  • Claudia Dahlerus
  • Lu Xia
  • Yanming Li
  • John D. Kalbfleisch

Abstract

To assess the quality of health care, patient outcomes associated with medical providers (eg, dialysis facilities) are routinely monitored in order to identify poor (or excellent) provider performance. Given the high stakes of such evaluations for payment as well as public reporting of quality, it is important to assess the reliability of quality measures. A commonly used metric is the inter‐unit reliability (IUR), which is the proportion of variation in the measure that comes from inter‐provider differences. Despite its wide use, however, the size of the IUR has little to do with the usefulness of the measure for profiling extreme outcomes. A large IUR can signal the need for further risk adjustment to account for differences between patients treated by different providers, while even measures with an IUR close to zero can be useful for identifying extreme providers. To address these limitations, we propose an alternative measure of reliability, which assesses more directly the value of a quality measure in identifying (or profiling) providers with extreme outcomes. The resulting metric reflects the extent to which the profiling status is consistent over repeated measurements. We use national dialysis data to examine this approach on various measures of dialysis facilities.

Suggested Citation

  • Kevin He & Claudia Dahlerus & Lu Xia & Yanming Li & John D. Kalbfleisch, 2020. "The profile inter‐unit reliability," Biometrics, The International Biometric Society, vol. 76(2), pages 654-663, June.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:2:p:654-663
    DOI: 10.1111/biom.13167
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    References listed on IDEAS

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    1. Jones, Hayley E. & Spiegelhalter, David J., 2011. "The Identification of “Unusual†Health-Care Providers From a Hierarchical Model," The American Statistician, American Statistical Association, vol. 65(3), pages 154-163.
    2. John D. Kalbfleisch & Kevin He, 2018. "Discussion on “Time‐dynamic profiling with application to hospital readmission among patients on dialysis,” by Jason P. Estes, Danh V. Nguyen, Yanjun Chen, Lorien S. Dalrymple, Connie M. Rhee, Kamyar ," Biometrics, The International Biometric Society, vol. 74(4), pages 1401-1403, December.
    3. Pan W., 2002. "A Note on the Use of Marginal Likelihood and Conditional Likelihood in Analyzing Clustered Data," The American Statistician, American Statistical Association, vol. 56, pages 171-174, August.
    4. Jason P. Estes & Danh V. Nguyen & Yanjun Chen & Lorien S. Dalrymple & Connie M. Rhee & Kamyar Kalantar‐Zadeh & Damla Şentürk, 2018. "Time‐dynamic profiling with application to hospital readmission among patients on dialysis," Biometrics, The International Biometric Society, vol. 74(4), pages 1383-1394, December.
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