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Are Hospital Quality Indicators Causal?

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  • Amitabh Chandra
  • Maurice Dalton
  • Douglas O. Staiger

Abstract

Hospitals play a key role in patient outcomes and spending, but efforts to improve their quality are hindered because we do not know whether hospital quality indicators are causal or biased. We evaluate the validity of commonly used quality indicators, such as mortality, readmissions, inpatient costs, and length-of-stay, using a quasi-experimental design where hospital closures reallocate large numbers of patients to hospitals of different quality. This setting allows us to measure whether patient outcomes improve as much as quality indicators predict when a relatively low-quality hospital closes, or decline as predicted when a relatively high-quality hospital closes. Using more than 20 years of Medicare claims for over 30 million patients admitted with five common diagnoses, we find that hospital quality indicators overstate differences in the causal impact of hospitals on mortality and readmission rates by 7 percent or less, but overstate differences in the causal impact of hospitals on inpatient cost and length-of-stay measures by closer to 40 percent. On average, hospital closures reduce patient mortality by shifting patients to higher quality hospitals, but the but the effect varies widely depending on the relative quality of the closing hospital.

Suggested Citation

  • Amitabh Chandra & Maurice Dalton & Douglas O. Staiger, 2023. "Are Hospital Quality Indicators Causal?," NBER Working Papers 31789, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:31789
    Note: AG CH EH IO LS PE PR
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    References listed on IDEAS

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    1. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
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    6. Amitabh Chandra & Douglas O. Staiger, 2007. "Productivity Spillovers in Health Care: Evidence from the Treatment of Heart Attacks," Journal of Political Economy, University of Chicago Press, vol. 115(1), pages 103-140.
    7. Amitabh Chandra & Douglas O Staiger, 2020. "Identifying Sources of Inefficiency in Healthcare [“The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care,”]," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(2), pages 785-843.
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    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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