Working Paper: NBER ID: w23166
Authors: Joseph J. Doyle Jr.; John A. Graves; Jonathan Gruber
Abstract: In response to unsustainable growth in health care spending, there is enormous interest in reforming the payment system to “pay for quality instead of quantity.” While quality measures are crucial to such reforms, they face major criticisms largely over the potential failure of risk adjustment to overcome endogeneity concerns. In this paper we implement a methodology for estimating the causal relationship between hospital quality measures and patient outcomes. To compare similar patients across hospitals in the same market, we xploit ambulance company preferences as an instrument for patient assignment. We find that a variety of measures used by insurers to measure provider quality are successful: assignment to a higher-scoring hospital results in better patient outcomes. We estimate that a two-standard deviation improvement in a composite quality measure based on existing data collected by CMS is causally associated with reductions in readmissions and mortality of roughly 15%.
Keywords: hospital quality; patient outcomes; healthcare reform; instrumental variables
JEL Codes: I10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| assignment to a higher-scoring hospital (I11) | better patient outcomes (I11) |
| two-standard deviation improvement in a composite quality measure (C43) | better patient outcomes (I11) |
| hospitals with higher process measures of quality (I11) | lower long-term mortality (I12) |
| lower patient satisfaction scores (I11) | higher odds of readmission and death (I12) |
| hospitals with higher readmission rates (I11) | higher odds of readmission for the marginal patient (I11) |
| hospitals with higher mortality rates (I14) | increased odds of mortality (I12) |