Working Paper: NBER ID: w14865
Authors: Jonathan Skinner; Douglas Staiger
Abstract: Inefficiency in the U.S. health care system has often been characterized as "flat of the curve" spending providing little or no incremental value. In this paper, we draw on macroeconomic models of diffusion and productivity to better explain the empirical patterns of outcome improvements in heart attacks (acute myocardial infarction). In these models, small differences in the propensity to adopt technology can lead to wide and persistent productivity differences across countries -- or in our case, hospitals. Theoretical implications are tested using U.S. Medicare data on survival and factor inputs for 2.8 million heart attack patients during 1986-2004. We find that the speed of diffusion for highly efficient and often low-cost innovations such as beta blockers, aspirin, and primary reperfusion explain a large fraction of persistent variations in productivity, and swamp the impact of traditional factor inputs. Holding technology constant, the marginal gains from spending on heart attack treatments appear positive but quite modest. Hospitals which during the period 1994/95 to 2003/04 raised their rate of technology diffusion (the "tigers") experienced outcome gains four times the gains in hospitals with diminished rates of diffusion (the "tortoises"). Survival rates in low-diffusion hospitals lag by as much as a decade behind high-diffusion hospitals, raising the question of why some hospitals (and the physicians who work there) adopt so slowly.
Keywords: technology diffusion; productivity growth; health care; heart attacks; acute myocardial infarction
JEL Codes: H51; I1; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| technology diffusion (O33) | productivity growth (O49) |
| technology diffusion (O33) | survival outcomes (C41) |
| tigers (L83) | survival gains (J17) |
| tortoises (K13) | survival lag (C41) |
| technology diffusion (O33) | marginal gains from spending (D61) |
| barriers to technology adoption (O33) | inefficiencies in healthcare (I11) |
| technology diffusion rates (O33) | productivity differences (O49) |