Do bad report cards have consequences? Impacts of publicly reported provider quality information on the CABG market in Pennsylvania, by Justin Wanga, Jason Hockenberry, Shin-Yi Chou, and Muzhe Yang (JHE)
Since 1992, the Pennsylvania Health Care Cost Containment Council (PHC4) has published cardiac care report cards for coronary artery bypass graft (CABG) surgery providers. We examine the impact of CABG report cards on a provider’s aggregate volume and volume by patient severity and then employ a mixed logit model to investigate the matching between patients and providers. We find a reduction in volume of poor performing and unrated surgeons’ volume but no effect on more highly rated surgeons or hospitals of any rating. We also find that the probability that patients, regardless of severity of illness, receive CABG surgery from low-performing surgeons is significantly lower.
Consumers, health insurance and dominated choices, by Anna D. Sinaiko and Richard A. Hirth (JHE)
We analyze employee health plan choices when the choice set offered by their employer includes a dominated plan. During our study period, one-third of workers were enrolled in the dominated plan. Some may have selected the plan before it was dominated and then failed to switch out of it. However, a substantial number actively chose the dominated plan when they had an unambiguously better choice. These results suggest limitations in the ability of health reform based solely on consumer choice to achieve efficient outcomes and that implementation of health reform should anticipate, monitor and account for this consumer behavior.
Technology Growth and Expenditure Growth in Health Care, by Amitabh Chandra and Jonathan S. Skinner (NBER)
In the United States, health care technology has contributed to rising survival rates, yet health care spending relative to GDP has also grown more rapidly than in any other country. We develop a model of patient demand and supplier behavior to explain these parallel trends in technology growth and cost growth. We show that health care productivity depends on the heterogeneity of treatment effects across patients, the shape of the health production function, and the cost structure of procedures such as MRIs with high fixed costs and low marginal costs. The model implies a typology of medical technology productivity: (I) highly cost-effective “home run” innovations with little chance of overuse, such as anti-retroviral therapy for HIV, (II) treatments highly effective for some but not for all (e.g. stents), and (III) “gray area” treatments with uncertain clinical value such as ICU days among chronically ill patients. Not surprisingly, countries adopting Category I and effective Category II treatments gain the greatest health improvements, while countries adopting ineffective Category II and Category III treatments experience the most rapid cost growth. Ultimately, economic and political resistance in the U.S. to ever-rising tax rates will likely slow cost growth, with uncertain effects on technology growth.