Medicare Payment Reform and Hospital Costs: Evidence from the Prospective Payment System and the Treatment of Cardiac Disease

Medicare’s Prospective Payment System (PPS) reform in 1983 tied hospital payments to the national average cost of each medical technology with the expectation of reducing health care costs. I show that an unintended consequence of PPS was generating financial incentives for hospitals to expand treatments that had average costs greater than marginal costs due to sizable fixed investments – i.e., the Medicare payment would be greater than the treatment cost at the margin. In the context of cardiac treatments, coronary artery bypass graft (CABG) surgery has a greater average-to-marginal cost ratio than angioplasty, whose ratio is greater than drug therapy’s. I document that the PPS reform induced a profit margin that was five times higher for CABG than for angioplasty, and that was negative for drug therapy. Exploiting the discontinuity in Medicare eligibility at the age of 65, and comparing outcomes before–and–after the PPS reform at the age-65 threshold, I find i) a discontinuous change in CABG use at age-65 after the reform that implies an increase of 40 to 60 percent; while ii) no change in angioplasty use; and iii) a decrease in drug therapy use. Nearly all of the increased CABG use is driven by a composition change in the patients who receive CABG – i.e., hospitals expanded CABG use by treating patients who are observably healthier for whom angioplasty is medically substitutable. As a result, the increased CABG use was not cost effective; the lower bound estimate of the cost per quality-adjusted life year (QALY) was over one million dollars. (JEL Codes: H51, I11, I18, L51) ∗I am deeply indebted to my PhD advisors, Kenneth Chay, Anna Aizer, Brian Knight, and Kaivan Munshi for their guidance and support throughout this project. I am also grateful to David Blau, Andrew Foster, Susan Helper, Blaise Melly, Sriniketh Nagavarapu, and seminar participants at Case Western Reserve University, McGill University, and The Ohio State University for valuable comments. Financial support from Brown University’s Population Study and Training Center and the Department of Community Health at Brown is gratefully acknowledged. I would like to thank Nataliya Kravets at the National Center for Health Statistics Research Data Center for help with the restricted National Hospital Discharge Survey Data. The findings and conclusions in this paper are those of the author and do not necessarily represent the views of the Research Data Center, the National Center for Health Statistics, or the Centers for Disease Control and Prevention. Email: [email protected].