This presentation is part of: I00-1 (1902) Health, Education, and Welfare

Efficiency, Technical Change, and Technology Diffusion among U.S. Hospitals

Gary D. Ferrier, Ph.D., Department of Economics, University of Arkansas, Sam Walton College of Business, Room 402, Fayetteville, AR 72701, Herve Leleu, Ph.D., Economics, IESEG, 3 rue de la Digue, Lille, 59000, France, James Moises, M.D., Emergency Medicine, Tulane University Medical Center, 1415 Tulane Avenue, New Orleans, LA 70112, and Vivian Grace Valdmanis, Ph.D., Health Policy and Public Health, Univeresity of the Sciences in Philadelphia, 600 S 43rd Street, Philadelphia, PA 19104.

There is an agreement in the literature that one of the major cost drivers in US hospital markets is duplication of services.  This has been empirically tested using cost equations and market share analysis.  In this paper, we apply the multi-period Malmquist approach to a sample of US hospitals operating in the 100 largest cities between the years 1992-2006.  We use American Hospital Association (AHA) data which have information on labor inputs, beds, patient days by payer, and the number of outpatient visits and emergency room visits.  Further, there is a detailed list of the types of technological medical services offered by each hospital.

Whereas, earlier works using the Malmquist approach and its decomposition into measures of efficiency change and technical change have illuminated the degree of change within markets with different hospital ownership forms, we add to the literature by comparing the change in hospitals within these markets and the diffusion of technology both between cities and within cities.  In this way, we can determine the saturation point of technology diffusion defined here as the point in time when economic improvements are no longer positive.  

By assessing these changes across hospital markets, we can also illustrate the optimal amount of total technology in a city.  Since we measure hospital productivity and technological diffusion over a long period of time, we can also gauge the degree of diffusion and changes in productivity by changes in market size, i.e., population by city.  Another by product of our analysis is that we can also examine what happened to the New Orleans hospital market post Hurricane Katrina and the downsizing of the hospital sector and technology availability.  In this way, we have a de facto natural experiment of the repercussions of drastic downsizing as a way to control costs as a matter of public policy.

We anticipate that our results will show that technological diffusion among hospitals operating during this time period will vary based on economic, market, and ownership of hospital status.