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The Impact of Health Information Technology on Hospital Productivity

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  • Jinhyung Lee
  • Jeffrey S. McCullough
  • Robert J. Town

Abstract

The US health care sector is, by most accounts, extraordinarily inefficient. Health information technology (IT) has been championed as a tool that can transform health care delivery. Recently, the federal government has taken an active role in promoting health IT diffusion. There is little systematic analysis of the causal impact of health IT on productivity or whether private and public returns to health IT diverge thereby justifying government intervention. We estimate the parameters of a value-added hospital production function correcting for endogenous input choices in order to assess the private returns hospitals earn from health IT. Despite high marginal products, the potential benefits from expanded IT adoption are modest. Over the span of our data, health IT inputs increased by more than 210% and contributed about 6% to the increase in value-added. Virtually all the increase in value-added is attributable to the increased use of inputs{there was little change in hospital multi-factor productivity. Not-for-profits invested more heavily and differently in IT than for-profit hospitals. Finally, we find no evidence of labor complementarities or network externalities from health IT.

Suggested Citation

  • Jinhyung Lee & Jeffrey S. McCullough & Robert J. Town, 2012. "The Impact of Health Information Technology on Hospital Productivity," NBER Working Papers 18025, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18025
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    1. Daron Acemoglu & Amy Finkelstein, 2008. "Input and Technology Choices in Regulated Industries: Evidence from the Health Care Sector," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 837-880, October.
    2. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Papers 2005-W04, Economics Group, Nuffield College, University of Oxford.
    3. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    4. Susan Athey & Scott Stern, 2002. "The Impact of Information Technology on Emergency Health Care Outcomes," RAND Journal of Economics, The RAND Corporation, vol. 33(3), pages 399-432, Autumn.
    5. Cutler, David & Landrum, Mary Beth & Huckman, Robert, 2004. "The Role of Information in Medical Markets: An Analysis of Publicly Reported Outcomes in Cardiac Surgery," Scholarly Articles 2640582, Harvard University Department of Economics.
    6. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    7. Katz, Michael L & Shapiro, Carl, 1986. "Technology Adoption in the Presence of Network Externalities," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 822-841, August.
    8. Gautam Gowrisankaran & Joanna Stavins, 2004. "Network Externalities and Technology Adoption: Lessons from Electronic Payments," RAND Journal of Economics, The RAND Corporation, vol. 35(2), pages 260-276, Summer.
    9. Ackerberg, Daniel & Caves, Kevin & Frazer, Garth, 2006. "Structural identification of production functions," MPRA Paper 38349, University Library of Munich, Germany.
    10. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    11. Nicholas Bloom & Raffaella Sadun & John Van Reenen, 2012. "Americans Do IT Better: US Multinationals and the Productivity Miracle," American Economic Review, American Economic Association, vol. 102(1), pages 167-201, February.
    12. Horwitz, Jill R. & Nichols, Austin, 2009. "Hospital ownership and medical services: Market mix, spillover effects, and nonprofit objectives," Journal of Health Economics, Elsevier, vol. 28(5), pages 924-937, September.
    13. Agha, Leila, 2014. "The effects of health information technology on the costs and quality of medical care," Journal of Health Economics, Elsevier, vol. 34(C), pages 19-30.
    14. Borzekowski, Ron, 2009. "Measuring the cost impact of hospital information systems: 1987-1994," Journal of Health Economics, Elsevier, vol. 28(5), pages 938-949, September.
    15. David M. Cutler & Robert S. Huckman & Mary Beth Landrum, 2004. "The Role of Information in Medical Markets: An Analysis of Publicly Reported Outcomes in Cardiac Surgery," American Economic Review, American Economic Association, vol. 94(2), pages 342-346, May.
    16. Kevin J. Stiroh, 2002. "Information Technology and the U.S. Productivity Revival: What Do the Industry Data Say?," American Economic Review, American Economic Association, vol. 92(5), pages 1559-1576, December.
    17. Ann Bartel & Casey Ichniowski & Kathryn Shaw, 2007. "How Does Information Technology Affect Productivity? Plant-Level Comparisons of Product Innovation, Process Improvement, and Worker Skills," The Quarterly Journal of Economics, Oxford University Press, vol. 122(4), pages 1721-1758.
    18. Erik Brynjolfsson & Lorin Hitt, 1996. "Paradox Lost? Firm-Level Evidence on the Returns to Information Systems Spending," Management Science, INFORMS, vol. 42(4), pages 541-558, April.
    19. David, Guy & Lindrooth, Richard C. & Helmchen, Lorens A. & Burns, Lawton R., 2014. "Do hospitals cross-subsidize?," Journal of Health Economics, Elsevier, vol. 37(C), pages 198-218.
    20. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Series Working Papers 2005-W04, University of Oxford, Department of Economics.
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    Citations

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    Cited by:

    1. Jeffrey S. McCullough & Stephen Parente & Robert Town, 2013. "Health Information Technology and Patient Outcomes: The Role of Organizational and Informational Complementarities," NBER Working Papers 18684, National Bureau of Economic Research, Inc.
    2. Dranove, David & Garthwaite, Craig & Li, Bingyang & Ody, Christopher, 2015. "Investment subsidies and the adoption of electronic medical records in hospitals," Journal of Health Economics, Elsevier, vol. 44(C), pages 309-319.
    3. Francesco Venturini & Ana Rincon-Aznar & Dr Michela Vecchi, 2013. "ICT as a general purpose technology: spillovers, absorptive capacity and productivity performance," National Institute of Economic and Social Research (NIESR) Discussion Papers 416, National Institute of Economic and Social Research.
    4. Gautam Gowrisankaran & Keith A. Joiner & Jianjing Lin, 2016. "Does Health IT Adoption Lead to Better Information or Worse Incentives?," NBER Working Papers 22873, National Bureau of Economic Research, Inc.
    5. Spyros Arvanitis & Euripidis N. Loukis, 2016. "Investigating the effects of ICT on innovation and performance of European hospitals: an exploratory study," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(4), pages 403-418, May.
    6. Yia-Wun Liang & Wen-Yi Chen & Yu-Hui Lin, 2015. "Estimating a Hospital Production Function to Evaluate the Effect of Nurse Staffing on Patient Mortality in Taiwan: The Longitudinal Count Data Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 154-169, December.
    7. Wahid Abdallah, 2017. "Electronic filing System, Bureaucratic Efficiency and Public Service Delivery: Evidence from Bangladesh," Working Papers id:12223, eSocialSciences.
    8. Breunig, Christoph & Kummer, Michael & Ohnemus, Jörg & Viete, Steffen, 2016. "IT outsourcing and firm productivity: Eliminating bias from selective missingness in the dependent variable," ZEW Discussion Papers 16-092, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.

    More about this item

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L31 - Industrial Organization - - Nonprofit Organizations and Public Enterprise - - - Nonprofit Institutions; NGOs; Social Entrepreneurship

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