IDEAS home Printed from https://ideas.repec.org/p/osf/thesis/tc24d.html
   My bibliography  Save this paper

Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?

Author

Listed:
  • Ayoubi, Charles

Abstract

The ageing population in all developed economies and the limited productivity characterizing the healthcare sector are leading to alarmingly increasing costs. The current rapid advances in machine learn-ing (ML), a subfield of artificial intelligence (AI), offer new automation and prediction capabilities that could, if properly integrated, help address the healthcare costs deadlock. Are ML-driven solutions the ap-propriate ingredient to produce this necessary transformation, or are they condemned to face the same destiny as previous attempts to remodel healthcare delivery? This paper aims at bringing first elements to answer this question by providing both qualitative and quantitative evidence on the development of ML in healthcare and discussing the organizational and institutional conditions for the ML potential to be real-ized. Building on a novel search methodology for publications and patents in ML and on hospital surveys, our results reveal two major observations. On the one hand, while the publication rate in the field has tripled in the last decade, the level of patenting in ML applied to healthcare has so far been relatively low. This result has several potential explanations, such as the early stage of the technology, its rapid growth, and the emergence of new business models based on data accumulation and appropriation rather than patenting. On the other hand, the bulk of firms’ publications are produced by IT firms rather than by com-panies in healthcare. This last observation seems to be driven by the disruptiveness of the new ML tech-nology allowing the entry of new actors in healthcare. The technology producers benefit from their mas-tery of ML and the lack of investment and capabilities among health experts.

Suggested Citation

  • Ayoubi, Charles, 2020. "Machine learning in healthcare: Mirage or miracle for breaking the costs dead-lock?," Thesis Commons tc24d, Center for Open Science.
  • Handle: RePEc:osf:thesis:tc24d
    DOI: 10.31219/osf.io/tc24d
    as

    Download full text from publisher

    File URL: https://osf.io/download/64a81a1c19252700dbc7aa43/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/tc24d?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David M. Cutler, 2011. "Where Are the Health Care Entrepreneurs? The Failure of Organizational Innovation in Health Care," NBER Chapters, in: Innovation Policy and the Economy, Volume 11, pages 1-28, National Bureau of Economic Research, Inc.
    2. James Bessen & Robert M. Hunt, 2007. "An Empirical Look at Software Patents," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 16(1), pages 157-189, March.
    3. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    4. Cutler, David M & Richardson, Elizabeth, 1998. "The Value of Health: 1970-1990," American Economic Review, American Economic Association, vol. 88(2), pages 97-100, May.
    5. Azar, José & Alekseeva, Liudmila & Gine, Mireia & Samila, Sampsa & Taska, Bledi, 2020. "The Demand for AI Skills in the Labor Market," CEPR Discussion Papers 14320, C.E.P.R. Discussion Papers.
    6. Brent Goldfarb, 2005. "Diffusion of general-purpose technologies: understanding patterns in the electrification of US Manufacturing 1880--1930," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 14(5), pages 745-773, October.
    7. Amitabh Chandra & Jonathan Skinner, 2012. "Technology Growth and Expenditure Growth in Health Care," Journal of Economic Literature, American Economic Association, vol. 50(3), pages 645-680, September.
    8. David Autor & Anna Salomons, 2018. "Is Automation Labor-Displacing? Productivity Growth, Employment, and the Labor Share," NBER Working Papers 24871, National Bureau of Economic Research, Inc.
    9. Carl Shapiro, 2019. "Protecting Competition in the American Economy: Merger Control, Tech Titans, Labor Markets," Journal of Economic Perspectives, American Economic Association, vol. 33(3), pages 69-93, Summer.
    10. Jinhyung Lee & Jeffrey S. McCullough & Robert J. Town, 2013. "The impact of health information technology on hospital productivity," RAND Journal of Economics, RAND Corporation, vol. 44(3), pages 545-568, September.
    11. Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    12. Elhanan Helpman & Manuel Trajtenberg, 1996. "Diffusion of General Purpose Technologies," NBER Working Papers 5773, National Bureau of Economic Research, Inc.
    13. Trajtenberg, Manuel, 2018. "AI as the next GPT: a Political-Economy Perspective," CEPR Discussion Papers 12721, C.E.P.R. Discussion Papers.
    14. Baumol, William J, 1993. "Health Care, Education and the Cost Disease: A Looming Crisis for Public Choice," Public Choice, Springer, vol. 77(1), pages 17-28, September.
    15. Miric, Milan & Boudreau, Kevin J. & Jeppesen, Lars Bo, 2019. "Protecting their digital assets: The use of formal & informal appropriability strategies by App developers," Research Policy, Elsevier, vol. 48(8), pages 1-1.
    16. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    17. Charles Ayoubi & Boris Thurm, 2023. "Knowledge diffusion and morality: Why do we freely share valuable information with Strangers?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(1), pages 75-99, January.
    18. Erik Brynjolfsson & Tom Mitchell & Daniel Rock, 2018. "What Can Machines Learn, and What Does It Mean for Occupations and the Economy?," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 43-47, May.
    19. Goolsbee, Austan & Klenow, Peter J, 2002. "Evidence on Learning and Network Externalities in the Diffusion of Home Computers," Journal of Law and Economics, University of Chicago Press, vol. 45(2), pages 317-343, October.
    20. Matt Taddy, 2018. "The Technological Elements of Artificial Intelligence," NBER Working Papers 24301, National Bureau of Economic Research, Inc.
    21. Igal Hendel & Yossi Spiegel, 2014. "Small Steps for Workers, a Giant Leap for Productivity," American Economic Journal: Applied Economics, American Economic Association, vol. 6(1), pages 73-90, January.
    22. Aizcorbe, Ana & Nestoriak, Nicole, 2011. "Changing mix of medical care services: Stylized facts and implications for price indexes," Journal of Health Economics, Elsevier, vol. 30(3), pages 568-574, May.
    23. Ajay Agrawal & John McHale & Alexander Oettl, 2018. "Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 149-174, National Bureau of Economic Research, Inc.
    24. Martin Feldstein, 2017. "Underestimating the Real Growth of GDP, Personal Income, and Productivity," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 145-164, Spring.
    25. David M. Cutler, 2011. "Where Are the Health Care Entrepreneurs? The Failure of Organizational Innovation in Health Care," Innovation Policy and the Economy, University of Chicago Press, vol. 11(1), pages 1-28.
    26. Naomi Fukuzawa & Takanori Ida, 2016. "Science linkages between scientific articles and patents for leading scientists in the life and medical sciences field: the case of Japan," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 629-644, February.
    27. Chris Bojke & Adriana Castelli & Katja Grašič & Andrew Street, 2017. "Productivity Growth in the English National Health Service from 1998/1999 to 2013/2014," Health Economics, John Wiley & Sons, Ltd., vol. 26(5), pages 547-565, May.
    28. Henry Chesbrough & Richard S. Rosenbloom, 2002. "The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 11(3), pages 529-555, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    2. Jens Prüfer & Patricia Prüfer, 2020. "Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands," Small Business Economics, Springer, vol. 55(3), pages 651-672, October.
    3. Ari Bronsoler & Joseph J. Doyle Jr. & John Van Reenen, 2021. "The Impact of Healthcare IT on Clinical Quality, Productivity and Workers," NBER Working Papers 29218, National Bureau of Economic Research, Inc.
    4. Jasmine Mondolo, 2022. "The composite link between technological change and employment: A survey of the literature," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1027-1068, September.
    5. Genz, Sabrina & Schnabel, Claus, 2021. "Digging into the digital divide: Workers' exposure to digitalization and its consequences for individual employment," Discussion Papers 118, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics.
    6. Yang, Chih-Hai, 2022. "How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan," Research Policy, Elsevier, vol. 51(6).
    7. Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.
    8. Fabio Montobbio & Jacopo Staccioli & Maria Enrica Virgillito & Marco Vivarelli, 2022. "The empirics of technology, employment and occupations: lessons learned and challenges ahead," DISCE - Quaderni del Dipartimento di Politica Economica dipe0028, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    9. Stefano Bianchini & Moritz Müller & Pierre Pelletier, 2022. "Artificial intelligence in science: An emerging general method of invention," Post-Print hal-03958025, HAL.
    10. Gordon H. Hanson, 2021. "Immigration and Regional Specialization in AI," NBER Working Papers 28671, National Bureau of Economic Research, Inc.
    11. Abe Dunn & Eli Liebman & Adam Hale Shapiro, 2016. "Decomposing Medical Care Expenditure Growth," NBER Chapters, in: Measuring and Modeling Health Care Costs, pages 81-111, National Bureau of Economic Research, Inc.
    12. Venturini, Francesco, 2022. "Intelligent technologies and productivity spillovers: Evidence from the Fourth Industrial Revolution," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 220-243.
    13. Andrea Szalavetz, 2019. "Artificial Intelligence-Based Development Strategy in Dependent Market Economies - Any Room amidst Big Power Rivalry?," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 40-54.
    14. Fulian Li & Wuwei Zhang, 2023. "Research on the Effect of Digital Economy on Agricultural Labor Force Employment and Its Relationship Using SEM and fsQCA Methods," Agriculture, MDPI, vol. 13(3), pages 1-17, February.
    15. Benjamin R. Handel & Jonathan T. Kolstad, 2021. "The Affordable Care Act After a Decade: Industrial Organization of the Insurance Exchanges," NBER Working Papers 29178, National Bureau of Economic Research, Inc.
    16. Gruetzemacher, Ross & Paradice, David & Lee, Kang Bok, 2020. "Forecasting extreme labor displacement: A survey of AI practitioners," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    17. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    18. Lucrezia Fanti & Dario Guarascio & Massimo Moggi, 2022. "From Heron of Alexandria to Amazon’s Alexa: a stylized history of AI and its impact on business models, organization and work," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 409-440, September.
    19. Cebreros Alfonso & Heffner-Rodríguez Aldo & Livas René & Puggioni Daniela, 2020. "Automation Technologies and Employment at Risk: The Case of Mexico," Working Papers 2020-04, Banco de México.
    20. Aaron K. Chatterji, 2017. "Innovation and American K-12 Education," NBER Chapters, in: Innovation Policy and the Economy, Volume 18, pages 27-51, National Bureau of Economic Research, Inc.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:thesis:tc24d. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://thesiscommons.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.