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The Impact of Information Technology on the Diffusion of New Pharmaceuticals

Author

Listed:
  • Kenneth J. Arrow
  • L. Kamran Bilir
  • Alan Sorensen

Abstract

Do information differences across US physicians contribute to treatment disparities? This paper uses a unique new dataset to evaluate how changes in physician access to a decision-relevant drug database affect prescribing decisions. Our results indicate doctors using the reference have a significantly greater propensity to prescribe generic drugs, are faster to begin prescribing new generics, and prescribe a more diverse set of products. These results are consistent with database users responding primarily to the increased accessibility of non-clinical information such as pricing and insurance formulary data, and suggest improvements to physician information access have important implications for aggregate healthcare costs.

Suggested Citation

  • Kenneth J. Arrow & L. Kamran Bilir & Alan Sorensen, 2020. "The Impact of Information Technology on the Diffusion of New Pharmaceuticals," American Economic Journal: Applied Economics, American Economic Association, vol. 12(3), pages 1-39, July.
    • Handle: RePEc:aea:aejapp:v:12:y:2020:i:3:p:1-39
    DOI: 10.1257/app.20170647
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    Citations

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    Cited by:

    1. Zhu, Z.;, 2023. "The Value of Patients: Heterogenous Physician Learning and Generic Drug Diffusion," Health, Econometrics and Data Group (HEDG) Working Papers 23/12, HEDG, c/o Department of Economics, University of York.
    2. Annie Tubadji & Toby Denney & Don J. Webber, 2021. "Cultural relativity in consumers' rates of adoption of artificial intelligence," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1234-1251, July.
    3. Méndez, Susan J. & Scott, Anthony & Zhang, Yuting, 2021. "Gender differences in physician decisions to adopt new prescription drugs," Social Science & Medicine, Elsevier, vol. 277(C).
    4. Hongyi Sun & Wenbin Ni & Pee-Lee Teh & Amrik Sohal, 2025. "An agent-mediated and entrepreneurship-oriented model for assessing high technology transfer," Information Technology and Management, Springer, vol. 26(1), pages 19-38, March.
    5. Eliana Barrenho & Eric Gautier & Marisa Miraldo & Carol Propper & Christiern Rose, 2020. "Innovation Diffusion and Physician Networks: Keyhole Surgery for Cancer in the English NHS," Discussion Papers Series 638, School of Economics, University of Queensland, Australia.
    6. Michael Gerlich, 2023. "Perceptions and Acceptance of Artificial Intelligence: A Multi-Dimensional Study," Social Sciences, MDPI, vol. 12(9), pages 1-24, September.
    7. McKibbin, Rebecca, 2023. "The effect of RCTs on drug demand: Evidence from off-label cancer drugs," Journal of Health Economics, Elsevier, vol. 90(C).
    8. Higgins, Matthew J. & Yan, Xin & Chatterjee, Chirantan, 2021. "Unpacking the effects of adverse regulatory events: Evidence from pharmaceutical relabeling," Research Policy, Elsevier, vol. 50(1).
    9. Sarbu, Miruna, 2022. "The impact of industry 4.0 on innovation performance: Insights from German manufacturing and service firms," Technovation, Elsevier, vol. 113(C).

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • L65 - Industrial Organization - - Industry Studies: Manufacturing - - - Chemicals; Rubber; Drugs; Biotechnology; Plastics

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