IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v37y2019i6d10.1007_s40273-019-00777-6.html
   My bibliography  Save this article

Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges

Author

Listed:
  • David Thesmar

    (MIT)

  • David Sraer

    (UC Berkeley)

  • Lisa Pinheiro

    (Analysis Group, Inc.)

  • Nick Dadson

    (Analysis Group, Inc.)

  • Razvan Veliche

    (Analysis Group, Inc.)

  • Paul Greenberg

    (Analysis Group, Inc.)

Abstract

Combinations of healthcare claims data with additional datasets provide large and rich sources of information. The dimensionality and complexity of these combined datasets can be challenging to handle with standard statistical analyses. However, recent developments in artificial intelligence (AI) have led to algorithms and systems that are able to learn and extract complex patterns from such data. AI has already been applied successfully to such combined datasets, with applications such as improving the insurance claim processing pipeline and reducing estimation biases in retrospective studies. Nevertheless, there is still the potential to do much more. The identification of complex patterns within high dimensional datasets may find new predictors for early onset of diseases or lead to a more proactive offering of personalized preventive services. While there are potential risks and challenges associated with the use of AI, these are not insurmountable. As with the introduction of any innovation, it will be necessary to be thoughtful and responsible as we increasingly apply AI methods in healthcare.

Suggested Citation

  • David Thesmar & David Sraer & Lisa Pinheiro & Nick Dadson & Razvan Veliche & Paul Greenberg, 2019. "Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges," PharmacoEconomics, Springer, vol. 37(6), pages 745-752, June.
    • Handle: RePEc:spr:pharme:v:37:y:2019:i:6:d:10.1007_s40273-019-00777-6
    DOI: 10.1007/s40273-019-00777-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40273-019-00777-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40273-019-00777-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eugene Jeong & Namgi Park & Young Choi & Rae Woong Park & Dukyong Yoon, 2018. "Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    2. Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2021. "Measuring Bias in Consumer Lending [Loan Prospecting and the Loss of Soft Information]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(6), pages 2799-2832.
    3. Hirshleifer, Jack, 1971. "The Private and Social Value of Information and the Reward to Inventive Activity," American Economic Review, American Economic Association, vol. 61(4), pages 561-574, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mathias Bärtl & Simone Krummaker, 2020. "Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques," Risks, MDPI, vol. 8(1), pages 1-27, March.
    2. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.
    3. Cedric Kuang, 2019. "Insight of Artificial Intelligence Application in Healthcare," International Journal of Sciences, Office ijSciences, vol. 8(08), pages 50-55, August.
    4. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    5. Fatma Khamis Al Badi & Khawla Ali Alhosani & Fauzia Jabeen & Agata Stachowicz-Stanusch & Nazia Shehzad & Wolfgang AMANN, 2022. "Challenges of AI Adoption in the UAE Healthcare," Vision, , vol. 26(2), pages 193-207, June.
    6. Zahlan, Ahmed & Ranjan, Ravi Prakash & Hayes, David, 2023. "Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research," Technology in Society, Elsevier, vol. 74(C).

    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. Boot, Arnoud & Hoffmann, Peter & Laeven, Luc & Ratnovski, Lev, 2021. "Fintech: what’s old, what’s new?," Journal of Financial Stability, Elsevier, vol. 53(C).
    2. Laeven, Luc & Boot, Arnoud & Hoffmann, Peter & Ratnovski, Lev, 2020. "Financial Intermediation and Technology: What’s Old, What’s New?," CEPR Discussion Papers 15004, C.E.P.R. Discussion Papers.
    3. König, Philipp J. & Pothier, David, 2018. "Safe but fragile: Information acquisition, sponsor support and shadow bank runs," Discussion Papers 15/2018, Deutsche Bundesbank.
    4. Heine Klaus & Mause Karsten, 2003. "Politikberatung als informationsökonomisches Problem," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 223(4), pages 479-490, August.
    5. Anup Malani & Cynthia Kinnan & Gabriella Conti & Kosuke Imai & Morgen Miller & Shailender Swaminathan & Alessandra Voena & Bartosz Woda, 2024. "Evaluating pricing health insurance in lower-income countries: A field experiment in India," IFS Working Papers W24/33, Institute for Fiscal Studies.
    6. Goldstein, Itay & Yang, Liyan, 2019. "Good disclosure, bad disclosure," Journal of Financial Economics, Elsevier, vol. 131(1), pages 118-138.
    7. Baker, Erin, 2005. "Uncertainty and learning in a strategic environment: global climate change," Resource and Energy Economics, Elsevier, vol. 27(1), pages 19-40, January.
    8. Pakes, Ariel & Schankerman, Mark A., 1978. "The Rate of Obsolescence of Knowledge, Research Gestation Labs, and the Private Rate of Return to Research Resources," Working Papers 78-13, C.V. Starr Center for Applied Economics, New York University.
    9. Matthias Schmidt & Hermann Held & Elmar Kriegler & Alexander Lorenz, 2013. "Climate Policy Under Uncertain and Heterogeneous Climate Damages," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 54(1), pages 79-99, January.
    10. Bertoletti, Lucía & Borraz, Fernando & Sanroman, Graciela, 2024. "Consumer Debt and Poverty: the Default Risk Gap," GLO Discussion Paper Series 1439, Global Labor Organization (GLO).
    11. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    12. Renaud Bourlès & Dominique Henriet, 2012. "Risk-sharing Contracts with Asymmetric Information," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 37(1), pages 27-56, March.
    13. Vernon Ruttan, 1980. "Bureaucratic productivity: The case of agricultural research," Public Choice, Springer, vol. 35(5), pages 529-547, January.
    14. Carrillo, Juan D. & Palfrey, Thomas R., 2011. "No trade," Games and Economic Behavior, Elsevier, vol. 71(1), pages 66-87, January.
    15. J. Michelle Brock & Ralph De Haas, 2023. "Discriminatory Lending: Evidence from Bankers in the Lab," American Economic Journal: Applied Economics, American Economic Association, vol. 15(2), pages 31-68, April.
    16. Barro, Robert J & Friedman, James W, 1977. "On Uncertain Lifetimes," Journal of Political Economy, University of Chicago Press, vol. 85(4), pages 843-849, August.
    17. K.J.M. De Jaegher, 2012. "The value of private information in the physician-patient relationship: a gametheoretic account," Working Papers 12-23, Utrecht School of Economics.
    18. Foucault, Thierry & Cespa, Giovanni, 2008. "Insiders-outsiders, transparency and the value of the ticker," HEC Research Papers Series 892, HEC Paris.
    19. Liu, Jun & Peleg, Ehud & Subrahmanyam, Avanidhar, 2004. "The Value of Private Information," University of California at Los Angeles, Anderson Graduate School of Management qt71t9z3w3, Anderson Graduate School of Management, UCLA.
    20. Banerjee, Anurag N. & Seccia, Giulio, 2002. "On the "Hirshleifer effect'' of unscheduled monetary policy announcements," Discussion Paper Series In Economics And Econometrics 0213, Economics Division, School of Social Sciences, University of Southampton.

    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:spr:pharme:v:37:y:2019:i:6:d:10.1007_s40273-019-00777-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.