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. Goldstein, Itay & Yang, Liyan, 2019. "Good disclosure, bad disclosure," Journal of Financial Economics, Elsevier, vol. 131(1), pages 118-138.
    5. 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.
    6. 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.
    7. Bertoletti, Lucía & Borraz, Fernando & Sanroman, Graciela, 2024. "Consumer Debt and Poverty: the Default Risk Gap," GLO Discussion Paper Series 1439, Global Labor Organization (GLO).
    8. 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.
    9. 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.
    10. Piero Gottardi & Rohit Rahi, 2014. "Value Of Information In Competitive Economies With Incomplete Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(1), pages 57-81, February.
    11. Kondor, Péter & Zawadowski, Adam, 2019. "Learning in crowded markets," Journal of Economic Theory, Elsevier, vol. 184(C).
    12. Boncinelli Leonardo, 2008. "Global vs. Local Information in (Anti-)Coordination Problems with Imitators," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 8(1), pages 1-19, June.
    13. 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.
    14. Nathaniel Hendren & Camille Landais & Johannes Spinnewijn, 2021. "Choice in Insurance Markets: A Pigouvian Approach to Social Insurance Design," Annual Review of Economics, Annual Reviews, vol. 13(1), pages 457-486, August.
    15. Manuel Amador & Pierre-Olivier Weill, 2010. "Learning from Prices: Public Communication and Welfare," Journal of Political Economy, University of Chicago Press, vol. 118(5), pages 866-907.
    16. Roger H. Gordon & Jeffrey K. MacKie-Mason, 1995. "Why Is There Corporate Taxation in a Small Open Economy? The Role of Transfer Pricing and Income Shifting," NBER Chapters, in: The Effects of Taxation on Multinational Corporations, pages 67-94, National Bureau of Economic Research, Inc.
    17. Falkinger, Josef, 2016. "The order of knowledge and robust action: How to deal with economic uncertainty?," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-30.
    18. Florian Gauer & Christoph Kuzmics, 2020. "Cognitive Empathy In Conflict Situations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1659-1678, November.
    19. Pagano, Marco & Picariello, Luca, 2023. "Talent discovery, layoff risk and unemployment insurance," European Economic Review, Elsevier, vol. 154(C).
    20. Brice Corgnet & Cary Deck & Mark DeSantis & Kyle Hampton & Erik O. Kimbrough, 2023. "When Do Security Markets Aggregate Dispersed Information?," Management Science, INFORMS, vol. 69(6), pages 3697-3729, June.

    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.