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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
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    References listed on IDEAS

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    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).

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