IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v25y2023i6d10.1007_s10796-021-10137-5.html
   My bibliography  Save this article

Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing

Author

Listed:
  • Marina Johnson

    (Montclair State University)

  • Abdullah Albizri

    (Montclair State University)

  • Antoine Harfouche

    (University Paris Nanterre)

Abstract

It is estimated that one out of seven health insurance claims is rejected in the US; hospitals across the country lose approximately $262 billion annually due to denied claims. This widespread problem causes huge cash-flow issues and overburdens patients. Thus, preventing claim denials before claims are submitted to insurers improves profitability, accelerates the revenue cycle, and supports patients’ wellbeing. This study utilizes Design Science Research (DSR) paradigm and develops a Responsible Artificial Intelligence (RAI) solution helping hospital administrators identify potentially denied claims. Guided by five principles, this framework utilizes six AI algorithms – classified as white-box and glass-box – and employs cross-validation to tune hyperparameters and determine the best model. The results show that a white-box algorithm (AdaBoost) model yields an AUC rate of 0.83, outperforming all other models. This research’s primary implications are to (1) help providers reduce operational costs and increase the efficiency of insurance claim processes (2) help patients focus on their recovery instead of dealing with appealing claims.

Suggested Citation

  • Marina Johnson & Abdullah Albizri & Antoine Harfouche, 2023. "Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing," Information Systems Frontiers, Springer, vol. 25(6), pages 2179-2195, December.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:6:d:10.1007_s10796-021-10137-5
    DOI: 10.1007/s10796-021-10137-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-021-10137-5
    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/s10796-021-10137-5?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. Papanicolas, Irene & Woskie, Liana R. & Jha, Ashish K., 2018. "Health care spending in the United States and other high-income countries," LSE Research Online Documents on Economics 87362, London School of Economics and Political Science, LSE Library.
    2. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    3. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, March.
    4. Zhang, Yongli & Yang, Yuhong, 2015. "Cross-validation for selecting a model selection procedure," Journal of Econometrics, Elsevier, vol. 187(1), pages 95-112.
    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. Farid Gasmi & Paul Noumba Um & Laura Recuero Virto & Peter Saba, 2024. "Digital Literacy, Sustainable Development and Radiation Regulation: Policy and Information Systems Implications," Information Systems Frontiers, Springer, vol. 26(6), pages 2027-2057, December.
    2. Uthayasankar Sivarajah & Yichuan Wang & Hossein Olya & Sherin Mathew, 2023. "Responsible Artificial Intelligence (AI) for Digital Health and Medical Analytics," Information Systems Frontiers, Springer, vol. 25(6), pages 2117-2122, December.
    3. Antoine Harfouche & Mohammad I. Merhi & Abdullah Albizri & Denis Dennehy & Jason Bennett Thatcher, 2024. "Sustainable Development Through Technological Innovations and Data Analytics," Information Systems Frontiers, Springer, vol. 26(6), pages 1989-1996, December.

    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. Chen, Changdong, 2024. "How consumers respond to service failures caused by algorithmic mistakes: The role of algorithmic interpretability," Journal of Business Research, Elsevier, vol. 176(C).
    2. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    3. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    4. Ortal Slobodin & Ilia Plochotnikov & Idan-Chaim Cohen & Aviad Elyashar & Odeya Cohen & Rami Puzis, 2022. "Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis," IJERPH, MDPI, vol. 19(11), pages 1-17, June.
    5. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
    6. Jan Ole Krugmann & Jochen Hartmann, 2024. "Sentiment Analysis in the Age of Generative AI," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 11(1), pages 1-19, December.
    7. Nan Zhang & Heng Xu, 2024. "Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning," Information Systems Research, INFORMS, vol. 35(2), pages 469-488, June.
    8. Vangelis Marinakis & Themistoklis Koutsellis & Alexandros Nikas & Haris Doukas, 2021. "AI and Data Democratisation for Intelligent Energy Management," Energies, MDPI, vol. 14(14), pages 1-14, July.
    9. Maury R. Randall & David Y. Suk & Kristin McCarthy, 2021. "U.S. Health Care: Too Much Of A Good Thing?," International Journal of Management and Marketing Research, The Institute for Business and Finance Research, vol. 14(1), pages 19-33.
    10. Gundula Krack, 2019. "How to make value-based health insurance designs more effective? A systematic review and meta-analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(6), pages 841-856, August.
    11. Zeckhauser, Richard, 2021. "Strategic sorting: the role of ordeals in health care," Economics and Philosophy, Cambridge University Press, vol. 37(1), pages 64-81, March.
    12. Arnold P M van der Lee & Lieuwe de Haan & Aartjan T F Beekman, 2019. "Rising co-payments coincide with unwanted effects on continuity of healthcare for patients with schizophrenia in the Netherlands," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    13. Sophie van Huellen & Duo Qin, 2019. "Compulsory Schooling and Returns to Education: A Re-Examination," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    14. Mark Gilchrist & Deana Lehmann Mooers & Glenn Skrubbeltrang & Francine Vachon, 2012. "Knowledge Discovery in Databases for Competitive Advantage," Journal of Management and Strategy, Journal of Management and Strategy, Sciedu Press, vol. 3(2), pages 2-15, April.
    15. Fadlon, Itzik & Van Parys, Jessica, 2020. "Primary care physician practice styles and patient care: Evidence from physician exits in Medicare," Journal of Health Economics, Elsevier, vol. 71(C).
    16. Guangbo Ma & Kun Xu, 2022. "Value-Based Health Care: Long-Term Care Insurance for Out-of-Pocket Medical Expenses and Self-Rated Health," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
    17. Helena Weith & Christian Matt, 2023. "Information provision measures for voice agent product recommendations— The effect of process explanations and process visualizations on fairness perceptions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-20, December.
    18. Agha, Leila & Frandsen, Brigham & Rebitzer, James B., 2019. "Fragmented division of labor and healthcare costs: Evidence from moves across regions," Journal of Public Economics, Elsevier, vol. 169(C), pages 144-159.
    19. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    20. Valentina Nino & David Claudio & Leonardo Valladares & Sean Harris, 2020. "An Enhanced Kaizen Event in a Sterile Processing Department of a Rural Hospital: A Case Study," IJERPH, MDPI, vol. 17(23), pages 1-20, November.

    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:infosf:v:25:y:2023:i:6:d:10.1007_s10796-021-10137-5. 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.