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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
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