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

Marina Johnson, Abdullah Albizri and Antoine Harfouche ()
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Marina Johnson: Montclair State University
Abdullah Albizri: Montclair State University
Antoine Harfouche: University Paris Nanterre

Information Systems Frontiers, 2023, vol. 25, issue 6, No 5, 2179-2195

Abstract: 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.

Keywords: Artificial intelligence; Analytics; Insurance claim denials; Design science; Responsible AI; White-box models (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-021-10137-5

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