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.

据估计，在美国，七分之一的健康保险索赔被拒绝；由于拒绝索赔，全国各地的医院每年损失约2620亿美元。这一普遍存在的问题导致了巨大的现金流问题，并使患者负担沉重。因此，在将索赔提交给保险公司之前防止索赔被拒绝可以提高盈利能力，加速收入周期并支持患者的健康。这项研究利用设计科学研究（DSR）范式，并开发了一种负责任的人工智能（RAI）解决方案，可帮助医院管理者识别可能被拒绝的索赔。该框架以五项原则为指导，利用六种AI算法（分为白盒和玻璃盒）进行分类，并采用交叉验证来调整超参数并确定最佳模型。结果表明，白盒算法（AdaBoost）模型的AUC率为0.83，优于所有其他模型。这项研究的主要含义是（1）帮助提供者降低运营成本并提高保险理赔流程的效率（2）帮助患者专注于康复而不是处理上诉性理赔。