INTRODUCTION We estimate the economic impact of applying risk assessment tools to identify very low-risk patients with upper gastrointestinal bleeding who can be safely discharged from the emergency department using a cost minimization analysis. METHODS We compare triage strategies (Glasgow-Blatchford score = 0/0-1 or validated machine learning model) with usual care using a Markov chain model from a US health care payer perspective. RESULTS Over 5 years, the Glasgow-Blatchford score triage strategy produced national cumulative savings over usual care of more than $2.7 billion and the machine learning strategy of more than $3.4 billion. DISCUSSION Implementing risk assessment models for upper gastrointestinal bleeding reduces costs, thereby increasing value.

中文翻译：

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通过机器学习模型或急性上消化道出血临床风险评分的风险分层实现价值：成本最小化分析。

引言 我们通过成本最小化分析，评估了应用风险评估工具来识别极低风险的上消化道出血患者的经济影响，这些患者可以安全地从急诊科出院。方法 我们从美国医疗保健支付者的角度使用马尔可夫链模型将分诊策略（Glasgow-Blatchford 评分 = 0/0-1 或经过验证的机器学习模型）与常规护理进行比较。结果 5 年来，格拉斯哥-布拉奇福德评分分类策略在全国范围内为常规护理累计节省了超过 27 亿美元，而机器学习策略则节省了超过 34 亿美元。讨论 实施上消化道出血风险评估模型可以降低成本，从而增加价值。