机器人流程自动化 (RPA) 被广泛用于减轻美国医院的管理负担,，但估计有 30-50% 的 RPA 计划表现不佳，因为流程是非正式选择的, 没有可重复的方法来对候选者进行分类, 对它们进行优先排序, 将每个流程与自动化层进行匹配 - Python 机器人, 开源协调器，例如 n8n, 或企业平台，例如 UiPath - 并在承诺之前预测财务回报资源。我们提出了一个统一这些决策的四个模块,数据驱动框架:跨五个价值流的20个重复医院流程的流程分类;一个优先级模块，从具有显式一致性检查的分析层次过程矩阵导出自动化适应性指数;一个工具层选择模块，推荐足以满足流程复杂性,集成,和合规性概况;的最低成本技术，以及量化投资回报率模块节省劳动力, 错误成本避免, 回报, 和净现值。应用于涵盖所有二十个流程的综合组合, 加上将其链接到医院 EHR/payer/ERP 系统的参考数据流架构: 20 中的 12 个明确了优先级阈值; 该排名对于 +/-20% 权重扰动具有稳健性 (Spearman 相关性 0.83, 前 5 组保留 97.7%, 2,000 蒙特卡罗试验); 自动化风险指数将四个合格流程标记为关键风险; 预算受限的投资组合优化显示，随着支出从 $400K 扩大到 $1.03M;，边际 NPV 不断减少，第二个蒙特卡罗分析显示，投资组合 NPV 在第 5 个百分位处保持正值。该框架是文献的概念综合，而不是根据基层医院数据; 校准的工具，我们讨论 HIPAA 治理和实证验证的研究议程。本文附带了一个补充的 Python 实现。

Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives underperform because processes are selected informally, without a repeatable method to catalogue candidates, prioritize them, match each to an automation tier -- a Python bot, an open-source orchestrator such as n8n, or an enterprise platform such as UiPath -- and forecast financial return before committing resources. We propose a four-module, data-driven framework unifying these decisions: a Process Taxonomy of twenty recurring hospital processes across five value streams; a Prioritization module deriving an Automation Suitability Index from an Analytic Hierarchy Process matrix with an explicit consistency check; a Tool-Tier Selection module recommending the least-cost technology sufficient for a process complexity, integration, and compliance profile; and a Return-on-Investment module quantifying labor savings, error-cost avoidance, payback, and net present value. Applied to a synthetic portfolio spanning all twenty processes, plus a reference data-flow architecture linking it to hospital EHR/payer/ERP systems: 12 of 20 clear the prioritization threshold; the ranking is robust to +/-20% weight perturbation (Spearman correlation 0.83, top-5 set preserved 97.7%, 2,000 Monte Carlo trials); an Automation Risk Index flags four qualifying processes as Critical risk; a budget-constrained portfolio optimization shows diminishing marginal NPV as spend scales from $400K to $1.03M; and a second Monte Carlo analysis shows portfolio NPV stays positive at its 5th percentile. The framework is a conceptual synthesis of the literature rather than an instrument calibrated on primary hospital data; we discuss HIPAA governance and a research agenda for empirical validation. A supplementary Python implementation accompanies the paper.

科目: 人工智能 (cs.AI); 计算和语言 (cs.CL)

Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)