一种数据驱动的框架，用于识别和优先排序医疗流程中的RPA机会
A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
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中文总结 AI 辅助
提出一个四模块数据驱动框架，通过流程分类、优先级排序、工具层级选择和投资回报预测，系统化识别和优先排序医院RPA机会，并验证其鲁棒性和财务可行性。
中文摘要 AI 辅助
机器人流程自动化（RPA）被广泛用于减轻美国医院的行政负担，然而，估计有30-50%的RPA项目表现不佳，原因是流程选择是非正式的，缺乏可重复的方法来编目候选流程、对其进行优先级排序、将每个流程匹配到自动化层级——如Python机器人、开源编排器（如n8n）或企业平台（如UiPath）——并在投入资源之前预测财务回报。我们提出了一个由四个模块组成的数据驱动框架，以统一这些决策：一个流程分类法，涵盖五个价值流中的二十个常见医院流程；一个优先级排序模块，通过具有明确一致性检查的层次分析法矩阵推导出自动化适宜性指数；一个工具层级选择模块，推荐足以满足流程复杂性、集成和合规性要求的最低成本技术；以及一个投资回报模块，量化劳动力节省、错误成本避免、回收期和净现值。将该框架应用于涵盖所有二十个流程的合成投资组合，并辅以将其与医院EHR/支付方/ERP系统关联的参考数据流架构：20个流程中有12个通过了优先级阈值；该排名对±20%的权重扰动具有鲁棒性（Spearman相关系数为0.83，前5名集合保留率为97.7%，进行了2,000次蒙特卡洛试验）；一个自动化风险指数将四个符合条件的流程标记为高风险；一个受预算约束的投资组合优化显示，随着支出从40万美元增加到103万美元，边际净现值递减；第二次蒙特卡洛分析显示，投资组合净现值在其第5百分位仍保持正值。该框架是对文献的概念性综合，而非基于主要医院数据校准的工具；我们讨论了HIPAA治理和实证验证的研究议程。论文附带一个补充的Python实现。
英文摘要
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.
发表机构
- Universidad de La Salle（拉萨尔大学）
机构由 AI 辅助整理，请以论文原文为准。
