发表机构
University of California, San Francisco; University of Michigan, Ann Arbor; University of Washington, Seattle（加利福尼亚大学旧金山分校; 密歇根大学安娜堡分校; 华盛顿大学西雅图分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出专家引导g计算法（egg-computation），结合专家判断与LLM技术，用于估计医院干预措施对平均住院时长的因果效应，在模拟和真实医院数据中表现优于传统方法。
AI 中文摘要
医院质量改进（QI）项目常面临多种候选干预措施以优化医院流程，但现有方法难以估计并排序这些干预措施的因果效应。本研究聚焦于医院最常用指标之一的平均住院时长（LOS）及其因果估计量——平均节省时间。为刻画该因果效应，定性方法依赖专家判断来映射患者轨迹，易受认知偏差影响；定量方法依赖数据驱动模型，在干预措施为无历史数据的假设性措施或存在需临床推理而非仅数据的复杂因果机制时失效。我们提出专家引导g计算法，简称egg-computation，它结合了两种方法的互补优势，将常用于映射患者轨迹的甘特图与因果DAG文献相连接。我们在甘特图上引入因果模型，并使用仅对数据无法识别的组件寻求专家输入的g计算变体来确立识别性。为使egg-computation实用化，我们开发了一个LLM辅助的流程，可靠地扩展专家推理能力。在模拟实验中，当患者具有不同因果结构和干预机制时，egg-computation的表现优于传统因果推断方法。在对一家城市安全网医院的11项候选QI干预措施的研究中，该LLM流程生成的图表和节省时间估计值与人类专家的结果高度一致。除医疗保健领域外，egg-computation是一种广泛适用的框架，可用于估计其因果机制可通过甘特图表示的候选干预措施的平均节省时间。
英文摘要
Hospital quality improvement (QI) programs routinely face multiple candidate interventions to optimize hospital flow, but existing methods struggle to estimate and rank the causal effects of such interventions. This work focuses on one of the most standard hospital metrics, the average length of stay (LOS), and its causal estimand, the average time saved. To characterize this causal effect, qualitative approaches rely on expert judgment to map patient trajectories, making them susceptible to cognitive biases; quantitative approaches rely on data-driven models, which fail when interventions are hypothetical with no historical data or have complex causal mechanisms that require clinical reasoning rather than data alone. We propose expert-guided g-computation, or egg-computation, which combines the complementary strengths of both approaches by connecting the Gantt charts commonly used to map patient trajectories with the causal DAG literature. We introduce a causal model over Gantt charts and establish identification using a variant of g-computation that seeks expert input only for components unidentifiable from data. To make egg-computation practical, we develop an LLM-assisted pipeline that reliably scales up expert reasoning. In simulations, egg-computation outperforms conventional causal inference methods when patients have diverse causal structures and intervention mechanisms. In a study of eleven candidate QI interventions at an urban safety-net hospital, the LLM pipeline generated graphs and time-saving estimates highly concordant with those of human experts. Beyond healthcare, egg-computation is a broadly applicable framework for estimating the average time saved for candidate interventions whose causal mechanisms can be represented using Gantt charts.
