发表机构
University of Bologna（博洛尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用意大利1608名1型糖尿病患者的交错采纳数据，结合匹配与事件研究设计，发现自动胰岛素输送增加而非减少门诊就诊，表明自动化重组而非替代临床护理。
AI 中文摘要
医疗技术是减少还是增加对专业护理的需求，是理解其对医疗利用和成本影响的核心问题。我们研究了在意大利国家卫生服务体系四家专科诊所接受治疗的1608名1型糖尿病成人患者中自动胰岛素输送（AID）的采纳情况，其中283人采纳了AID。由于采纳具有临床针对性和交错性，我们将风险集粗化精确匹配与交错事件研究设计相结合。匹配保留了181名采纳者及其可比的未采纳对照组。我们进一步通过外推事件时间中未采纳者的轨迹，调整了采纳前差异趋势。调整后，AID采纳与更频繁的常规门诊参与相关。对糖尿病专科医生就诊概率的估计效应在采纳后一个学期为16.3个百分点，在采纳后四个学期为39.3个百分点，相对于采纳前一学期55.2%的就诊概率。HbA1c检测和更广泛的护理过程指数显示出类似的积极模式，尽管证据的精确度较低。就诊估计对趋势外推假设仍然敏感，因此应在该限制条件下进行解释。这些发现与基于任务的观点一致，即自动化替代了常规剂量决策，同时通过解释、调整和监督任务补充了临床劳动。因此，面向患者的自动化可能重组而非简单减少医疗使用。
英文摘要
Whether medical technology reduces or increases demand for professional care is central to understanding its effects on healthcare utilization and costs. We study automated insulin delivery (AID) adoption among 1,608 adults with type 1 diabetes treated in four specialist clinics of the Italian National Health Service, 283 of whom adopt AID. Because adoption is clinically targeted and staggered, we combine risk-set coarsened exact matching with a staggered event-study design. Matching retains 181 adopters with comparable untreated controls. We further adjust for differential pre-adoption trends by extrapolating the untreated trajectory in event time. After adjustment, AID adoption is associated with greater routine outpatient engagement. The estimated effect on the probability of a diabetologist visit is 16.3 percentage points one semester after adoption and 39.3 percentage points four semesters after adoption, relative to a 55.2% visit probability in the semester before adoption. HbA1c testing and a broader process-of-care index show similar positive patterns, although the evidence is less precise. The visit estimates remain sensitive to the trend-extrapolation assumption and should therefore be interpreted conditionally on that restriction. The findings are consistent with a task-based view in which automation substitutes for routine dosing decisions while complementing clinical labor through interpretive, adjustment, and supervisory tasks. Patient-facing automation may therefore reorganize rather than simply reduce healthcare use.
