了解医疗保健中的爽约模式:来自意大利北部的一项回顾性研究。
Understanding No-Show Patterns in Healthcare: A Retrospective Study from Northern Italy.
作者信息
Russotto Antonino, Ragusa Paolo, Catozzi Dario, De Angelis Aldo, Durbano Alessandro, Siliquini Roberta, Orecchia Stefania
机构信息
Department of Sciences of Public Health and Paediatrics, University of Turin, 10126 Turin, Italy.
S.C. Distretto Sud-Est, ASL Città di Torino, 10128 Turin, Italy.
出版信息
Healthcare (Basel). 2025 Jul 30;13(15):1869. doi: 10.3390/healthcare13151869.
The aim of this study was to analyse no-show patterns in healthcare appointments, identify associated factors, and explore key determinants influencing non-attendance. This was a retrospective observational study. We analysed 120,405 healthcare appointments from 2022-2023 in Turin, Northern Italy. Data included demographics, appointment characteristics, and attendance records. Logistic regression identified significant predictors of no-shows, adjusting for confounders. A 5.1% (n = 6198) no-show percentage was observed. Younger patients (<18 years) and adults (18-65 years) had significantly higher odds of missing appointments than elderly patients (>65 years) (OR = 2.32, 95% CI: 2.17-2.47; OR = 2.46, 95% CI: 2.20-2.74; < 0.001). First-time visits had a higher no-show risk compared to follow-up visits and diagnostics (OR = 1.11, 95% CI: 1.04-1.18; < 0.001). Each additional day of waiting increased the likelihood of no-show by 1% (OR = 1.01, 95% CI: 1.01-1.01; < 0.001). No-show percentages are influenced by demographic and service-related factors. Strategies targeting younger patients, longer waiting times, and non-urgent appointments could reduce no-show percentages.
本研究的目的是分析医疗预约中的爽约模式,确定相关因素,并探索影响未就诊的关键决定因素。这是一项回顾性观察研究。我们分析了意大利北部都灵2022年至2023年期间的120405次医疗预约。数据包括人口统计学信息、预约特征和出勤记录。逻辑回归确定了爽约的显著预测因素,并对混杂因素进行了调整。观察到的爽约率为5.1%(n = 6198)。与老年患者(>65岁)相比,年轻患者(<18岁)和成年人(18 - 65岁)错过预约的几率显著更高(OR = 2.32,95% CI:2.17 - 2.47;OR = 2.46,95% CI:2.20 - 2.74;< 0.001)。与随访就诊和诊断相比,首次就诊的爽约风险更高(OR = 1.11,95% CI:1.04 - 1.18;< 0.001)。等待的每一天都会使爽约的可能性增加1%(OR = 1.01,95% CI:1.01 - 1.01;< 0.001)。爽约率受人口统计学和服务相关因素的影响。针对年轻患者、较长等待时间和非紧急预约的策略可以降低爽约率。
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