Husman Tiffany, Miraftab-Salo Omeed, Ilan Alonn, Brodie Shauna, Paciorek Alan, Durr Megan L, Chang Jolie, Xu Mary Jue
School of Medicine, University of California, San Francisco, San Francisco, California, USA.
Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco Department of Public Health, San Francisco, California, USA.
Laryngoscope. 2025 Sep;135(9):3123-3133. doi: 10.1002/lary.32150. Epub 2025 Mar 26.
Identify predictors of patient no-show at an urban safety-net otolaryngology outpatient clinic.
Retrospective cohort study including all scheduled patients and appointments in 2023. Predictor variables included sociodemographic factors, primary diagnosis, insurance, and the neighborhood deprivation index (NDI) based on census tract information. The outcome was analyzed as a binary variable using univariate and multivariate mixed-effects logistic regression models.
Among 2339 patients and 4641 scheduled appointments, 1639 patients completed all scheduled visits and 700 (29.9%) missed at least 1 visit. Among all appointments, 18.4% were missed. The prior no-show rate was 9% (IQR 4%-18%), and days from scheduling to appointment was 42 days (IQR 19-75). Univariate analysis demonstrated significant sociodemographic factors associated with higher odds of missing an appointment, including NDI (OR 1.03, p = 0.001), male gender (OR 1.35, p = 0.004), Black/African American race (OR 1.49, p = 0.022), unemployment and disability status (OR 1.45, p = 0.007 and OR 2.12, p < 0.001 respectively), unstable/unknown housing (OR 3.66, p < 0.001), and sexual orientation as lesbian or gay (OR 1.93, p = 0.003). NDI remained a significant factor in multivariate analysis (OR 1.03, p = 0.001). Patient portal inactivation and lead time were significant intervenable factors in multivariate analysis (OR 1.23, p = 0.049 and OR 1.26, p < 0.001, respectively).
NDI, patient portal activation, and time from appointment scheduling to visit are significant predictors of patient no-show. This study offers insights into potential interventions addressing specific barriers to improving patient no-show rates for an urban, safety-net outpatient population.
3 (retrospective cohort study).
确定城市安全网耳鼻喉科门诊患者爽约的预测因素。
回顾性队列研究,纳入2023年所有预约患者及预约情况。预测变量包括社会人口学因素、主要诊断、保险以及基于普查区信息的邻里贫困指数(NDI)。采用单变量和多变量混合效应逻辑回归模型将结果作为二元变量进行分析。
在2339例患者和4641次预约中,1639例患者完成了所有预约就诊,700例(29.9%)至少错过1次就诊。在所有预约中,18.4%被错过。既往爽约率为9%(四分位间距4%-18%),从预约到就诊的天数为42天(四分位间距19-75天)。单变量分析显示,与错过预约几率较高相关的显著社会人口学因素包括NDI(比值比1.03,p = 0.001)、男性(比值比1.35,p = 0.004)、黑人/非裔美国人种族(比值比1.49,p = 0.022)、失业和残疾状况(分别为比值比1.45,p = 0.007和比值比2.12,p < 0.001)、不稳定/未知住房(比值比3.66,p < 0.001)以及女同性恋或男同性恋性取向(比值比1.93,p = 0.003)。NDI在多变量分析中仍是一个显著因素(比值比1.03,p = 0.001)。患者门户停用和提前期在多变量分析中是显著的可干预因素(分别为比值比1.23,p = 0.049和比值比1.26,p < 0.001)。
NDI、患者门户激活以及从预约安排到就诊的时间是患者爽约的显著预测因素。本研究为解决城市安全网门诊人群提高患者爽约率的特定障碍的潜在干预措施提供了见解。
3(回顾性队列研究)。