Ojalvo Israel, Mehran Nikki, Sharpe James, Zhang Qiang, Myers Jonathan S, Razeghinejad Reza, Lee Daniel, Kolomeyer Natasha Nayak
Glaucoma Service, Wills Eye Hospital, Philadelphia, Pennsylvania, USA.
Ophthalmology, SUNY Downstate Medical Center, Brooklyn, New York, USA.
Ophthalmic Epidemiol. 2025 Oct;32(5):543-552. doi: 10.1080/09286586.2024.2442367. Epub 2024 Dec 18.
To identify factors that are associated with no-shows and cancellations in a glaucoma clinic.
Retrospective observational study of patients seen at a glaucoma clinic over a two-year period (6/2017-5/2019). Demographics and clinic information were recorded from the electronic medical record. A total of 36,810 visits from 7,383 patients were studied. Weather data was collected from the National Centers for Environmental Information. Distance analysis was calculated utilizing Bing Maps application programming interface (API) on Microsoft Excel. Visits were divided into three groups based on appointment status: kept, cancelled, and no-show.
Bivariate analysis found a statistically significant difference in various factors amongst patients based on appointment status. Patients <15 miles from clinic had a higher rate of no-show, but a lower rate of cancellations compared to those farther ( < 0.0001) Using multivariable logistic regression, the following factors were associated with the likelihood of patient cancellation: average snowfall (Odds Ratio = 1.37); presence of storm event (OR = 1.12), new visit (OR = 1.82), follow-up appointments (OR = 1.90), and travel distance > 15 miles (OR = 1.11). The following factors were associated with patient no-show: resident clinic (OR = 1.79), new visit (OR = 2.24), follow-up appointments (OR = 2.18), age (OR = 0.99), average snowfall (OR = 1.27), presence of storm event (OR = 1.41), average windspeed (OR = 0.98), and travel distance > 15 miles (OR = 0.67).
Patient age, gender, travel distance, appointment type, and weather were all significantly associated with rates of patient cancellations and no-shows. These risk factors could lead to interventions to improve appointment adherence and patient retention. Weather is an under-analyzed factor in patient follow-up rates that warrants further investigation.
确定与青光眼诊所患者爽约和取消预约相关的因素。
对一家青光眼诊所在两年期间(2017年6月至2019年5月)接待的患者进行回顾性观察研究。从电子病历中记录人口统计学和诊所信息。共研究了7383名患者的36810次就诊情况。天气数据从美国国家环境信息中心收集。利用微软Excel上的必应地图应用程序编程接口(API)进行距离分析。根据预约状态将就诊分为三组:如约就诊、取消预约和爽约。
双变量分析发现,根据预约状态,患者在各种因素上存在统计学显著差异。与距离诊所较远的患者相比,距离诊所<15英里的患者爽约率较高,但取消预约率较低(<0.0001)。使用多变量逻辑回归分析,以下因素与患者取消预约的可能性相关:平均降雪量(优势比=1.37);有风暴事件(优势比=1.12)、初次就诊(优势比=1.82)、随访预约(优势比=1.90)以及行程距离>15英里(优势比=1.11)。以下因素与患者爽约相关:常驻诊所(优势比=1.79)、初次就诊(优势比=2.24)、随访预约(优势比=2.18)、年龄(优势比=0.99)、平均降雪量(优势比=1.27)、有风暴事件(优势比=1.41)、平均风速(优势比=0.98)以及行程距离>15英里(优势比=0.67)。
患者年龄、性别、行程距离、预约类型和天气均与患者取消预约和爽约率显著相关。这些风险因素可能促使采取干预措施以提高预约依从性和患者留存率。天气是患者随访率中一个未得到充分分析的因素,值得进一步研究。