Factors associated with nonattendance at clinical medicine scheduled outpatient appointments in a university general hospital.
Factors associated with nonattendance at clinical medicine scheduled outpatient appointments in a university general hospital.
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DOI：
10.2147/ppa.s51841
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发表时间：
2013
影响因子：
2.2
通讯作者：
de Quiros FG
中科院分区：
文献类型：
--
作者：
Giunta D;Briatore A;Baum A;Luna D;Waisman G;de Quiros FG
初级保健门诊预约不出勤是世界范围内的一个主要卫生保健问题。我们的目的是估计寻求初级保健的门诊患者预约不出勤的普遍程度，确定相关因素并建立预测预约不出勤的模型。2010年1月至2011年7月期间，在布宜诺斯艾利斯意大利医院对有初级保健门诊预约的成年患者进行了一项队列研究。我们评估了这些患者的病史和特征，以及他们的就诊时间和出勤率。病人被分为两组：一组按时赴约，另一组没有。我们估计了比值比（OR）和相应的95%置信区间（95% CI），并利用与缺勤相关的因素和临床相关的因素，通过逻辑回归建立了缺勤的预测模型。采用赤池信息准则对不同模型进行比较。随机分配一代队列和验证队列。在纳入研究的113,716次预约中，25,687次错过（22.7%;95% CI: 22.34%-22.83%）。我们发现，缺勤与年龄（OR: 0.99; 95% CI: 0.99 - 0.99）、个人健康记录中的问题数量（OR: 0.98; 95% CI: 0.98 - 0.99）、请求与预约日期之间的时间（OR: 1; 95% CI: 1 - 1）、缺勤史（OR: 1.07; 95% CI: 1.07 - 1.07）、预约时间晚于下午4点（OR: 1.30; 95% CI: 1.24-1.35）和一周中的特定日子（OR: 1.00; 95% CI: 1.06-1.1）之间存在统计学上显著的关联。缺席的预测模型包括请求预约的患者、预约请求和实际预约日期的特征。在世代队列中，预测模型的受试者工作特征曲线下面积为0.892 （95% CI: 0.890-0.894）。与患者特征相关的证据，以及确定有较高不出勤可能性的预约，应该促进指导策略，以减少不出勤率，以及未来对这一主题的研究。使用预测模型可以进一步指导管理策略，以减少缺勤率。
Nonattendance at scheduled outpatient appointments for primary care is a major health care problem worldwide. Our aim was to estimate the prevalence of nonattendance at scheduled appointments for outpatients seeking primary care, to identify associated factors and build a model that predicts nonattendance at scheduled appointments. A cohort study of adult patients, who had a scheduled outpatient appointment for primary care, was conducted between January 2010 and July 2011, at the Italian Hospital of Buenos Aires. We evaluated the history and characteristics of these patients, and their scheduling and attendance at appointments. Patients were divided into two groups: those who attended their scheduled appointments, and those who did not. We estimated the odds ratios (OR) and corresponding 95% confidence intervals (95% CI), and generated a predictive model for nonattendance, with logistic regression, using factors associated with lack of attendance, and those considered clinically relevant. Alternative models were compared using Akaike’s Information Criterion. A generation cohort and a validation cohort were assigned randomly. Of 113,716 appointments included in the study, 25,687 were missed (22.7%; 95% CI: 22.34%–22.83%). We found a statistically significant association between nonattendance and age (OR: 0.99; 95% CI: 0.99–0.99), number of issues in the personal health record (OR: 0.98; 95% CI: 0.98–0.99), time between the request for and date of appointment (OR: 1; 95% CI: 1–1), history of nonattendance (OR: 1.07; 95% CI: 1.07–1.07), appointment scheduled later than 4 pm (OR: 1.30; 95% CI: 1.24–1.35), and specific days of the week (OR: 1.00; 95% CI: 1.06–1.1). The predictive model for nonattendance included characteristics of the patient requesting the appointment, the appointment request, and the actual appointment date. The area under the receiver operating characteristic curve of the predictive model in the generation cohort was 0.892 (95% CI: 0.890–0.894). Evidence related to patient characteristics, and the identification of appointments with a higher likelihood of nonattendance, should promote guided strategies to reduce the rate of nonattendance, as well as to future research on this topic. The use of predictive models could further guide management strategies to reduce the rate of nonattendance.