Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Rio de Janeiro, RJ, 22451-900, Brazil.
Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rua Marquês de São Vicente, 225, Rio de Janeiro, RJ, 22451-900, Brazil.
Obes Surg. 2019 Jan;29(1):40-47. doi: 10.1007/s11695-018-3480-9.
No-shows of patients to their scheduled appointments have a significant impact on healthcare systems, including lower clinical efficiency and higher costs. The purpose of this study was to investigate the factors associated with patient no-shows in a bariatric surgery clinic.
We performed a retrospective study of 13,230 records for 2660 patients in a clinic located in Rio de Janeiro, Brazil, over a 17-month period (January 2015-May 2016). Logistic regression analyses were conducted to explore and model the influence of certain variables on no-show rates. This work also developed a predictive model stratified for each medical specialty.
The overall proportion of no-shows was 21.9%. According to multiple logistic regression, there is a significant association between the patient no-shows and eight variables examined. This association revealed a pattern in the increase of patient no-shows: appointment in the later hours of the day, appointments not in the summer months, post-surgery appointment, high lead time, higher no-show history, fewer numbers of previous appointments, home address 20 to 50 km away from the clinic, or scheduled for another specialty other than a bariatric surgeon. Age group, forms of payment, gender, and weekday were not significant predictors. Predictive models were developed with an accuracy of 71%.
Understanding the characteristics of patient no-shows allows making improvements in management practice, and the predictive models can be incorporated into the clinic dynamic scheduling system, allowing the use of a new appointment policy that takes into account each patient's no-show probability.
患者未能按预约就诊对医疗系统有重大影响，包括降低临床效率和增加成本。本研究的目的是调查巴西里约热内卢一家减肥手术诊所中与患者失约相关的因素。
我们对 2660 名患者的 13230 份记录进行了回顾性研究，该记录来自巴西里约热内卢的一家诊所，时间跨度为 17 个月（2015 年 1 月至 2016 年 5 月）。我们进行了逻辑回归分析，以探讨和建模某些变量对失约率的影响。这项工作还为每个医学专业制定了一个预测模型。
总体失约率为 21.9%。根据多变量逻辑回归，患者失约与我们检查的八个变量之间存在显著关联。这种关联揭示了患者失约率增加的模式：预约时间在当天较晚的时间，预约不在夏季，手术后预约，较长的预约提前期，较高的失约史，预约前的就诊次数较少，家庭住址距离诊所 20 至 50 公里，或预约的科室不是减肥外科医生。年龄组、支付方式、性别和星期几不是显著的预测因素。预测模型的准确性为 71%。
了解患者失约的特点可以改进管理实践，预测模型可以纳入诊所动态调度系统，允许采用新的预约政策，考虑每个患者的失约概率。
