Cancer Care and Research Center, Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, WI, USA.
Information Technology and Digital Services Analytics, Gundersen Health System, Marshfield, WI, USA.
BMC Health Serv Res. 2023 Sep 14;23(1):989. doi: 10.1186/s12913-023-09969-5.
No-show appointments pose a significant challenge for healthcare providers, particularly in rural areas. In this study, we developed an evidence-based predictive model for patient no-shows at the Marshfield Clinic Health System (MCHS) rural provider network in Wisconsin, with the aim of improving overbooking approaches in outpatient settings and reducing the negative impact of no-shows in our underserved rural patient populations.
Retrospective data (2021) were obtained from the MCHS scheduling system, which included 1,260,083 total appointments from 263,464 patients, as well as their demographic, appointment, and insurance information. We used descriptive statistics to associate variables with show or no-show status, logistic regression, and random forests utilized, and eXtreme Gradient Boosting (XGBoost) was chosen to develop the final model, determine cut-offs, and evaluate performance. We also used the model to predict future no-shows for appointments from 2022 and onwards.
The no-show rate was 6.0% in both the train and test datasets. The train and test datasets both yielded 5.98. Appointments scheduled further in advance (> 60 days of lead time) had a higher (7.7%) no-show rate. Appointments for patients aged 21-30 had the highest no-show rate (11.8%), and those for patients over 60 years of age had the lowest (2.9%). The model predictions yielded an Area Under Curve (AUC) of 0.84 for the train set and 0.83 for the test set. With the cut-off set to 0.4, the sensitivity was 0.71 and the positive predictive value was 0.18. Model results were used to recommend 1 overbook for every 6 at-risk appointments per provider per day.
Our findings demonstrate the feasibility of developing a predictive model based on administrative data from a predominantly rural healthcare system. Our new model distinguished between show and no-show appointments with high performance, and 1 overbook was advised for every 6 at-risk appointments. This data-driven approach to mitigating the impact of no-shows increases treatment availability in rural areas by overbooking appointment slots on days with an elevated risk of no-shows.
失约预约对医疗服务提供者，特别是在农村地区构成了重大挑战。在这项研究中，我们针对威斯康星州 Marshfield 诊所医疗系统（MCHS）农村医疗服务提供者网络的患者失约情况，建立了一个基于证据的预测模型，旨在改进门诊环境下的超额预约方法，并减少我们服务不足的农村患者群体中失约的负面影响。
我们从 MCHS 预约系统中获取了回顾性数据（2021 年），其中包括 263464 名患者的 1260083 次总预约以及他们的人口统计学、预约和保险信息。我们使用描述性统计方法将变量与出现或失约状态相关联，使用逻辑回归和随机森林，最终选择极端梯度提升（XGBoost）来开发最终模型、确定截止值并评估性能。我们还使用该模型预测 2022 年及以后的未来失约预约。
在训练集和测试集中，失约率均为 6.0%。训练集和测试集的曲线下面积（AUC）均为 5.98。提前预约（>60 天的提前期）的预约失约率更高（7.7%）。21-30 岁患者的预约失约率最高（11.8%），60 岁以上患者的预约失约率最低（2.9%）。模型预测在训练集的 AUC 为 0.84，在测试集的 AUC 为 0.83。将截止值设置为 0.4 时，灵敏度为 0.71，阳性预测值为 0.18。模型结果用于建议每个提供者每天为每 6 个高危预约增加 1 次预约。
我们的研究结果表明，基于主要为农村医疗系统的行政数据开发预测模型是可行的。我们的新模型在高绩效方面区分了出现和失约预约，建议每 6 个高危预约增加 1 次预约。这种基于数据的方法可以减少失约的影响，通过在失约风险较高的日子超额预约预约时段，增加农村地区的治疗机会。
