Clinical Digital Solutions, Northwell Health, New Hyde Park, NY, USA.
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
J Gen Intern Med. 2023 Aug;38(10):2298-2307. doi: 10.1007/s11606-023-08065-y. Epub 2023 Feb 9.
Non-arrivals to scheduled ambulatory visits are common and lead to a discontinuity of care, poor health outcomes, and increased subsequent healthcare utilization. Reducing non-arrivals is important given their association with poorer health outcomes and cost to health systems.
To develop and validate a prediction model for ambulatory non-arrivals.
Retrospective cohort study.
Patients at an integrated health system who had an outpatient visit scheduled from January 1, 2020, to February 28, 2022.
Non-arrivals to scheduled appointments.
There were over 4.3 million ambulatory appointments from 1.2 million adult patients. Patients with appointment non-arrivals were more likely to be single, racial/ethnic minorities, and not having an established primary care provider compared to those who arrived at their appointments. A prediction model using the XGBoost machine learning algorithm had the highest AUC value (0.768 [0.767-0.770]). Using SHAP values, the most impactful features in the model include rescheduled appointments, lead time (number of days from scheduled to appointment date), appointment provider, number of days since last appointment with the same department, and a patient's prior appointment status within the same department. Scheduling visits close to an appointment date is predicted to be less likely to result in a non-arrival. Overall, the prediction model calibrated well for each department, especially over the operationally relevant probability range of 0 to 40%. Departments with fewer observations and lower non-arrival rates generally had a worse calibration.
Using a machine learning algorithm, we developed a prediction model for non-arrivals to scheduled ambulatory appointments usable for all medical specialties. The proposed prediction model can be deployed within an electronic health system or integrated into other dashboards to reduce non-arrivals. Future work will focus on the implementation and application of the model to reduce non-arrivals.
门诊预约失约很常见，会导致医疗服务连续性中断、健康结果变差，并增加后续医疗保健的利用。鉴于预约失约与较差的健康结果和医疗系统成本相关，减少预约失约很重要。
开发和验证用于门诊失约预测的模型。
回顾性队列研究。
于 2020 年 1 月 1 日至 2022 年 2 月 28 日期间在综合医疗系统中有门诊预约的患者。
预约失约情况。
在 120 万成年患者中，有超过 430 万次门诊预约。与按时就诊的患者相比，预约失约的患者更有可能是单身、少数族裔，并且没有固定的初级保健提供者。使用 XGBoost 机器学习算法的预测模型具有最高的 AUC 值（0.768[0.767-0.770]）。使用 SHAP 值，模型中最具影响力的特征包括重新安排的预约、提前期（从预约日期到预约日期的天数）、预约提供者、与同一科室上次预约后的天数以及患者在同一科室的既往预约状态。预测预约日期接近预约日期的情况下，失约的可能性较小。总体而言，该预测模型在每个科室的校准效果都很好，特别是在操作相关的概率范围 0 到 40%内。观察次数较少且失约率较低的科室通常校准效果较差。
我们使用机器学习算法开发了一个用于预测门诊预约失约的模型，适用于所有医学专业。所提出的预测模型可在电子健康系统中部署或集成到其他仪表板中以减少失约。未来的工作将侧重于模型的实施和应用，以减少失约。
