Background: Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers. Objective: The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers). Methods: We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments. Results: The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system’s knowledge increased the utility of our highly deidentified data and improved the predictive power of the models. Conclusions: The random forest method, enhanced with the system’s knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.

中文翻译：

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通过在心理健康护理中使用高度识别的数据来预测患者等待时间：增强的机器学习方法

背景：等待时间会影响患者满意度、治疗效果和患者接受的护理效率。心理健康中的等待时间预测是一项复杂的任务，并且受到难以预测门诊患者所需治疗次数、高未出现率以及使用团体治疗的可能性的影响。如果输入数据的效用较低，则等待时间分析的任务变得更加具有挑战性，当通过删除直接和准标识符对数据进行高度去识别时，就会发生这种情况。客观的：本研究的第一个目的是开发机器学习模型，通过使用实时数据预测精神科门诊患者从转诊到首次预约的等待时间。第二个目标是通过利用系统的知识来提高这些预测模型的性能，同时对输入数据进行高度去识别。第三个目标是确定导致等待时间长的因素，第四个目标是构建这些模型，使其实用且易于实施（因此对护理提供者有吸引力）。方法：我们通过使用 6 种机器学习方法来预测新门诊患者的首次预约等待时间，分析了来自加拿大安大略海岸心理健康科学中心 8 家门诊的回顾性高度去识别的行政数据。我们使用系统的知识来减轻我们数据的低效用。数据包括通过 30,342 次预约接受护理的 4187 名患者。结果：不同类型的心理健康诊所的平均等待时间差异很大。超过半数诊所的平均轮候时间超过3个月。各诊所的预约次数和未出现率差异很大。尽管存在这些差异，但随机森林方法为 8 家诊所中的 4 家提供了最小均方根误差值，并为其他 4 家诊所提供了第二个最小均方根误差值。利用系统的知识增加了我们高度去识别的数据的效用，并提高了模型的预测能力。结论：使用系统知识增强的随机森林方法为新门诊患者提供可靠的等待时间预测，而不管高度去识别的输入数据的效用低以及不同诊所和患者类型的等待时间差异很大。优先系统被确定为导致等待时间长的一个因素，并建议使用快速通道系统作为潜在的解决方案。