Bar-Ilan University, Ramat-Gan, Israel.
Isr J Health Policy Res. 2022 Jun 3;11(1):25. doi: 10.1186/s13584-022-00534-9.
Mental health contact centers (also known as Hotlines) offer crisis intervention and counselling by phone calls and online chats. These mental health helplines have shown great success in improving the mental state of the callers, and are increasingly becoming popular in Israel and worldwide. Unfortunately, our knowledge about how to conduct successful routing of callers to counselling agents has been limited due to lack of large-scale data with labeled outcomes of the interactions. To date, many of these contact centers are overwhelmed by chat requests and operate in a simple first-come-first-serve (FCFS) scheduling policy which, combined, may lead to many callers receiving suboptimal counselling or abandoning the service before being treated. In this work our goal is to improve the efficiency of mental health contact centers by using a novel machine-learning based routing policy.
We present a large-scale machine learning-based analysis of real-world data from the online contact center of ERAN, the Israeli Association for Emotional First Aid. The data includes over 35,000 conversations over a 2-years period. Based on this analysis, we present a novel call routing method, that integrates advanced AI-techniques including the Monte Carlo tree search algorithm. We conducted an experiment that included various realistic simulations of incoming calls to contact centers, based on data from ERAN. We divided the simulations into two common settings: standard call flow and heavy call flow. In order to establish a baseline, we compared our proposed solution to two baseline methods: (1) The FCFS method; and (2) a greedy solution based on machine learning predictions. Our comparison focuses on two metrics - the number of calls served and the average feedback of the callers (i.e., quality of the chats).
In the preliminary analysis, we identify indicative features that significantly contribute to the effectiveness of a conversation and demonstrate high accuracy in predicting the expected duration and the callers' feedback. In the routing methods evaluation, we find that in heavy call flow settings, our proposed method significantly outperforms the other methods in both the quantity of served calls and average feedback. Most notably, we find that in the heavy call flow settings, our method improves the average feedback by 24% compared to FCFS and by 4% compared to the greedy solution. Regarding the standard-flow setting, we find that our proposed method significantly outperforms the FCFS method in the callers' average feedback with a 12% improvement. However, in this setting, we did not find a significant difference between all methods in the quantity of served-calls and no significant difference was found between our proposed method and the greedy solution.
The proposed routing policy has the potential to significantly improve the performance of mental health contact centers, especially in peak hours. Leveraging artificial intelligence techniques, such as machine learning algorithms, combined with real-world data can bring about a significant and necessary leap forward in the way mental health hotlines operate and consequently reduce the burden of mental illnesses on health systems. However, implementation and evaluation in an operational contact center is necessary in order to verify that the results replicate in practice.
心理健康联络中心（也称为热线）通过电话和在线聊天提供危机干预和咨询。这些心理健康求助热线在改善来电者的精神状态方面取得了巨大成功，在以色列和全球范围内越来越受欢迎。不幸的是，由于缺乏带有交互结果标记的大规模数据，我们对如何成功将来电者转接到咨询代理的了解受到限制。迄今为止，许多这些联络中心因聊天请求而不堪重负，并采用简单的先来先服务（FCFS）调度策略进行操作，这可能导致许多来电者接受的咨询效果不佳或在接受治疗之前放弃服务。在这项工作中，我们的目标是通过使用基于新型机器学习的路由策略来提高心理健康联络中心的效率。
我们对 ERAN（以色列情感急救协会）在线联络中心的真实世界数据进行了大规模基于机器学习的分析。该数据包括 2 年期间超过 35000 次对话。基于此分析，我们提出了一种新的呼叫路由方法，该方法集成了包括蒙特卡罗树搜索算法在内的先进 AI 技术。我们进行了一项实验，该实验根据 ERAN 的数据模拟了各种传入呼叫到联络中心的情况。我们将模拟分为两种常见设置：标准呼叫流程和繁忙呼叫流程。为了建立基线，我们将我们提出的解决方案与两种基线方法进行了比较：（1）FCFS 方法；（2）基于机器学习预测的贪婪解决方案。我们的比较侧重于两个指标-服务的呼叫数量和来电者的平均反馈（即聊天的质量）。
在初步分析中，我们确定了对对话效果有显著贡献的指示性特征，并证明了在预测预期持续时间和来电者反馈方面具有很高的准确性。在路由方法评估中，我们发现，在繁忙的呼叫流程设置中，与其他方法相比，我们提出的方法在服务的呼叫数量和平均反馈方面都有显著的提高。值得注意的是，我们发现，在繁忙的呼叫流程设置中，与 FCFS 相比，我们的方法将平均反馈提高了 24%，与贪婪解决方案相比，提高了 4%。关于标准流量设置，我们发现与 FCFS 相比，我们提出的方法在来电者的平均反馈方面有 12%的显著提高。然而，在这种设置下，我们没有发现所有方法在服务的呼叫数量方面有显著差异，也没有发现我们提出的方法与贪婪解决方案之间有显著差异。
所提出的路由策略有可能显著提高心理健康联络中心的性能，尤其是在高峰时段。利用人工智能技术，例如机器学习算法，并结合实际数据，可以在心理健康热线的运作方式上带来重大而必要的飞跃，从而减轻卫生系统的精神疾病负担。然而，在实际运营的联络中心中进行实施和评估是必要的，以验证结果在实践中是否具有复制性。
