Centrum Wiskunde & Informatica, Amsterdam, the Netherlands.
113 Suicide Prevention, Amsterdam, the Netherlands.
BMC Public Health. 2023 May 27;23(1):984. doi: 10.1186/s12889-023-15887-2.
Each year, many help seekers in need contact health helplines for mental support. It is crucial that they receive support immediately, and that waiting times are minimal. In order to minimize delay, helplines must have adequate staffing levels, especially during peak hours. This has raised the need for means to predict the call and chat volumes ahead of time accurately. Motivated by this, in this paper, we analyze real-life data to develop models for accurately forecasting call volumes, for both phone and chat conversations for online mental health support.
This research was conducted on real call and chat data (adequately anonymized) provided by 113 Suicide Prevention (Over ons | 113 Zelfmoordpreventie) (throughout referred to as '113'), the online helpline for suicide prevention in the Netherlands. Chat and phone call data were analyzed to better understand the important factors that influence the call arrival process. These factors were then used as input to several Machine Learning (ML) models to forecast the number of call and chat arrivals. Next to that, senior counselors of the helpline completed a web-based questionnaire after each shift to assess their perception of the workload.
This study has led to several remarkable and key insights. First, the most important factors that determine the call volumes for the helpline are the trend, and weekly and daily cyclic patterns (cycles), while monthly and yearly cycles were found to be non-significant predictors for the number of phone and chat conversations. Second, media events that were included in this study only have limited-and only short-term-impact on the call volumes. Third, so-called (S)ARIMA models are shown to lead to the most accurate prediction in the case of short-term forecasting, while simple linear models work best for long-term forecasting. Fourth, questionnaires filled in by senior counselors show that the experienced workload is mainly correlated to the number of chat conversations compared to phone calls.
(S)ARIMA models can best be used to forecast the number of daily chats and phone calls with a MAPE of less than 10 in short-term forecasting. These models perform better than other models showing that the number of arrivals depends on historical data. These forecasts can be used as support for planning the number of counselors needed. Furthermore, the questionnaire data show that the workload experienced by senior counselors is more dependent on the number of chat arrivals and less on the number of available agents, showing the value of insight into the arrival process of conversations.
每年都有许多有需要的求助者通过健康热线寻求心理支持。重要的是，他们能够立即获得支持，并且等待时间最短。为了最大限度地减少延迟，热线必须有足够的人员配备水平，尤其是在高峰时段。这就需要有一种方法来提前准确地预测电话和聊天量。受此启发，本文通过分析实际生活数据，为在线心理健康支持的电话和聊天量建立了准确的预测模型。
本研究使用了由荷兰自杀预防在线热线 113（通过全称为“113”）提供的真实电话和聊天数据（充分匿名）进行。分析了聊天和电话数据，以更好地了解影响来电过程的重要因素。然后，这些因素被用作输入，通过几个机器学习（ML）模型来预测来电和聊天的数量。此外，热线的高级顾问在每次轮班后都会完成一个基于网络的问卷调查，以评估他们对工作量的感知。
这项研究产生了一些显著和关键的见解。首先，决定热线电话量的最重要因素是趋势以及每周和每日的周期性模式（周期），而月度和年度周期被发现对电话和聊天数量没有显著的预测作用。其次，本研究中包含的媒体事件仅对电话量有有限的、短期的影响。第三，（S）ARIMA 模型被证明在短期预测中可以得出最准确的预测，而简单的线性模型则最适合长期预测。第四，高级顾问填写的问卷显示，与电话相比，经验丰富的工作量主要与聊天数量相关。
（S）ARIMA 模型可用于预测每日聊天和电话数量，短期预测的 MAPE 低于 10。这些模型比其他模型表现更好，表明来电数量取决于历史数据。这些预测可用于支持规划所需的顾问人数。此外，问卷数据显示，高级顾问所经历的工作量更多地取决于聊天的到达量，而不是可用代理的数量，这表明了解对话到达过程具有价值。
