Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

中文翻译：

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深度学习方法

在远程医疗服务中，保持医生与患者之间的高质量对话至关重要，在远程医疗服务中，有效而有效的通信对于促进患者健康至关重要。评估医疗对话的质量通常是基于人类的听觉-感知评估来进行的。通常，由于他们遵循系统的评估标准，因此需要经过培训的专家来执行此类任务。但是，每天咨询的迅速增加使评估过程效率低下且不切实际。本文使用基于深度学习的分类模型，研究了远程医疗服务中基于医患语音的对话质量评估过程的自动化。为此，数据包括从Altibbi获得的录音。Altibbi是一个数字医疗平台，可在中东和北非（MENA）提供远程医疗和远程医疗服务。目的是协助Altibbi的运营团队以自动化的方式评估所提供的咨询。所提出的模型是使用三组特征开发的：从信号级别，成绩单级别以及信号和成绩单级别中提取的功能。在信号级别，计算各种统计和频谱信息以表征语音记录的频谱包络。在笔录级别，使用预训练的嵌入模型来包含文本信息的语义和上下文特征。此外，还探索和分析了信号和转录水平的混合体。设计的分类模型依赖于深度神经网络和卷积神经网络的堆叠层。评估结果表明，与Altibbi运营团队采用的手动评估方法相比，该模型具有更高的精确度。