Ngantcha Patricia, Amith Muhammad, Tao Cui, Roberts Kirk
Texas Southern University, Houston, TX, USA.
School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Digit Hum Model Appl Health Saf Ergon Risk Manag (2021). 2021;12777:250-268. doi: 10.1007/978-3-030-77817-0_19.
Patient-provider communication plays a major role in healthcare with its main goal being to improve the patient's health and build a trustworthy relationship between the patient and the doctor. Provider's efficiency and effectiveness in communication can be improved through training in order to meet the essential elements of communication that are relevant during medical encounters. We surmised that speech-enabled conversational agents could be used as a training tool. In this study, we propose designing an ontology-based interaction model that can direct software agents to train dental and medical students. We transformed sample scenario scripts into a formalized ontology training model that links utterances of the user and the machine that expresses patient-provider communication. We created two instance-based models from the ontology to test the operational execution of the model using a prototype software engine. The assessment revealed that the dialogue engine was able to handle about 62% of the dialogue links. Future direction of this work will focus on further enhancing and capturing the features of patient-provider communication, and eventual deployment for pilot testing.
患者与医疗服务提供者之间的沟通在医疗保健中起着重要作用，其主要目标是改善患者的健康状况，并在患者与医生之间建立信任关系。通过培训可以提高医疗服务提供者沟通的效率和效果，以满足医疗互动中相关沟通的基本要素。我们推测，语音对话代理可以用作培训工具。在本研究中，我们提议设计一种基于本体的交互模型，该模型可以指导软件代理对牙科和医学专业学生进行培训。我们将示例场景脚本转换为一个形式化的本体训练模型，该模型将用户和机器的话语联系起来，这些话语表达了患者与医疗服务提供者之间的沟通。我们从本体中创建了两个基于实例的模型，以使用原型软件引擎测试该模型的操作执行情况。评估显示，对话引擎能够处理约62%的对话链接。这项工作未来的方向将集中在进一步增强和捕捉患者与医疗服务提供者沟通的特征，并最终进行试点测试部署。
