In healthcare, the ability to care for oneself is reflected in the "Activities of Daily Living (ADL)," which serve as a measure of functional ability (functioning). A lack of functioning may lead to poor living conditions requiring personal care and assistance. To accurately identify those in need of support, assistance programs continuously evaluate participants' functioning across various domains. However, the assessment process may encounter consistency issues when multiple assessors with varying levels of expertise are involved. Novice assessors, in particular, may lack the necessary preparation for real-world interactions with participants. To address this issue, we developed a dialogue system that simulates interactions between assessors and individuals of varying functioning in a natural and reproducible way. The dialogue system consists of two major modules, one for natural language understanding (NLU) and one for natural language generation (NLG), respectively. In order to generate responses consistent with the underlying knowledge base, the dialogue system requires both an understanding of the user's query and of biographical details of an individual being simulated. To fulfill this requirement, we experimented with query classification and generated responses based on those biographical details using some recently released InstructGPT-like models.

翻译：在医疗保健领域，自理能力通过"日常生活活动"体现，该指标用于衡量功能性能力。功能性不足可能导致需要个人护理和援助的生活困境。为准确识别需要支持的对象，援助项目会持续评估参与者在不同维度的功能表现。然而，当涉及专业知识水平不同的评估者时，评估过程可能出现一致性问题。特别是新手评估者，可能缺乏与参与者进行真实互动的必要准备。针对该问题，我们开发了一个对话系统，能以自然且可复现的方式模拟评估者与不同功能水平个体间的交互。该对话系统包含两大核心模块：自然语言理解模块和自然语言生成模块。为确保生成的回答与底层知识库保持一致，对话系统需要同时理解用户查询内容及被模拟个体的背景信息。为满足这一要求，我们尝试了查询分类方法，并基于这些背景信息，采用近期发布的类InstructGPT模型生成相关回答。