# Uncertainty Detection: A Multi-View Decision Boundary Approach Against Healthcare Unknown Intents
## 2 Literature Review and Design Theories  
### Challenges in Healthcare E-Services  
The digital transformation of healthcare systems has become a significant trend among healthcare organizations, hospitals, and facilities worldwide [17]. The global objective is to provide 24/7 remote access to healthcare services, enhance doctor-patient interactions, improve health condition tracking, optimize repetitive processes, reduce healthcare costs, and make patient care delivery accessible to everyone. Traditionally, healthcare information was disseminated in a one-way direction; however, the advent of digital platforms has enabled both one-to-many and interactive one-to-one communication on a large scale. Chatbots play a key role in automating these dialogues to meet the growing demand for continuous service. Driven by the need for scalable and accessible care, particularly in areas like mental health support and chronic disease management [18; 19], they are increasingly integrated into digital mental health interventions to support healthcare e-services such as diagnostics and screening, symptom management, behavior change, and content delivery [20]. However, the successful implementation of such tools in sensitive healthcare contexts requires careful consideration of clinical workflows, patient safety, and ethical guidelines, aspects extensively studied in medical informatics [21; 22; 23]. A chatbot's ability to comprehend user queries largely depends on the conversation corpus provided by dialogue system designers. Since people's query needs are non-enumerable and change over time, new queries appear regularly in real-world scenarios [11]. Some intents and their corresponding utterance samples are unavoidably neglected to consider. In addition, a healthcare chatbot is usually limited to serving a specific range of queries [10] as an accident-proof measure, reflecting crucial patient safety and risk mitigation principles in healthcare technology design [23; 24]. Certain types of user intents are deliberately excluded from the scope of healthcare chatbots. These potentially harmful dialogues are omitted from the training data because designers do not intend for chatbots to respond to uncertain or high-risk medical queries without professional oversight. In developing healthcare e-services, chatbot designers carefully curate the training samples, which inherently limits the chatbot's ability to fully understand or respond to human utterances. On the other hand, a core challenge highlighted in user-centered design studies for health technologies is the mismatch between system design and real-world user needs and capabilities [25; 26; 27]. Users are usually unaware of the intent design framework established by dialogue system developers and often lack medical expertise. Most are unaware of what information needs to be mentioned or emphasized in healthcare e-services to effectively convey their query intent and receive an appropriate response. This lack of health literacy and difficulty in articulating medical concerns is a well-documented barrier in health informatics [28; 29]. Their languages contain slang, dialects, emotional expressions, abbreviations, or informal expressions, all of which can obscure the intended meaning and make it more challenging for the chatbot to accurately interpret the user's input. Also, people's expertise reserves vary a lot. Users from different age groups, cultural contexts, and educational backgrounds express their health concerns in vastly different ways. Considering most individuals are unfamiliar with the specialized \"healthcare vocabulary\" required to convey information accurately [30], chatbots find it increasingly difficult to learn real-world language usage rules and accurately infer people's true query intents. In this context, the complexity of healthcare communication combined with the limitations of chatbot language comprehension can result in a crisis of unqualified or inappropriate responses in automated dialogue services [31].