Innovating Conversational Agents for Healthcare

At Sensely, we enhance healthcare through extensive research, expert collaboration, and innovative design principles for conversational agents, ensuring effective user experiences and improved well-being.

A group of people engaging in a cheerful conversation indoors. The focus is on a man wearing glasses, a black jacket, and a lanyard, who is smiling broadly. The setting appears casual and light-hearted, with a soft, defocused background.

Patients and welfare service recipients have significant differences in physical, psychological and social backgrounds, and dialogue agents need to achieve highly personalized interactions. When facing anxious patients, agents must not only provide professional medical advice, but also use affective computing technology to identify their emotional state and provide comfort and encouragement.

Conversational Agents

Researching design principles for healthcare conversational agents and evaluation.

Expert Interviews

Conducting interviews with medical and AI experts for insights.

An elderly man in a suit, seated and smiling, is engaged in conversation in a cozy room filled with books and toys. A woman with blonde hair is sitting beside him, partially visible from behind. The setting appears informal and warm with shelves containing colorful toys and books in the background.

Prototype Development

Designing a conversational agent prototype for user testing.

User Trials

Recruiting users for small-scale trials in healthcare settings.

Data Collection

Utilizing quantitative methods to gather interaction data effectively.

Predict the future

The application effects of conversational agents in the fields of healthcare and welfare require long-term observation and evaluation, such as the long-term impact on the health management of chronic disease patients and the continuous support effect on the psychological health of users. However, most current evaluations only focus on short-term performance indicators and lack follow-up research on long-term effects. Long-term effect evaluation requires a lot of time. For example, to evaluate speech agents used for diabetes health management, it is necessary to monitor the patient's blood pressure control, lifestyle, etc. for a long time. These data are affected by various environmental factors such as the patient's own dependence and life changes, which increases the evaluation weight.