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Developing a Mental Health Virtual Assistance (Chatbot) for Healthcare Workers and their Families
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- Author / Creator
- Zamani, Ali
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Approximately 1 in 3 Canadians experiences addiction or mental health challenges at some point in their lifetime. Unfortunately, there are multiple barriers to accessing mental healthcare, including system fragmentation, episodic care, long wait times, and insufficient support for health system navigation. In addition, stigma may further reduce an individual’s likelihood of seeking support. Digital technologies present new and exciting opportunities to bridge significant gaps in mental healthcare service provision, reduce barriers pertaining to stigma, and improve health outcomes for patients and mental health system integration and efficiency. Chatbots (ie, computer programs designed to simulate conversation with human users, especially over the Internet) may be explored to support those in need of information or access to services and present the opportunity to address gaps in traditional, fragmented, or episodic mental health system structures on demand with personalized attention. The recent COVID-19 pandemic has exacerbated even further the need for mental health support among Canadians and called attention to the inefficiencies of the system. As healthcare workers and their families are at an even greater risk of mental illness and psychological distress during the COVID-19 pandemic, this technology is first piloted to support this vulnerable group.
In this project, we developed mental health software Mira Chatbot to support healthcare workers and their families in the Canadian provinces of Alberta and Nova Scotia. The software features four major elements: the Mira Chatbot, Mira Resource Portal, Mira Dataset, and Mira Interface. Mira Chatbot’s primary purpose is to provide strategic resources for users responding to the custom needs that they share. Users provide their unique information through two main tasks defined within the Mira Chatbot: intent detection and entity extraction. Intent detection is a process where Mira Chatbot identifies the needs of the user based on a chat experience with an accuracy rate of 99.1%. Through the task of entity extraction, Mira Chatbot recognizes important keywords from a sentence with an accuracy rate of 95.4%.
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- Subjects / Keywords
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- Graduation date
- Fall 2022
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.