Developing a Digital Mental Health Platform for the Arab World: From Research to Action.

Individuals within the Arab world rarely access mental health services. One of the major reasons for this relates to the stigma associated with mental disorders. According to the World Health Organization (WHO), untreated and undiagnosed individuals living with moderate to severe mental health disorders are more likely to die 10-20 years earlier than the estimated life expectancy of the general population. Mental disorders also cause a large amount of costs to economies. Access to mental health services is out of reach for many individuals within in the Arab world due to insufficient planning, inadequate community resources, and military conflicts. Online mental health information and services are growing within the region; however, they are embedded and often sidelined within a wealth of other general health information. The purpose of this paper is to present the conceptual framework of the Mental Health Assistant (MeHA) digital platform being developed for the Arab world. The aim of this platform is to provide mental health information and educational resources through the use of a conversational agent, multi-media information, and to digitally connect patients with mental health service providers. The conceptual framework for the platform is based on mental health and information technology expert feedback, review of both academic and gray literature on mental health, and an examination of leading mental health digital platforms. As a result of this process, we developed a conceptual framework that will guide the development of the MeHA platform.

An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent.

Conversational agents are being used to help in the screening, assessment, diagnosis, and treatment of common mental health disorders. In this paper, we propose a bootstrapping approach for the development of a digital mental health conversational agent (i.e., chatbot). We start from a basic rule-based expert system and iteratively move towards a more sophisticated platform composed of specialized chatbots each aiming to assess and pre-diagnose a specific mental health disorder using machine learning and natural language processing techniques. During each iteration, user feedback from psychiatrists and patients are incorporated into the iterative design process. A survival of the fittest approach is also used to assess the continuation or removal of a specialized mental health chatbot in each generational design. We anticipate that our unique and novel approach can be used for the development of conversational mental health agents with the ultimate goal of designing a smart chatbot that delivers evidence-based care and contributes to scaling up services while decreasing the pressure on mental health care providers.