Case Studies

Use cases for mental health task sharing programs

This chapter explores how AI could enhance certain task-sharing programs by identifying opportunities to apply these tools across the program and by sharing real-world examples of emerging use cases.

• Section 1 outlines the implementation cycle for scaling a previously tested mental health task-sharing program and highlights where AI could unlock opportunities and strengthen task sharing models. 
• Section 2 discusses real-world examples that illustrate how different organizations are piloting or integrating AI into their workflows.

While this chapter was created with evidence-based task-sharing programs in mind, the insights and examples throughout could also apply to the broader spectrum of mental health skill-building programs (such as programs that train volunteers or peer support specialists and programs that upskill mental health professionals).

We also acknowledge that there are a variety of interpretations and uses of task-sharing models and that not all settings will reflect the approaches included in this chapter. Common definitions for stages in implementation cycles are suggested to provide visibility on the kinds of activities that are often included in each specific stage. Real-world examples are included in Section 2, though we recognize these do not reflect the realities of all countries or contexts, where task-sharing approaches may differ and where cultural relevance may limit the use of current AI tools. Organizations can explore minimum viable product versions that are adapted to available technology resources and data availability.

Applying a task-sharing program to a mental health context requires a multiphase approach—the implementation cycle. This approach allows programs to be as effective as possible in the context they are being used and allows them to be scaled into more and bigger contexts. As explained in chapter 2, task sharing programs can be delivered through different types of nonspecialists in a range of settings, each of which will have their own unique approaches and challenges.

The implementation cycle follows a generic approach and may include elements that are not applicable to all contexts. The visual of the example implementation cycle shared below follows a gradual progression and includes six phases which could be used by non-task sharing programs: program development, trainee selection, training, assignment, intervention, and completion. Each phase is critical to maintain quality, ensure provider competency, and optimizing client outcomes. At the end of the completion phase, implementation requires ongoing support and supervision, where AI might also play a role.

For the purposes of this field guide, a phase is a major stage in the program’s implementation cycle (program adaptation or trainee selection, for example) that groups together activities with a common objective. Phases organize the implementation process into manageable segments, providing a clear road map from start to finish. Within each phase, steps are specific tasks that contribute to achieving the phase's objective (for example, in phase one, program adaptation, a step would be to conduct a situation analysis for a new context). These steps provide operational guidance, ensuring systematic execution of each phase.

Instructions for interacting with the diagram

1. Select your role: Click on your user type (task-sharing program staff, supervisor, care provider, or client) to highlight the steps most relevant to your experience in a task-sharing program
2. Explore the implementation cycle: Hover over each phase to see a brief description of each stage.
3. View challenges and AI opportunities: Click on a step to reveal the main challenges and the ways that AI can help improve the step, if applicable.
4. Access detailed AI use cases: Within relevant steps, click on a specific AI use case to explore detailed insights, including users, applications, and design considerations.

This section explores potential AI use cases that could help address challenges present throughout the implementation cycle and expand the reach of mental health task sharing programs. Each use case includes detailed scenarios that show how AI can be applied in practice, identifies the opportunities unlocked with these tools, describes direct end users, and provides real-world examples of AI in mental health task-sharing in certain contexts.

Through practical examples and real-world applications, this section demonstrates how AI has the potential to enhance the capacity and impact of mental health task sharing programs, paving the way for scalable and sustainable mental health services. Throughout, though not explicitly listed, clients are always among the end users benefiting from each of these solutions, given that the overall goal of improving efficiency, training, and other aspects is to have these lead to better client care as a result.

We acknowledge that some of the use cases and examples presented may require additional consideration when it comes to how they compare to the specifics of other countries or contexts.