Use case 1: Applicant screening tool

Description:

Applicants, who applied to become a care provider, undergo an AI-enabled screening process to determine their willingness to participate in the task-sharing program and evaluate if they have the skills needed to be a mental health task-sharing provider.

Example Scenario:

An applicant interested in becoming a care provider completes an online prescreening assessment through the applicant screening tool. The AI tool asks a series of adaptive questions to evaluate the applicant’s alignment with the program’s selection criteria (such as relevant work experience or physical proximity to the target population). The applicant screening tool assesses the applicant’s responses for critical thinking, communication skills, and motivation. Once the applicant finishes the assessment, the AI tool generates a preliminary suitability score and identifies areas for follow-up during the interview process. The application automatically notifies the program coordinator about qualified candidates, streamlining the selection process.

Opportunities unlocked:

The applicant screening tool could address several challenges in mental health task-sharing programs, including:

• Time-intensive, variable screening process. The AI tool could automate initial assessments using a set of criteria and shortlist qualified candidates, which could help standardize the care provider evaluations and substantially reduce the workload on program coordinators and trainers who would otherwise need to conduct time-consuming interviews, role-plays, or background checks.
• High dropout rates. The AI tool could evaluate applicants’ skills and engagement levels, identifying potential risks for dropout early in the process. This feature could help minimize mid-training attrition and ensure consistent staffing levels.

How could the end user(s) benefit from this solution?

The primary end users who could directly benefit from this solution is:

• Task sharing organization. Responsible for managing the recruitment and selection process, the task sharing organization could benefit from automated shortlisting and suitability scoring. They could also benefit from a generated provider competence matrix that can help adapt the training to better suit the specific cohort of trainees, thereby reducing their administrative burden and allowing them to focus on final interviews and onboarding. Additionally, reducing dropout rates could save an organization time and resources, and standardizing processes helps to ensure that all care providers have a similar quality.
• Client. Reducing dropout risks and ensuring that selected volunteers are thoroughly screened, high-quality candidates would help provide clients with exceptional and consistent care.
• Care provider in training. Providing a clear description of the job experience and requirements helps applicants decide if they can and want to commit to the program, saving time for those who aren’t interested in going through the screening and training process.