The focus of the research is on the challenges associated with achieving explainability in analytics and AI powered approaches applied in risk predictions in maternal health. As part of this research, we aim to conduct a usability study of an AI-driven postpartum depression risk assessment tool engaging healthcare providers - including doctors, nurses, and midwives - as participants. The usability study will assess healthcare provider's interactions and engagement with the web-based tool, measured by the System Explainability Scale (SES) Score, which quantifies its overarching Explainability, considering the dimensions of understandability, trust, and usability.
We will employ empirical investigation to assess the user experience of the developed tool through a pre-study questionnaire that relates to participants' information, experiences, and expectation from such tools, task-driven questionnaire while using the tool and observations, post-study feedback (System Explainability Scale score), and discussions. The usability testing step-up can be either one-on-one (in-person studies) or virtually via Zoom sessions depending on the participants' discretion.
Requirements for healthy volunteers are different than for those with a specific condition. If you are interested in becoming a healthy volunteer for this study, use the below categories to determine if you are able to participate.
100% Remote (online, phone, text)
Mohammad Kibria
School of Information and Library Science
Behavioral or Social
Observational
Mental and Emotional Health
Opinions and Perceptions
Pregnancy
Sexual and/or Reproductive Health
Women's Health
UNC or UNC Health employees
UNC Students (undergrad, grad, professional)
24-1060
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