L2M – Market Validation and Human Factors Strategy for an Edge AI-Based Care Platform

This project focuses on validating the real-world use and integration of an advanced, AI-enabled platform designed to improve prenatal care through early detection of conditions such as gestational diabetes and preeclampsia. Rather than simply testing technical performance, the project places strong emphasis on human factors engineering and understanding how healthcare professionals interact with new technology in busy clinical settings. By working directly with obstetricians, nurses, and clinic staff, the project will assess usability, workflow compatibility, and overall clinician experience. This user-centered approach will identify potential barriers to adoption, streamline training and implementation strategies, and ensure that the platform supports rather than disrupts existing care routines. Insights gained will help create evidence-based recommendations for effective integration into prenatal care and potentially other healthcare areas in the future. Ultimately, this work aims to empower clinicians with tools that are intuitive, reliable, and seamlessly fit into their daily practices, supporting better patient outcomes and more efficient care delivery.

Faculty Supervisor:

Catherine Burns

Student:

Partner:

DMZ Ventures Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program: