Role

Lead UX Designer / Product Manager

The Design Process

Problem Statement: Empower non-technical healthcare users with AI/ML tools to predict and prevent adverse events, improving patient safety and quality of care.

Research: As the research team lead, I delved into AI/ML technologies and their healthcare applications, conducting UX research to understand the needs of non-technical end users, such as risk and feedback managers.

Analysis: I identified the main challenge: designing an intuitive system that enables non-technical users to harness AI/ML predictions and take actionable steps to prevent adverse events.

Ideation: I collaborated with the team to brainstorm interfaces that simplify complex AI/ML concepts, making predictions accessible and actionable for non-technical end users.

Prototyping and Testing: I created prototypes of the interfaces and conducted user testing with non-technical users. Through testing, we discovered that users found algorithm names and raw data intimidating. In response, we iterated on designs and introduced simple charts to present AI/ML model performance.

Implementation: I worked closely with the development team to build the user-friendly interfaces featuring charts that enabled non-technical users to easily compare model effectiveness without engaging with raw data. I also provided tailored training and support to help users understand and interpret the visualizations.

Results: The IRAP Project demonstrated our ability to put advanced AI/ML technologies into the hands of non-technical users, empowering them to enhance patient safety through proactive adverse event prevention.

Reflection: This project showcased my UX research expertise, adaptability, and commitment to designing intuitive interfaces that bridge the gap between complex technologies and non-technical end users. By simplifying AI/ML concepts with charts, we enabled users to confidently leverage advanced tools for proactive adverse event prevention in healthcare.