The first step when developing AI is to understand the user need; but just as critical, is knowing the context in which the data is being collected.
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Hailey Fehrenbach
Senior UX Researcher
Hailey is a classically trained human factors engineer with over 5 years of experience in medical device and healthcare analytics product development. With a background in design, engineering, and research, she delivers useful, usable, and engaging experiences for all customer touch points across medical device and healthcare industries. Her experience spans research on robotic surgical equipment and combination devices for at-home use, to the design and development of neurotech and cardiac diagnostic platforms. Hailey has a BS in Design from the University of Cincinnati and a MS in Industrial and Human Factors Engineering from Wright State University.



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Read our team’s latest bold insights
Three things to improve acceptance of AI
To truly deliver on the promise of AI, developers need to keep the end users in mind. By integrating three components of context, interaction, and trust, AI can be the runaway success that futurists predict it will be.
Recruiting methods and study logistics for human factors and user research
A stronger recruiting strategy that includes relationships with patient support groups and clinical treatment centers can provide better access to difficult-to-reach patient populations. Being intentional about how you plan the logistics of your human factors and user research can mitigate risks to validity introduced by biases.
Singularity and the potential impact on UX design principles
If we are approaching a rapid technology shift as some experts predict, core UX design principles will have to be redefined to adapt to radically different interaction models.