A quick follow-up to our blog posts about AI… The name of the game is no longer Moore's Law where we see processors getting exponentially faster. AI technology is driven not by computing processes of the past, but from an evolution beyond central processing unit (CPU)...
Back to team page
Patrick McCormack
Director
Patrick has 10 years of experience applying HFE/UE to the development of medical devices with an emphasis on ensuring safe and effective use as part of the FDA approval process. He possesses expertise in the interpretation and implementation of various design standards, including IEC 62366, AAMI HE75, BS EN ISO 14971, and others. Patrick has a BS in Human Factors Psychology from Embry-Riddle Aeronautical University.



Bold facts
Learn more about

What is your favorite way to give back to the community?

What fictional universe would you like to live in?

In your spare time (or if you had spare time), you would absolutely do this:

Your favorite city in the world is...and why?

You cannot start the day without doing this:

Your ultimate celebrity dinner party guest list would include:

Long-term personal or professional goal?

Any other facts to share?
Read our team’s latest bold insights
The critical component missing from AI technology
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.
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.