How a Europe trip changed my healthcare AI governance design perspective
Introducing the “Three Es” Philosophy: Ethical, Easy, Enforced
Travel rewires the brain.
During a recent trip to Europe, I was blown away by the mundane systems integrated into citizens’ daily lives.
While a European may find my fascination with recycle bins and Internet cookies to be surprising, there are many lessons we Americans can learn.
And it totally changed the way I will think about AI governance in healthcare in the future.
Lesson One: Recycling Made Effortless
As I strolled through Vienna, I never looked for a trash can.
Color-coded bins sat at nearly every corner with clear labeling for the material to be deposited.
Unlike the U.S., where recycling can be a chore (or even impossible in some areas), it could not have been easier on my trip.
In addition to its ease, locals treated recycling as non-negotiable; those who improperly disposed of items were quietly corrected.
Takeaway: Make doing the right thing the easiest thing. Good people will act appropriately, and with a firm social nudge, so will almost everyone else.
Lesson Two: Privacy by Default
In Budapest, every U.S. app on my phone suddenly “cared deeply about my privacy.”
Pop-ups asked for permission to track me, something that almost never happens back home.
The change traced back to one law: the General Data Protection Regulation (GDPR).
What is GDPR, and why does it matter for AI governance in healthcare?
GDPR is a 2018 European Union regulation that gives users control over their personal data.
It requires clear consent, data-minimizing designs, and hefty fines for violations.
In other words, it puts the privacy and autonomy of the user first.
How America Needs to Improve in Healthcare Technology Design
Some websites now make it possible to opt-out of non-necessary cookies, but the process is often cumbersome and not intuitive. I believe this is by design.
Some states, like California, have stronger data privacy laws. The result is a patchwork of inconsistent systems that more often than not prioritize profits over people.
Takeaway: Start with the end user in mind when designing systems. Safeguards are foundational and should be embedded before the first line of code is shipped. Retroactive fixes are rarely as effective.
What lessons can we learn from Europe in Healthcare AI design?
When a system combines ethics, ease, and enforcement, the result is effective with low friction. I call this philosophy the “Three Es” and will incorporate it heavily into my thought process moving forward.
Ethical: Be intentional about data use, update schedules, and bias design from the start.
Easy: Doing the “right thing” should follow the path of least resistance.
Enforced: Responsible use can be enforced at the system and social levels.
Conclusion
Make doing the right thing easy, and most people will. Make it mandatory, then almost everyone must.
That’s why it’s so important that we address the broken incentives that are so pervasive in healthcare, because they do affect healthcare AI as well. Using AI in healthcare can be a powerful tool, but we cannot let patient safety fall to the wayside.
Read how I use AI in my writing here: AI Use Policy
Read how I use analytics to improve my newsletter here: Privacy & Analytics
Great article.
Designing AI systems that make the right choice effortless, hold users accountable, and prioritize ethics from the start feels not just thoughtful, but essential.