Google recently faced pushback over a new health benefits tool that used AI to recommend plan options. Employees worried they’d be required to share personal data with a third-party vendor to keep their health coverage. Google later clarified that wasn’t the case—the tool was voluntary, and data sharing wasn’t mandatory.
That clarification helped. But the reaction itself said something important about the moment we’re in: even highly technical people are uneasy about how AI systems handle health data.
And healthcare organizations—payers, providers, and digital health companies—should pay attention.
The signal behind the story
AI is rapidly being embedded into the systems that manage health and wellness. Health plans are exploring personalization engines for member benefits. Providers are integrating predictive models into care coordination. Employers are using AI tools to help employees navigate coverage options.
Each of these use cases brings value, but they also depend on personal data—sometimes deeply personal. When people start to feel like participation is required or data use isn’t fully transparent, trust erodes fast.
That’s the real message from Google’s experience: the next phase of AI adoption in healthcare isn’t a technical challenge. It’s a trust challenge.
The new reality of AI and health data
AI models thrive on detail. They learn from the patterns in claims data, EHR records, biometric feeds, and social determinants. But those same data flows raise questions about who controls them, how they’re secured, and what they’re used for beyond the original purpose.
Healthcare organizations can’t rely on old notions of “consent.” People now expect ongoing visibility and choice—especially as AI tools start influencing coverage decisions, clinical recommendations, and benefit eligibility.
A simple privacy disclaimer isn’t enough anymore. The standard is shifting toward explainable data relationships—where every participant knows what’s being shared, why, and how it benefits them.
How healthcare leaders can get ahead of the trust gap
To build confidence in AI-driven health systems, leaders should focus less on compliance checklists and more on transparency, control, and intent.
Here are five principles to guide responsible adoption:
1. Keep participation optional and meaningful
AI tools should empower choice, not make it conditional. Whether it’s a member-facing assistant or a clinical recommender, ensure users can opt in without losing access to essential benefits or services.
2. Clarify the purpose—and the limits—of data use
Be explicit about which data types are collected and why. Spell out what’s off-limits for sharing or inference. Avoid broad “data enrichment” clauses that make people question your motives.
3. Involve compliance and ethics teams early
Data governance, privacy, and clinical compliance should be part of the design phase, not an afterthought. That’s how you catch potential conflicts before they hit production.
4. Vet and monitor third-party partners
If your AI solution relies on a partner’s models or data infrastructure, require ongoing audits. Transparency shouldn’t stop at your organizational boundary.
5. Communicate the human value
Explain in plain language how AI helps people—shorter wait times, fewer denials, faster service. When individuals see tangible benefits tied to their data, trust grows.
What’s at stake
AI is already reshaping healthcare administration and decision-making. Done right, it can reduce costs, streamline processes, and make care more personal. Done poorly, it can deepen skepticism and slow adoption across the industry.
The lesson from Google’s policy flare-up isn’t about blame—it’s about awareness.
The public is paying close attention to how organizations use health data in the age of AI. If transparency and trust aren’t built in from the start, even well-intentioned programs can backfire.
As healthcare organizations move forward, the question isn’t just “Can we use AI this way?”
It’s “Will people trust us to?”
