Synthetic Patients, Real Ethics: Why Nurses Must Shape the Future of Healthcare AI Training Data

As AI models accelerate into clinical decision-making, the need for vast, diverse, and ethically sourced data grows louder. Enter synthetic data: artificially generated health records designed to mimic real patients without compromising privacy. On paper, it sounds like a win: privacy-preserving, scalable, and bias-reducing. However, as this trend continues to surge forward, a critical voice is often missing from the conversation: nurses.

Synthetic ≠ Safe

Synthetic data sidesteps privacy risks, but it can still carry over real-world biases if trained on flawed sources. If your baseline data underrepresents marginalized groups or overrepresents certain conditions, the AI will echo those distortions. Nurses, who often see firsthand where documentation deviates from lived experience, must be at the table when these datasets are created and validated.

Representation Must Go Beyond the Chart

Clinical narratives, especially nursing notes, carry nuance that structured data often misses. Pain that doesn’t appear in vital signs. Discomfort that’s dismissed in documentation. If synthetic data is generated solely from structured EHR fields, it risks flattening the full spectrum of patient experiences. Nurses can advocate for the inclusion of narrative data elements in synthetic record generation without compromising identity.

Why Nurses Are the Missing Link

Nurses are trained to be observant, critical thinkers, and patient advocates. Yet few are consulted in data engineering teams building healthcare AI. Involving nurses from the ground up ensures that AI tools built on synthetic data accurately reflect the true clinical reality, rather than administrative abstraction.

The Next Frontier of Nurse Leadership

This is a call to action. Nurse leaders in informatics, research, and education must demand a seat at the table where synthetic data pipelines are designed. We don’t need to code the algorithms—but we must inform what data goes in and how it’s interpreted on the way out.

Key Takeaways

• Synthetic data offers promise but also risks reinforcing old biases in new forms.

• Nurse insight is crucial to ensuring that synthetic data accurately reflects real clinical complexity.

• Representation in AI starts with representation in data design.