Pager Health Innovation & Technology Culture

How does innovation show up in your company culture?

At Pager Health, innovation is a daily practice, not a one-off initiative. Working at the intersection of AI and healthcare means curiosity and responsibility have to coexist. Our culture encourages teams to ask hard questions like, “Should we build this, who does it help, and where do clinicians need to stay in the loop?” before jumping to solutions.

Innovation also shows up in how cross-functional we are. Product, engineering, data science and clinicians collaborate closely, ensuring we’re not just building technically impressive systems, but ones that actually work in real healthcare settings. One of our secret weapons in our products that's been here since day one at Pager is how much clinical thought is baked into them. Our goal is not replacing physicians. We’re giving them AI superpowers so they can enjoy practicing medicine again. And the patients who see them? They receive faster and elevated care. Wins all around. 

At Pager, we value people who think deeply, challenge assumptions, and stay grounded in the lived experiences of patients and clinicians. That balance is what allows us to innovate responsibly.

What’s one recent innovation that improved user or employee experience?

One of our most significant recent innovations was the development of proprietary safety and clinical guardrails that allow us to responsibly expose a LLM directly to members in a healthcare setting. Internally known as Project Helix, this work took over a year and brought together PhDs, medical doctors and an exceptional team of engineers. I’m incredibly proud of the team not just for what they built, but for how thoughtful and rigorous they were throughout.

The goal was to solve one of the hardest problems in healthcare AI: enabling real-time, member-facing AI in high-stakes clinical scenarios without compromising safety or trust. The system actively de-risks situations like crisis emergencies and behavioral health events with real-time mechanisms that detect and intervene when patterns of harm emerge and bring clinicians in when needed.

We also invested in building systems that can identify hallucinations, intervene in the moment, and continuously refine the model. The result is a better experience on both sides: Members get faster, clearer guidance, and clinicians gain confidence that AI is augmenting care responsibly, not introducing risk.

How do you balance experimentation with stability?

In healthcare AI, stability starts with accuracy. We’re willing to experiment when the benefits are clear, but only within very high performance standards. An accuracy mistake in healthcare isn’t like getting a recipe wrong; it can directly impact someone’s loved one.

Before any model leaves experimentation, we test extensively across edge cases, real-world scenarios and clinical contexts until we clearly understand its accuracy and behavior. We don’t consider a model stable just because it works most of the time; stability means we can measure its accuracy, trust its outputs, and stand behind it in high-stakes situations.

Once in production, we continuously monitor model performance. If accuracy drift changes, our data scientists are alerted immediately so we can intervene to refine the model. That closed loop is how we innovate while maintaining the standards healthcare demands.

If you’re evaluating a healthcare company that says it’s using AI, ask questions about testing, accuracy and ongoing monitoring. Responsible AI in healthcare requires real rigor and discipline — and teams who lose sleep over getting it right (I have the Slack messages to prove it).