My first role in healthcare was in 2018. The mission was ambitious: build connectivity to hospital EHRs using any technology available—RPA, HL7, IHE Profiles, FHIR, SFTP, custom APIs, or whatever combination would get us live. The broader goal was to connect the world’s health data, even if that meant stitching together brittle, inconsistent interfaces one system at a time. Connecting health data isn’t just an engineering challenge I’m drawn to—it’s a way to give people faster, safer, more efficient experiences with the healthcare system. When you’re on the other side of that experience as a patient, you realize how transformative it can be.
Around that time, I came across Redox and quickly realized the team was pursuing a similar mission but with deep domain expertise in EHRs and interoperability. We even partnered with Redox for parts of our connectivity work. It was obvious they understood both the complexity of healthcare data and the practical reality of making interoperability work at scale. In a space where most solutions only solve part of the problem, that kind of end-to-end expertise is uncommon, and it stood out immediately.
After that role, I spent the next few years in healthtech building complex workflows for providers & payers and AI products in revenue cycle management that relied directly on the quality of upstream data. That work gave me a front-row seat to how data flows across the healthcare ecosystem—from ambient capture, utilization management, and clinical documentation to coding, auditing, billing, payments, and denials. It also gave me a front-row seat to the promise of AI in healthcare—not as a shiny new technology, but as a way to deliver those faster, safer, more efficient experiences that first drew me to healthcare interoperability in the first place.
One pattern kept showing up: When upstream data is unreliable, everything downstream becomes more expensive, more manual, and less accurate. Above all, AI requires accurate data. Lots and lots of it. As a software engineer and CTO, these seem like challenges that can be solved, and I knew I wanted to be part of the solution.
Healthcare is finally ready for AI
Today, AI is showing meaningful results across healthcare workflows that have looked the same for decades. In order for AI to continue improving—and to produce accurate outcomes over an extended period of time—the data it consumes must be reliable, consistent, and accurate.
That’s why I’m excited to contribute to Redox’s mission right now. For more than a decade, Redox has been the connectivity layer of healthcare—normalizing data, simplifying integrations, and enabling products to move information across systems reliably. Combining that legacy with a clear plan to use and support AI doesn’t just position Redox to participate in the AI evolution; it positions Redox to lead it.
Redox: Where interoperability meets intelligence
I’ve learned that for AI to move beyond one-off tactical use cases and deliver real operational value, it needs two things: strong, well-structured APIs and access to rich, domain-specific datasets.
Redox has both.
Our Platform API exposes a clean, secure, and scalable layer over our capabilities, giving AI systems a consistent way to interact with an otherwise fragmented ecosystem. On top of that, thousands of proprietary clinical data mapping definitions capture years of complex interoperability knowledge, turning messy, variable clinical data into something AI can reliably understand and act on. And because we have data observability at scale, we continuously track how health data moves across systems—what’s sent, what’s received, what fails, and how it performs—so we can safely power and monitor mission-critical use cases.
When you combine these ingredients with LLM agents, you get a powerful foundation for an intelligent layer that can interpret intent, perform safe actions, and learn from real operational signals over time.
For me, that’s not just an interesting technical challenge—it’s why I came to Redox. We’ve already laid much of this foundation, proving that interoperable, reliable connectivity can exist at scale. Now we’re focused on leveling it up even further: turning that foundation into the platform the industry needs to unlock AI at scale so clinicians can focus on care, organizations can trust their systems, and patients ultimately feel the difference.
In my next post, I’ll share where we’re applying AI to reimagine the Redox experience, and what that means for the future of healthcare connectivity.
This post was written by Sasi Mukkamala, Redox’s Chief Technology Officer.