When Every Second Matters: How Athenahealth Protects Patients with Apollo GraphQL
Valeria Gomez
Insights shared live at GraphQL Summit 2025 from architect Colin Barringer on modernizing healthcare systems safely and at scale
Clinical software is unforgiving. If the right screen does not update in time, a patient can be harmed. For Athenahealth, that wasn’t theoretical: in one workflow, a nurse could record a latex allergy while a surgeon’s view failed to refresh. Preventing that error became the spark for bringing in Apollo GraphQL.
When architect Colin Barringer described the challenge, he didn’t soften the stakes: “If we make the wrong mistake, a person could die.” That level of consequence shaped every decision, from where to start to how quickly they could safely ship.
Athenahealth set a clear goal: enable real-time data updates across clinical roles without disrupting a 25-year-old production system used by more than 20,000 medical practices. Achieving that meant threading new technology through legacy code, one verified step at a time.
The Problem: Legacy Code in Life-or-Death Software
Athenahealth’s platform has been evolving since 1997, long before today’s cloud playbooks existed. Over time, thousands of developers contributed code that still ran at scale but carried decades of tradeoffs.
One artifact from that history was the data broker, a custom JavaScript library built in 2013 that had become a bottleneck. It efficiently batched data requests in its era, but it didn’t integrate with React, had no dedicated support, and couldn’t stream updates between users. For clinical software, that gap is a risk.
The challenge wasn’t just technical. Teams were measured by how fast they shipped features, not how safely they modernized the stack. Everyone agreed modernization was needed, but no one wanted to be the first to slow delivery in an environment where lives and uptime were on the line.
The Approach: A Steel Thread Through Legacy Code
Barringer borrowed an idea from bridge building: start with a single steel thread across the gap and strengthen it step by step. In software, that means shipping one thin, end-to-end slice in production to prove value and safety before expanding.
The team chose the ambulatory surgical center (ASC) workflow as their test case. It required instant data synchronization that the existing stack couldn’t support, but Barringer knew that Apollo’s GraphQL subscriptions could. The specialty also covered just ten practices out of more than 20,000, making it low risk yet high visibility.
They changed only what was necessary. The allergy entry page stayed as it was, still using the data broker and proprietary APIs. They added a React micro-frontend with Apollo Client connected to Apollo Server running in AWS. The server called Athenahealth’s public REST APIs and listened to a database event queue. When relevant events appeared, it filtered and pushed updates to clients through Apollo subscriptions.
One design choice set the direction for everything that followed. The team built resolvers on Athenahealth’s public APIs instead of internal endpoints, so employees and partners worked from the same surface. Barringer called it “drinking our own champagne.”
The Results: Real-Time Safety and Rapid Adoption
The turning point came during the first live test. A nurse added a latex allergy, and both browsers updated in under one second against a five-second safety budget. Average operation time was ~230 ms, with 30–40 ms from the extra network hop. It was a small moment, but it proved everything: safety, speed, and trust in the new system.
Operation volume grew from 200,000 per month to 75 million in seven months. When Apollo reached a mainstream feature, traffic doubled again, reaching ~250 million operations within a year. A single steel thread had become the foundation for real-time care at scale.
The Platform: Infrastructure as Product
Athenahealth treated its platform like a product. The team prioritized consistency and developer experience, delivering templates and tools that let other teams move fast without sacrificing safety.
Today, multiple React micro-frontends share a single Apollo Client instance that batches requests, deduplicates operations, and keeps views in sync. Apollo Router federates across three subgraphs: electronic health record data, billing, and event processing, with plans to grow to about twenty as adoption expands.
The Next Frontier: Connecting AI Agents Safely
Athenahealth is also experimenting with Apollo MCP Server to connect AI agents safely to their supergraph. In a live demo, an AI assistant used the graph to retrieve insights about social determinants of health directly from Apollo GraphQL. Setup was simple: they whitelisted safe queries and let the agent use those tools.
Barringer emphasized that trust and safety remain paramount as they design controls to prevent hallucinations or unsafe data exposure. The company’s legal and engineering teams are now collaborating on frameworks to define and validate trustworthy agent behavior.
Lessons You Can Apply
Athenahealth’s experience offers a repeatable pattern for organizations modernizing legacy systems in regulated, high-stakes environments:
- Identify a use case that cannot be solved with existing technology.
- Start with a small, controlled scope but real business value
- Deploy end-to-end in production to prove value and safety.
- Document decisions and scale with governance, not guesswork
- Invest early in shared tooling and templates to sustain safe velocity
As Barringer summarized, “The steel thread approach allows us to build the bridge to new tech in midair.”
For teams considering Apollo GraphQL in mission-critical systems, Athenahealth’s story shows that careful constraints like patient safety, legacy preservation, and limited scope can become advantages. Once developers see real value and ease of use, adoption accelerates on its own.
Watch Colin Barringer’s full session and demo from GraphQL Summit 2025.