Every day, millions of lab results, orders, and clinical notes move silently between systems inside a hospital, running through something almost no one on staff will ever see: the interface layer. It works like a healthcare organization’s nervous system, carrying signals between the EHR and everything connected to it. Most of the time, that traffic is invisible by design—until something breaks and a clinician is left making a decision without the full picture. Yet many organizations still rely on legacy systems they’ve used for decades, hoping the cracks won’t show. They’ve learned to work around interface engines that struggle to support growing integration demands, connect systems quickly, and manage increasingly complex data exchanges. Meanwhile, those systems have fallen further behind advances in technology, especially AI.
Hospitals and health systems are evolving. Holding on to a legacy interface engine risks falling behind advances across the healthcare ecosystem while maintenance costs rise without added value. Replacing these engines has become a key priority on CIO roadmaps, but swapping one legacy system for another that does the same job is only worthwhile if it delivers significantly greater value. The real question is no longer whether data gets from system A to system B. It’s whether that data arrives accurate, current, and ready to use the moment someone needs it.
Why legacy systems get replaced
The reason so many organizations stay on legacy systems is simple, said Michael Marquez, director of interoperability solutions and strategy at ELLKAY: nothing appears to be wrong. “When integrations are working in healthcare, the interface engine is invisible. Everything works and no one pays attention,” he said.
But this can change fast.
“Because the interface engine is the lifeblood of the EHR and the EHR is the lifeblood of clinical care, when an event occurs or something breaks, leadership notices and it becomes “strategic” very quickly,” Mr. Marquez said.
Three pressures typically force the issue. A growing integration backlog delays strategic initiatives and pulls teams off higher-priority work just to maintain legacy connections. Tribal knowledge walks out the door as experienced staff retire, leaving undocumented interfaces and workarounds behind. And budget pressure keeps rising, which a hand-maintained interface layer makes harder to absorb, not easier, without putting the reliable data exchange clinicians need at the point of care at risk.
When organizations bring these problems to ELLKAY, it rarely stays a conversation about which system to buy next. It turns into a bigger one: what the organization actually wants its data to do.
What replacing it should actually solve
For most organizations, that starts as a mindset shift before it’s a technology one: moving from scattered, single-purpose data silos to one enterprise-wide strategy, so data trapped in one system can actually be used somewhere else.
It also means treating data as a strategic asset and a source of revenue and better clinical decisions, rather than reactively, only when something breaks. That requires curating data into a format people can actually use, not just storing it.
Connectivity alone doesn’t get them there. A traditional interface layer is built to focus on connectivity, getting the data to flow from system A to system B. What’s replacing it has to do more: account for the quality of that data, and how it’s presented and used to make better decisions.
“Data orchestration is key. That is connectivity plus quality plus usefulness,” said Gurpreet Singh, senior vice president of interoperability solutions and strategy at ELLKAY. “We help organizations think about the value of data with a broader lens than just connectivity.”
It also means technology that increasingly resolves its own problems instead of waiting for someone to notice them. In a legacy environment, teams troubleshoot by hand, often by just resetting the system. In a modern one, the technology diagnoses the issue itself and recommends a fix.
“The technology can enable the first level of triaging, by solving problems and proposing solutions,” Mr. Singh said. “It is about simplifying, having technology do things on its own and then proposing things that people can implement and agree or disagree with.”
And increasingly, it means one strategic partner instead of a stack of point vendors, each accountable for a single layer and nothing past it.
“Organizations with a data orchestration viewpoint are saying, ‘We don’t want to coordinate with all of these disparate vendors. We want one partner that can orchestrate and coordinate with us,'” Mr. Marquez said.
Managing that many vendors is already close to a full-time job for most IT leaders. Bringing those areas under one platform frees that time back up for the work the organization actually needs them doing.
Making the change
The turning point often comes when a CIO or senior IT leader recognizes that legacy infrastructure is no longer simply an IT burden, it is limiting the organization’s ability to innovate. When valuable clinical and operational data remains trapped in disconnected systems, analytics teams spend more time assembling information than acting on it, and AI initiatives lack the timely, complete data needed to deliver meaningful results. At that point, maintaining the status quo carries its own cost: slower decisions, delayed innovation and missed opportunities to improve care and operational performance. Modernization becomes less about replacing technology and more about unlocking the value already sitting inside the organization’s data.
Those risks can be reduced by transitioning in phases, with each step tested and validated before the next begins, often without end users ever noticing the change behind the scenes. That phased approach is also why this isn’t a luxury upgrade to get to someday. It’s an evolution most organizations are already partway through, whether their interface layer has caught up yet or not.
As healthcare organizations adopt AI for clinical and operational uses, bolting AI models onto legacy platforms won’t get them far. A modern, robust data infrastructure has to come first. “Without the right data fabric under the AI, there’s no foundation for innovation,” Mr. Singh said.
ELLKAY has supported large, midsize and rural health systems and hospitals through this kind of modernization.
“The approach we’re using at ELLKAY is to look beyond an interface engine as a data connectivity mechanism to intelligent data orchestration,” Mr. Singh said. “We help organizations generate value from their data, so that the data can be used to make meaningful changes within the organization. This includes driving revenue and, most importantly, driving better care for patients.”
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