It’s 2 a.m. in the ER, and a physician is asking the hospital’s new AI assistant to summarize a patient’s chart. The cursor spins for minutes while the AI tool waits on the decades-old database underneath to answer, something it was never designed to do.
The electronic health record (EHR) system is too old and was not built to keep up with AI’s demands. It’s like asking a 50-year-old runner to sprint with a 25-year-old; a pulled muscle or torn ligament is coming. Hospitals everywhere are operating this way as they onboard AI tools, not knowing whether their underlying systems can withstand the warp-speed demands of these technologies.
In another industry, a slow or sometimes-crashing system may create headaches for the sales team. In healthcare, patients are at risk. The doctor may have forgotten how to find the information “the old way” as the cursor spins and spins. The intersection of overloaded and aging systems, outsized patient needs, and today’s desire for speed and emergent technologies has the makings of a crisis. And it is a crisis nobody in healthcare is enthused to talk about or proactively solve.
HEALTHCARE IS DAZZLED BY AI PROMISES
Healthcare AI comes with massive upside; insights are frequently published touting how AI is making healthcare more prescriptive, efficient, and cheaper. It can help with workforce shortages and faster documentation, while providing speedy answers and shrinking time spent navigating prior authorizations. OpenAI is now teaming up with Epic, the largest EHR company in the U.S., which also has its own roadmap of AI features. Major national insurers are embedding solutions from those same AI companies onto their platforms. Health systems are stacking chat overlays and AI layers on top of all of it.
The AI genie is out of the bottle, and nobody wants to put it back. The problem, however, is that few leaders are asking whether the underlying EHR systems can handle the newfound pressure. If no, many large health systems and insurers could find themselves in predicaments with their EHRs. When something gives, it will give out without warning.
The problem is not unique to healthcare. Reporting from 2023 cited Dell estimating that 70% of software used by Fortune 5000 companies was developed at least 20 years ago. In a 2026 survey, 97% of U.S. database professionals said their organizations run at least some versions of outdated programs.
Every piece of healthcare software sits on a data architecture built for the structured, intermittent access of one clinician clicking through one screen at a time. An AI chatbot changes that entirely, as it queries the data in real time and then decides what output the doctor sees. The data architecture was never designed to be asked that quickly or that often. Old or misread data could easily become a confident chatbot response to medical staff.
Multiply that by thousands of users, as the AI queries and normal human staff on deck simultaneously hit the EHR day and night. A slowdown of the EHR under this duress doesn’t stay inside the new AI tool. It will drag down every workflow across the entire health system. The healthcare industry is essentially testing the bridge’s weight limit by driving more trucks across it instead of checking the bridge’s rating.
WHAT’S THE PLUMBING LIKE?
Rebuilding data architecture is expensive, complicated, and genuinely risky. Upgrading a production database can carry operational risk, including compatibility problems and service disruption, while running unsupported software creates security and reliability risks.
It’s like painting a house every few years, but not fixing what’s behind the walls, like aging plumbing or electrical wiring, until something breaks. Companies and governments struggle to fund work like modernizing infrastructure when it has no direct line to revenue, especially when the only return is avoiding a failure or crisis. It seems easier just to repaint.
The fast, unsupervised access by AI has made this old problem urgent in a way it wasn’t even six months ago. If one hospital runs a small AI pilot and it goes wrong, the damage remains small. If every hospital in a health system across a state moves at once, it could trigger a crisis of its own making without ever knowing it was possible. Leaders worry about cybersecurity and hacking, but few are looking underneath the hood.
Stakeholders should share responsibility to avoid fallout. That means health system technologists confirming that infrastructure can support a new tool in production before they deploy it. Vendors adding AI to existing healthcare platforms must know their own systems’ capabilities, because that creates risk for customers. AI builders offering products that bolt onto someone else’s underlying system will push that system’s limits. What’s the plan when it freezes up?
ASK THE HARD QUESTIONS NOW
It’s time to focus on risk mitigation. Healthcare is already in the AI race, driven by momentum and a desire to keep up with changing technologies. But as a sector where patient safety is on the line, we cannot afford to ignore what AI is being bolted onto and what could happen when it fails.
A 50-year-old runner who pulls a muscle keeping pace with a 25-year-old won’t have just one injury if he keeps running at the same speed; he will have them over and over again. Decades-old architecture will break down the same way when it’s asked to keep up indefinitely.
Three steps are needed, the first being to audit reality. Health systems and insurers must understand what their infrastructure can handle when AI comes calling. That includes how old systems are and how things perform when both AI and staff query at the same time. Second, invest. Leaders should proactively fund infrastructure upgrades with the same urgency they are bringing to AI rollouts by supporting or replacing underpinning technologies before it’s too late. Third, commit to a roadmap, as modernization endeavors are a multiyear effort and the long-term return will be AI that works.
The value of AI cannot be realized until the industry addresses what nobody wants to talk about—that AI adoption is moving faster than anyone’s understanding of the risk beneath it.
Effie Carlson is CEO of Watershed Health.
