Hospitals didn’t buy EHRs, HIS, or ERPs because they liked the software. They bought them for a very practical reason: to no longer depend entirely on people’s heads. The goal has always been standardization. Do things the same way every time. Make the second clinic work like the first. Make results less dependent on who is on the shift.
And to a large extent, it worked.
Modern healthcare systems already encode much of organizational memory. Protocols, order sets, billing rules, inventory logic, authorizations: all this institutional knowledge is transformed into software. Anyone who says “software never helped” is either a newcomer to healthcare or rewriting history.
But there has always been a ceiling.
Traditional systems are effective at applying what we already understand clearly and in advance. They are much worse at handling anything that is ambiguous, situational, or learned over time. As soon as something becomes non-deterministic – “it depends”, “usually”, “except when” – it falls on humans.
The remaining 20% is where healthcare operations actually take place.
The reception decides whether or not to admit a patient.
The nurse adapts a protocol because something is wrong.
The billing team applies a known workaround to avoid rejections.
The administrator knows which rule matters and which can be bypassed.
None of this is noise. This is the system that works. But historically, this part of the organization has been almost impossible to code. Not because it doesn’t matter, but because it was too expensive to formalize. You should have anticipated every edge case, written every rule, and maintained it forever.
So hospitals standardized what they could and humans took care of the rest.
What’s changing today is not that hospitals suddenly want memory. They always have. What’s changing is that AI reduces the cost of capturing and using context.
As AI makes intelligence cheap, the real advantage shifts to organizations whose systems can accumulate and reuse context, the hard-won understanding of how work actually gets done.
This is the same change we see in software development. AI hasn’t just made coding faster. This made nondeterministic work cheaper. Work where the input isn’t perfectly defined, where the right answer depends on history, intent, and precedent.
Healthcare operations are full of this kind of work.
AI agents change the equation because they can integrate into workflows and do three things that systems couldn’t do before at scale: observe decisions, retain the rationale, and reuse it later.
Not by hard-coding rules from the start, but by learning from the actual behavior of the organization.
This is where “memory” becomes very literal.
A hospital that remembers is not a hospital with more dashboards. This is one where the system can say things like:
- “This situation comes up a lot, and this is how we usually resolve it.”
- “When this protocol is exceeded, the results are better or worse.”
- “These exceptions keep repeating themselves; maybe they shouldn’t be exceptions anymore.”
It’s new.
Before, the only way to get this information was through postmortems, meetings, consultants, or heroic managers. Now this can happen continuously, as work occurs.
This has second-order effects that are easy to underestimate.
First, it changes who wins.
Over the next 12 to 18 months, hospitals that consider AI as a feature will experience marginal gains. Hospitals that view AI as a means to capture organizational judgment will get worse. The gap between “well managed” and “barely holding together” will widen, not because one has better doctors, but because one learns faster than the other.
Second, it triggers a version of the Jevons paradox in operations.
When you reduce the cost of exception handling, the throughput demand increases. If scheduling becomes easier, more appointments take place. If documentation becomes lighter, clinicians see more patients. If billing friction decreases, organizations increase volume further. The capacity does not free up and remain unused, it is consumed.
Hospitals that do not integrate memory into their systems will experience this as chaos. Hospitals that do this will feel it as leverage.
Third, and this is the uncomfortable part, AI reveals weak infrastructure.
AI agents do not work in isolation. They need context: unified data, consistent workflows, decisions made within systems rather than through side channels. Fragmented software doesn’t get smarter with AI; it gets stronger.
This is why the future is not “AI replaces systems”. It’s the opposite. AI gives more value to integrated systems because memory can only be formed where work actually takes place.
In five years it will seem obvious.
It may seem odd that hospitals once relied so heavily on institutional knowledge that disappeared when someone went on vacation. It will seem ineffective for organizations to repeatedly relearn the same lessons. And it will be clear that the real competitive advantage was not intelligence, but memory.
Not remembering the facts. Remember how the organization thinks.
This is what changes now. And hospitals that don’t adapt won’t fail overnight, but they will find themselves constantly relearning what others have already coded.
This is the work we spend our time on Ecaresoftbuild systems like Nimbo And Cirrus that are close enough to daily operations to truly capture context, not just transactions. Not as a vision of the future, but as something that hospitals and clinics are already using to standardize what they can, and now, increasingly, to learn from what they couldn’t before.