Why Interoperability Is Essential to AI in Healthcare
By André Leite and Vinícius Lain, authors of AI in Healthcare.
An artificial intelligence can only reason about what it can see. If exams, medications, images, allergies, history and data from different providers stay locked in isolated systems, the algorithm is working with a fragmented patient.
That is why interoperability is not a technical detail that comes before AI. It is one of the conditions for AI in healthcare to be clinically useful and safe.
The data exists, but it never arrives
The problem is a familiar one. The patient says they had a CT scan at another facility. The doctor knows the exam exists but cannot get to it in time. So it gets repeated. Another clinician prescribed a medication, but that information sits in a different system. An important result is in a scanned PDF. An allergy was recorded in a module that does not talk to the prescribing screen.
In that scenario, the health system has data but no integrated view. The consequences show up as repeated tests, delays, cost, risk and decisions made with incomplete context.
In the book, we use two images for this problem: the Titanic that receives a critical warning and cannot turn it into action, and the Tower of Babel where everyone is working but the systems speak different languages.
AI without interoperability is intelligence with partial vision
Picture an algorithm that is excellent at identifying clinical risk but sees labs and not medications. Or a decision-support system that reads the current EHR but never receives the exams done elsewhere in the network.
The output can look sophisticated and still rest on incomplete information. That is an especially dangerous risk, because the appearance of precision can hide the absence of context.
The quality of AI does not depend only on the model. It also depends on the data architecture around it.
Interoperability does not mean a single system
The solution is not necessarily to buy one platform that does everything. Specialized systems will keep existing. The goal is to let important information flow in a standardized and secure way.
This is where APIs and standards like HL7 FHIR come in, created to allow structured data exchange between different systems. Doctors and administrators do not need to master the technical specification. It is enough to understand the strategic consequence: an institution with an open architecture can connect solutions, swap out components and bring in innovation with less dependence on a single vendor.
Vendor lock-in: when innovation needs permission
One of the biggest obstacles to digital transformation is technological lock-in. The institution buys a core system and finds out, years later, that any new integration depends on expensive, lengthy projects or is technically restricted by the vendor.
That turns innovation into a request for permission.
Healthcare technology contracts should treat interoperability as a strategic requirement: documented APIs, the ability to export data, recognized standards, clear integration rules and access to information that belongs to the institution itself and to the patient.
Integrated data does not mean data without governance
Interoperability increases value, but it also increases responsibility. The more information circulates, the greater the need for access control, traceability, security, quality and a legitimate purpose of use.
The goal is not to open everything to everyone. It is to let the right data reach the right person or system, at the right time, with the right protections.
The patient at the center of the architecture
There is also an important conceptual shift: a clinical history should not be organized around the buildings where it was produced. It should follow the person.
When data travels with the patient, we depend less on individual memory to reconstruct treatments, exams and earlier decisions. The patient stops acting as a messenger between systems that do not talk to each other.
The silent prerequisite for AI
There is an understandable temptation to start digital transformation with what is most visible: a new algorithm, an impressive dashboard, a generative AI solution. But often the highest-impact work sits below the surface.
Data identity, integration, standards, quality, APIs and governance do not make for cinematic demos. But they are the infrastructure on which real intelligence can operate.
Before asking which AI to buy, an institution should ask another question: can our own data talk to each other?
If the answer is no, even the most advanced algorithm will keep trying to assemble the patient's story with pieces missing.
André Leite and Vinícius Lain are the authors of AI in Healthcare: How Technology Is Transforming the Future of Human Care.
Read more at iaemsaude.com/en.
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