The case for a universal medical coder to achieve true interoperability

The healthcare industry is once again entering a period of heightened expectations for interoperability. Federal agencies are stepping up the fight against information blocking, promoting an interoperability framework, expanding the U.S. Core Data for Interoperability (USCDI) system, and signaling greater accountability for technology vendors and developers.

At the same time, industry leaders are promoting emerging concepts such as “conversational interoperability,” which essentially involves clinicians using natural language to query electronic health records (EHRs) and immediately retrieve relevant information.

This vision reflects optimism that new technologies, particularly AI and large language models (LLMs), will simplify clinicians’ interaction with complex systems. Yet history reminds us that enthusiasm for the next breakthrough often exceeds reality. From early vocabulary standards to “semantic interoperability” to Healthcare Rapid Interoperability Resources (FHIR), each wave of interoperability initiatives has promised transformation but struggled with the same obstacle: the absence of clean, structured, clinically valid data as a foundation.

A promising, but incomplete trend

Conversational interoperability may gain attention over the next 9-12 months as demonstrations of AI-based interfaces continue to impress audiences. The concept is appealing because it promises to reduce the friction clinicians face when navigating EHRs. However, the AI ​​can only surface information that exists in the file. If the underlying data is incomplete, unstructured, or inaccurate, the results of a natural language query will also be erroneous. In other words, bad data leads to bad conversations.

LLMs have additional limitations. They can hallucinate, returning confident but incorrect answers, and require enormous computing resources. Without structured input, these tools risk amplifying gaps and errors rather than resolving them. Likewise, vendor demonstrations appear convincing, but their practical use reveals the fragility of systems built on fragile databases.

The persistent data challenge

The reality is that most healthcare data remains unstructured. Critical details about symptoms, treatments, and patient context often reside in free-text notes or disparate systems, inaccessible to structured queries. When this information cannot be reliably extracted, clinicians are left with incomplete views of their patients, compromising both the quality and safety of care.

Standards such as FHIR provide mechanisms for aggregating and reporting data, but they do not address the issue of ensuring that the data is clinically meaningful. FHIR, in practice, is often a container of inconsistent or incomplete information rather than a guarantee of usability. True interoperability requires more than the ability to exchange data; this requires that the data exchanged have consistent clinical meaning across systems, users, and use cases.

Why structured, clinically valid data is important

Structured, clinically valid data is essential for several reasons:

• Clinical decision making: Providers rely on accurate, contextual information to make safe and effective treatment decisions. Inaccurate or incomplete data can directly impact patient outcomes.
• Care coordination: As healthcare delivery becomes increasingly distributed across networks of hospitals, clinics and post-acute care facilities, the ability to share standardized and meaningful data is vital for continuity of care.
• Population health and value-based care: Risk stratification, quality measurement, and outcomes-based reimbursement all depend on accurate, structured data that can be aggregated and analyzed.
• Support for innovation: Whether it’s predictive analytics, clinical decision support, or emerging AI applications, advanced tools can only be as effective as the data they rely on.

Without a reliable database, any other interoperability initiative, whether conversational, semantic or technical, remains incomplete.

The case for a universal medical coder

One path to solving this challenge is the development and adoption of a universal medical coder: a system capable of translating clinical concepts into structured, standardized, and contextually accurate representations at the point of care.

Such a tool would map free text entries and unstructured documentation into consistent, clinically valid codes across all vocabularies, including the International Classification of Diseases (ICD), Systematized Nomenclature of Clinical Terms of Medicine (SNOMED CT), Logical Observation Identifier Names and Codes (LOINC), and others.

Regulatory compliance and billing efficiency are essential functions of a universal medical coder, but its greatest value lies in creating a true clinical database. By capturing concepts in real time, within the clinician’s workflow, it ensures that data remains accurate, complete and interoperable between systems. This in turn would allow interoperability frameworks such as FHIR to deliver on their promises, as the data contained within the container would be as usable as the container itself.

A positioning for the future

Health officials should resist the temptation to resort to the latest buzzword as a goal. Conversational interoperability, while fascinating, should be viewed as a layer within a broader architecture.

The underlying challenge remains unchanged: the industry must first invest in data integrity and fidelity. Only then will advanced applications, such as conversational interfaces, predictive AI or population health analytics, have a lasting impact.

This approach also requires balance. The industry benefits from innovation and enthusiasm, but it must temper expectations with realism. Impressive demonstrations should not distract from the hard work of creating structured, clinically valid datasets. Policymakers, vendors, and providers must recognize that interoperability cannot be solved by a single user interface or standard. Instead, interoperability is achieved when each patient encounter produces usable, exchangeable, and meaningful data.

Conclusion

Healthcare’s renewed push toward interoperability is both necessary and long overdue. Enforcement of information blocking regulations, expansion of USCDI, and industrial innovation are all vital steps. However, these initiatives will not reach their full potential unless the industry prioritizes structured, clinically valid data as an essential foundation.

The emergence of concepts such as conversational interoperability highlights both the opportunities and risks of the current moment. Such trends can improve usability, but they cannot compensate for poor data quality.

A universal medical coder, applied consistently across all healthcare settings, offers a practical solution to the persistent data integrity challenge. Only by meeting this fundamental requirement can healthcare move beyond cycles of over-promised breakthroughs and realize the vision of truly interoperable, patient-centered care.

Photo: nevarpp, Getty Images

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