The case for true interoperability with universal medical coders

The healthcare industry is once again entering a period of heightened expectations for interoperability. Federal agencies are stepping up enforcement against information blackouts, promoting interoperability frameworks, expanding the U.S. Core Data for Interoperability (USCDI) and demonstrating greater accountability for providers and technology 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 artificial intelligence and large language models (LLMs), will simplify clinicians' interactions with complex systems. Yet history reminds us that enthusiasm for the next breakthrough often outweighs reality. From early vocabulary standards to “semantic interoperability” to Fast Healthcare Interoperability Resources (FHIR), each wave of interoperability initiatives promises transformation, but all face the same obstacle: a lack of clean, structured, and clinically valid data to build upon.

A promising but incomplete trend

Conversational interoperability is likely to gain traction over the next nine to 12 months as demos of AI-driven interfaces continue to impress audiences. The concept is attractive because it promises to reduce the friction clinicians face when using electronic medical records. However, AI can only reveal information that exists in the records. If the underlying data is incomplete, unstructured, or inaccurate, the results of a natural language query will be equally flawed. In other words, flawed data leads to flawed conversations.

Additional restrictions apply to the LL.M. They can produce hallucinations, return confident but incorrect responses, and they require significant computing resources. Without structured input, these tools risk amplifying gaps and errors rather than solving them. Likewise, vendor demonstrations may seem compelling, but actual use reveals the fragility of systems built on weak data.

Ongoing data challenges

The reality is that most healthcare data remains unstructured. Critical details about symptoms, treatments, and patient background often reside in free-text comments or disparate systems and are not accessible through structured queries. When this information cannot be reliably extracted, clinicians are faced with an incomplete view of their patients, compromising the quality and safety of care.

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

Why structured, clinically valid data is important

Structured and clinically valid data is crucial for the following reasons:

• Clinical decision-making: Providers rely on precise, context-aware information to make safe and effective treatment decisions. Inaccurate or incomplete data can directly impact patient outcomes.
• Care Coordination: As health care services become increasingly distributed across a network of hospitals, clinics, and post-acute facilities, the ability to share standardized and meaningful data is critical to continuity of care.
• Population Health and Value-Based Care: Risk stratification, quality measurement, and outcomes-based reimbursement all rely on accurate, structured data that can be aggregated and analyzed.
• Innovation empowerment: Whether through predictive analytics, clinical decision support or emerging artificial intelligence applications, advanced tools are only as effective as the data they rely on.

Without a solid data foundation, all other interoperability initiatives, whether conversational, semantic, or technical, remain incomplete.

The case for universal medical coders

One way to address this challenge is to develop and adopt a universal medical coder: a system capable of translating clinical concepts into structured, standardized, and contextually accurate representations at the point of care.

Such tools can map free text input and unstructured documents into consistent clinically valid codes across vocabularies, including International Classification of Diseases (ICD), System Nomenclature of Medical Clinical Terminology (SNOMED CT), Logical Observation Identifier Names and Codes (LOINC), and others.

Regulatory compliance and billing efficiency are essential capabilities of a universal medical coder, but its greater value lies in enabling a true clinical data foundation. By capturing concepts in real-time within clinicians' workflows, it ensures data remains accurate, complete, and interoperable across systems. In turn, this will allow interoperability frameworks such as FHIR to deliver on their promise, as the data within the container will be as available as the container itself.

Positioning for the future

Healthcare leaders should resist the temptation to end by chasing the latest buzzword. Session interoperability, while interesting, must be viewed as one layer within a broader architecture.

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

This approach also requires balance. The industry benefits from innovation and enthusiasm, but expectations must be tempered with reality. An impressive presentation should not distract from the hard work of building a structured, clinically valid data set. Policymakers, vendors, and providers alike must recognize that interoperability cannot be solved solely through user interfaces or standards. Instead, interoperability is achieved when every patient encounter yields usable, exchangeable, and meaningful data.

in conclusion

A renewed push for interoperability in healthcare is both necessary and long overdue. Regulatory enforcement of information blackouts, expansion of USCDI, and industry innovation are all critical steps. However, these initiatives will not reach their full potential unless industry prioritizes structured, clinically valid data as the necessary foundation.

The emergence of concepts such as session interoperability highlights current opportunities and risks. This trend may improve availability, but it cannot compensate for poor data quality.

Universal Medical Coder is applied consistently across care settings, providing a practical solution to the persistent challenge of data integrity. Only by meeting this core requirement can healthcare escape the breakout cycle of over-promise and realize the vision of truly interoperable, patient-centered care.

Photo: nevarpp, Getty Images

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