The healthcare industry is once again entering a period of heightened expectations around interoperability. Federal agencies are intensifying enforcement against information blocking, promoting an Interoperability Framework, expanding the United States Core Data for Interoperability (USCDI), and signaling 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 the optimism that new technologies, especially AI and large language models (LLMs), will simplify clinician interaction with complex systems. Yet history reminds us that enthusiasm for the next breakthrough often outpaces reality. From early vocabulary standards to “semantic interoperability” to Fast Healthcare Interoperability Resources (FHIR), each wave of interoperability initiatives has promised transformation, but struggled against the same barrier: the absence of clean, structured, and clinically valid data as a foundation.
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A promising, but incomplete, trend
Conversational interoperability may capture attention in the next 9 to 12 months, as demonstrations of AI-driven interfaces continue to impress audiences. The concept is attractive because it promises to reduce the friction clinicians face when navigating EHRs. However, AI can only surface the information that exists within the record. 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.
LLMs present additional limitations. They can hallucinate, returning confident but incorrect responses, and they require enormous computational resources. Without structured inputs, these tools risk amplifying gaps and errors rather than resolving them. Similarly, vendor demonstrations appear compelling, but practical use reveals the fragility of systems built on weak data foundations.
The persistent data challenge
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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 face incomplete views of their patients, undermining both care quality and safety.
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. FHIR, in practice, is often a container for inconsistent or incomplete information rather than a guarantee of usability. True interoperability requires more than the ability to exchange data; it requires that exchanged data carry consistent clinical meaning across systems, users, and use cases.
Why structured, clinically valid data matters
Structured and clinically valid data are essential for several 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 healthcare delivery becomes more distributed across networks of hospitals, clinics, and post-acute 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.
- Innovation enablement: Whether through predictive analytics, clinical decision support, or emerging AI applications, advanced tools can only be as effective as the data on which they rely.
Without a reliable data foundation, every other interoperability initiative, whether conversational, semantic, or technical, remains incomplete.
The case for a universal medical coder
One path toward 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 inputs and unstructured documentation into consistent, clinically valid codes across vocabularies, including the International Classification of Diseases (ICD), Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and others.
Regulatory compliance and billing efficiency are essential functions of a universal medical coder, but its greater value lies in enabling a true clinical data foundation. By capturing concepts in real-time, within the clinician’s workflow, it ensures that data remains accurate, complete, and interoperable across systems. This, in turn, would allow interoperability frameworks like FHIR to deliver on their promise, because the data inside the container would be as usable as the container itself.
Positioning for the future
Healthcare leaders should resist the temptation to pursue the latest buzzword as an endpoint. Conversational interoperability, while intriguing, 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 will 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 it must temper expectations with realism. Impressive demonstrations should not distract from the hard work of building structured, clinically valid datasets. Policymakers, vendors, and providers alike must recognize that interoperability is not solved by a user interface or a standard alone. Instead, interoperability is achieved when every patient encounter yields usable, exchangeable, and meaningful data.
Conclusion
Healthcare’s renewed push toward interoperability is both necessary and overdue. Regulatory enforcement against information blocking, expansion of USCDI, and industry innovation are all vital steps. However, these initiatives will not achieve their full potential unless the industry prioritizes structured, clinically valid data as the essential foundation.
The emergence of concepts such as conversational interoperability highlights both the opportunities and the risks of the current moment. Such trends may improve usability, but they cannot compensate for poor data quality.
A universal medical coder, applied consistently across care settings, offers a practical solution to the enduring challenge of data integrity. Only by addressing this core requirement can healthcare move beyond cycles of over-promised breakthroughs and realize the vision of truly interoperable, patient-centered care.
Photo: nevarpp, Getty Images
David Lareau is Chief Executive Officer of Medicomp. Lareau joined Medicomp in 1995 and has responsibility for operations and product management, including customer relations and marketing. Prior to joining Medicomp, Lareau founded a company that installed management communication networks in large enterprises such as The World Bank, DuPont and Sinai Hospital in Baltimore. The Sinai Hospital project, one of the first PC-based LAN systems using email and groupware, was widely acknowledged as one of the largest and most successful implementations of this technology.
Lareau’s work at Sinai led to the founding of a medical billing company that led, in turn, to his partnership with Medicomp. Realizing that the healthcare industry made less use of information technology than almost any other industry, particularly in the area of clinical care, Lareau immediately saw the potential for Medicomp’s powerful technologies and joined the company to help fulfill Peter Goltra’s vision.
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