The healthcare industry is experiencing renewed urgency around interoperability as federal agencies ramp up efforts to combat information blocking. Initiatives such as the promotion of an Interoperability Framework and the expansion of the United States Core Data for Interoperability (USCDI) are setting a higher standard for accountability among providers and technology developers. Concurrently, industry leaders are advocating for innovative concepts like “conversational interoperability,” which allows clinicians to interact with electronic health records (EHRs) using natural language. This ambition reflects a belief that advancements in technologies, particularly artificial intelligence (AI) and large language models (LLMs), can streamline clinician access to critical information.
Despite this optimism, history shows that enthusiasm for technological breakthroughs often outpaces actual implementation. Previous initiatives, ranging from early vocabulary standards to Fast Healthcare Interoperability Resources (FHIR), have demonstrated a consistent challenge: the lack of clean, structured, and clinically valid data as a foundation for interoperability.
The Challenges of Conversational Interoperability
Conversational interoperability may gain traction in the coming months, especially as demonstrations of AI-driven interfaces impress audiences. This concept is appealing because it aims to reduce the complexity clinicians face in navigating EHRs. However, the effectiveness of AI in this context is limited to the quality of the underlying data. If data is incomplete, unstructured, or inaccurate, the outcomes of any natural-language query will reflect those flaws. As a result, poor data leads to ineffective conversations.
Furthermore, LLMs have limitations. They can produce confident but incorrect answers, a phenomenon known as “hallucination,” and require significant computational resources. Without properly structured inputs, these advanced tools may exacerbate existing gaps and errors rather than resolve them.
Addressing the Data Integrity Challenge
A significant issue in healthcare is that much of the data remains unstructured. Important information regarding symptoms, treatments, and patient context is often buried within free-text notes or scattered across disparate systems, rendering it inaccessible for structured queries. When this vital information cannot be reliably extracted, clinicians are left with incomplete views of their patients, undermining the quality and safety of care.
Standards such as FHIR offer frameworks for packaging and transmitting data but do not guarantee that the data exchanged is clinically meaningful. In practice, FHIR can serve merely as a container for inconsistent or incomplete information, lacking the assurance of usability that healthcare providers require. True interoperability hinges not only on the ability to exchange data but also on ensuring that the exchanged information maintains consistent clinical meaning across various systems and use cases.
The need for structured, clinically valid data is essential. Without a dependable data foundation, other interoperability initiatives—whether conversational, semantic, or technical—will remain inadequate.
One potential solution to this challenge is the development of a universal medical coder. This system would translate clinical concepts into structured, standardized, and contextually accurate representations at the point of care. Such a tool would convert free-text inputs and unstructured documentation into consistent, clinically valid codes across different vocabularies, including the International Classification of Diseases (ICD), Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and Logical Observation Identifiers Names and Codes (LOINC).
While regulatory compliance and billing efficiency are crucial functions of a universal medical coder, its true value lies in creating a robust clinical data foundation. By capturing concepts in real-time during the clinician’s workflow, it ensures that data remains accurate, complete, and interoperable across systems. Consequently, interoperability frameworks like FHIR can fulfill their potential, as the data contained will be as usable as the frameworks themselves.
Healthcare leaders must avoid the temptation to engage in the latest trends without addressing underlying data issues. Although conversational interoperability is an intriguing concept, it should be regarded as one element within a larger architecture. The focus must remain on investing in data integrity and fidelity to allow advanced applications like conversational interfaces and predictive AI to deliver lasting impact.
This approach requires a balanced perspective. While the industry benefits from innovation and enthusiasm, expectations must be tempered with realism. Compelling demonstrations should not distract from the essential work of building structured, clinically valid datasets.
Policymakers, vendors, and providers must recognize that interoperability cannot be achieved solely through user interfaces or isolated standards. Instead, it is realized when every patient encounter yields usable, exchangeable, and meaningful data.
In conclusion, the healthcare industry’s renewed commitment to interoperability is both necessary and overdue. Enforcement against information blocking, the expansion of the USCDI, and ongoing industry innovations are crucial steps forward. Nevertheless, these efforts will fall short of their full potential unless the focus shifts to establishing structured, clinically valid data as the foundational element. As concepts like conversational interoperability emerge, they underscore both the opportunities and the risks present in the current landscape. Improving usability is essential, but it cannot compensate for inadequate data quality. A universal medical coder, consistently applied across care settings, presents a practical solution to the persistent challenge of data integrity. Addressing this core requirement is vital for healthcare to move beyond cycles of over-promised breakthroughs and realize the vision of genuinely interoperable, patient-centered care.
