Entrepreneurs entering healthcare often spot inefficiencies and launch companies to solve them. Whether it’s optimizing transitions of care, simplifying billing, or empowering home health teams, most health tech startups inevitably deal with digital health information. But solving these problems means more than just building a great product – it means ensuring your solution speaks the same language as the rest of the healthcare continuum.
Healthcare data exchange involves a wide range of formats and standards – like HL7, FHIR, X12, NCPDP, etc. – used by different organizations for a variety of purposes. This complexity can make the critical task of sharing health data feel cumbersome and clunky. This is where interoperability comes into play. Though it’s become a buzzword, true interoperability is a prerequisite for patient-centered, efficient and scalable digital health solutions. And among the different types of interoperability, semantic interoperability stands out as the goal many are striving toward.
Understanding the layers of interoperability
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It’s easy to think of interoperability as a tech problem. But it’s really a trust and translation problem – making sure data carries the same meaning wherever it goes. Interoperability in healthcare can be broken down into three key levels: foundational, structural and semantic.
1. Foundational interoperability: Getting the data from point A to B
This is the most basic level – think of it as mailing a letter in any language. The infrastructure exists to deliver information from one system to another, but there’s no guarantee the recipient can interpret it.
This is where ubiquitous tools like fax machines come into play. DIRECT messaging also often operates at the foundational level, though it can carry structured data when paired with CDA or other rich attachments. While foundational interoperability ensures that data can be shared, it does little to guarantee usability. Systems may receive messages, but someone often has to manually interpret and input the information into the appropriate fields. These manual processes can lead to inaccuracies and inefficiencies creating additional administrative burdens for an already overloaded healthcare system.
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Foundational interoperability may check the “data exchange” box, but it contributes little to the automation, analytics, or timely clinical decision-making needed in today’ s healthcare ecosystem
2. Structural interoperability: Standardized formats, unstandardized meaning
Structural interoperability takes a step forward by using standard formats to organize data. This is akin to everyone agreeing to speak English, but with different regional dialects and expressions. You can understand the structure of what’s being said – but not always the intent.
In healthcare, standards like HL7 v2, CDA, and CCD establish common structures for how information such as patient demographics or discharge summaries is organized and exchanged. These formats simplify data parsing and transmission, but they don’t guarantee a shared understanding of what the data means. One system might record a “myocardial infarction,” another might call it a “heart attack,” and a third could use a proprietary internal code. This lack of semantic consistency creates errors, duplication, and extra layers of translation. While CDA and CCD are primarily structural standards, they can incorporate coded vocabularies that move implementations closer to true semantic interoperability when used effectively.
Custom integrations, data mapping, and middleware are often used to patch over these gaps – resulting in costly and time-consuming processes that don’t scale well.
3. Semantic interoperability: Speaking the same language, with shared meaning
Semantic interoperability goes a step further – not only does data arrive in a structured format, but both the sender and receiver use the same codes and terminology to define the data. To make this level of understanding possible, both public and private sectors are actively driving initiatives to develop and adopt standardized data exchange protocols. These efforts are essential to ensuring that healthcare data isn’t just exchanged, but is also interpreted consistently and used meaningfully across diverse systems and organizations.Think SNOMED for diagnoses, LOINC for lab results, and RxNorm for medications – all referenced via APIs like FHIR.
This level of interoperability enables machines to read, interpret, and act on data with minimal human input. A standardized diagnosis code of ICD-10 I21.9 (Acute Myocardial Infarction) can be instantly integrated into the patient’s chart, trigger clinical decision support tools, or inform discharge planning automatically.
This is what makes semantic interoperability the gold standard: it enables automation, improves accuracy, and reduces friction between systems. It powers features like real-time medication reconciliation, predictive analytics and patient-facing apps that actually understand your medical history.
Why it matters for your health tech company
If your platform interacts with patient data – whether it’s clinical, financial or operational – your long-term viability depends on how well you integrate with the healthcare ecosystem. Solutions that rely solely on foundational or structural interoperability are often brittle, require custom workarounds, and limit scalability.
Striving for semantic interoperability isn’t just about compliance – it’s a strategic differentiator. It allows your solution to:
- Eliminate redundant documentation.
- Enhance provider satisfaction by reducing manual workflows.
- Enable richer analytics and insights.
- Power real-time care coordination and decision support.
Recent federal initiatives like ONC’s USCDI v4, TEFCA, and CMS’s Interoperability and Prior Authorization Rule are accelerating the industry’s move toward standardized, machine-readable health data. Aligning with these frameworks not only supports compliance but also ensures long-term relevance as payers and providers adopt semantically consistent data exchange models.
The takeaway
Interoperability isn’t just a technical requirement; it’s a business imperative. Foundational and structural interoperability may get you through early integrations, but to unlock scale, automation and true clinical impact, semantic interoperability should guide your long-term architecture.
Photo: DrAfter123, Getty Images
Pascal Odek spearheaded the creation of WellBeam’s electronic health record-integrated platform aimed at transforming post-acute care workflows and reducing clinician burnout. He collaborates with providers to understand the unique challenges involved with coordinating services. By implementing electronic signatures and real-time messaging, the WellBeam team’s efforts have cut home health order authorization times from 21 days to two or three days at client health systems, according to the organization. Odek’s team has also built an automated billing workflow for related charges, unlocking new revenue streams for some clients. Within WellBeam, he leads hackathons and AI workshops, fostering problem-solving and creativity among employees.
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