Overview

Healthcare interoperability is complex and multi-faceted. With the explosion of digital health applications, it’s never been more critical. Much emphasis has been placed on the foundational and structural standards to improve communication between systems. While this is an essential aspect of healthcare technologies to master, we must ensure that the data can be understood and interpreted for meaningful and applicable use. As we standardize protocols and formats, standards around semantic interoperability will provide the next layer of infrastructure for robust healthcare data sharing and interoperability.

What is Semantic Interoperability?

Interoperability is the ability of two or more software systems to share and exchange information. In healthcare, that refers to communication between various systems that organizations, payers, governments, providers, and patients use. Semantics means relating to meaning in language and logic.

Semantic interoperability in healthcare occurs when software solutions and systems can communicate and understand the codified data through publicly available definitions, code sets, and translations. In simple terms, the software and technology should be able to interpret and understand what is being said by external systems, and it should provide meaning and value to the end-user

The Four Levels of Healthcare Interoperability

The Healthcare Information and Management Systems Society (HIMSS) Board created a multi-layered framework that breaks healthcare interoperability into four levels: foundational, structural, semantic, and organizational. Understanding the different levels is critical to achieving successful healthcare data integration and complete interoperability.

1. Foundational – Establishes the base interconnectivity requirements to share data between two or more systems. This layer can also be referred to as simple transport, as data can be sent and received through established secure communication channels. This level does not account for the interpretation and understanding of exchanged data.
2. Structural – Defines the data's format, syntax, and requirements down to field level. This layer sets unified standards across systems to make data formats consistent and predictable. External systems expect data types, require fields, and can easily interpret and understand the data. Some examples of structural definitions are HL7, FHIR, and C-CDA.
3. Semantic – Sets the requirement of interpretation and use of healthcare information between two systems. The semantic level uses models and codification through value sets and standardized definitions to create a unified way for systems to understand data. Procedures, diagnoses, billing codes, and demographic value sets are some examples of semantic meaning.
4. Organizational – The highest level of interoperability accounts for healthcare interoperability's security, governance, social, and legal considerations. Entities and individuals define policies, procedures, and consent to ensure the trusted exchange of health data.

A Real-Life Example

Let's say you are extremely busy and hard at work developing the next big digital health company. Your family has a 5-year-old daughter, and due to the time commitments, you enroll in an international program that provides Au Pairs from European countries. You agree to receive an Au Pair from Germany who will arrive by plane in one month and stay with you at your house for the following year. You will provide shelter and food and pay her $500 per month. She'll do everything from preparing meals, driving your daughter around, watching after her while you are working, helping her with homework, and communicating with the family.

Everything about the situation sounds excellent. You have an organization that provides access to an Au Pair and a good understanding of the foundation and structure of the arrangement (i.e., when and how the Au Pair will arrive, how long the Au Pair will be with you, what the service will cost you, and her general responsibilities). The one big problem is she speaks German and doesn't know English. The connection becomes much more complicated and substantially less valuable if you don't know how to speak German. It may even cost you more time and money than it saves you.

Why Semantic Interoperability So Important

Just like the example above, semantics is a huge part of healthcare interoperability. Without it, entire workflows and connections become useless. EMR or EHR Systems have proprietary ways of storing clinical and financial information. Computer systems do not employ the same process of interpretation as human beings. So, when sharing information between systems, it is critically important for the systems to transform data to have a shared semantic vocabulary. By ensuring accurate interpretation, we can induce meaningful use for an end-user.

Patient care is at risk when we omit semantic interoperability as a healthcare and EMR integration consideration. Medical history, prior conditions, and other critical pieces of data may be misrepresented or absent from a patient chart making providers and treatment plans less effective.

The number of software solutions and systems healthcare providers and entities use is increasing as the industry moves to value-based care, consumer and virtual-driven healthcare, and alternative care delivery and reimbursement models. Healthcare innovation is predicated on data sharing, which is only made possible with high semantic standardization.

Semantic standardization can happen more efficiently locally within an organization and its systems. Although there are technology and knowledge barriers, the translation and standardizations reside primarily under a single entity's control. Semantic interoperability becomes much more complex at a macro, inter-organizational level.

The Future of Semantic Interoperability

Healthcare technology is undergoing a seismic transformation, with technology and innovation growing at an alarming pace. The emphasis has been on improving the organizational level of interoperability through policy regulations to open data access. The release and adoption of HL7 FHIR modernize the foundational and structural levels. 

While the healthcare market still needs to improve overall implementations of FHIR R4B, ensuring semantics through standard vocabulary and terminology when exchanging health information between different systems and organizations now comes to the forefront.

In addition to standard codified fields such as problems and diagnosis, other free-text and string values will need to be normalized. This will require modern integration platforms to embed native reference functionality to normalize data and bring meaning to users across the healthcare continuum. 

The semantic level of healthcare interoperability seems like a logical and practical space where AI (Artificial Intelligence) could deliver benefits. An AI solution could learn and ensure accuracy when writing to shared vocabularies and extract information from free-text or string values. 

Semantic interoperability in healthcare refers to the ability of different software systems to exchange and interpret codified data through publicly available definitions, code sets, and translations. This ensures that data shared between systems is not only transferred but also understood and used meaningfully by external systems and end-users.

Semantic interoperability is one of four levels of healthcare interoperability defined by the HIMSS framework. Unlike foundational interoperability, which focuses on basic data exchange, and structural interoperability, which standardizes data formats and syntax, semantic interoperability ensures that exchanged data is interpreted correctly and meaningfully. The organizational level adds another layer by addressing governance, security, and policy considerations.

Semantic interoperability is crucial because it ensures that healthcare data is accurately interpreted and used across different systems. Without it, critical patient information such as medical history and prior conditions might be misrepresented or missing, compromising patient care and treatment plans. As the healthcare industry adopts more digital solutions, the need for a shared semantic vocabulary becomes increasingly vital for effective data sharing and innovation.

Achieving semantic interoperability faces several challenges, including variations in how systems implement standards, differences in terminologies and code sets, and the complexity of aligning data elements across systems. At a macro, inter-organizational level, these challenges become more pronounced, requiring significant effort in standardizing and translating data to ensure consistent interpretation and meaningful use.

The future of semantic interoperability in healthcare involves continued emphasis on standardizing vocabulary and terminology, particularly with the adoption of HL7 FHIR. Innovations like AI could play a significant role by learning and ensuring accuracy in shared vocabularies and extracting information from free-text or string values. As technology and policy advancements drive the healthcare industry’s digital transformation, achieving high semantic standardization will be crucial for effective data sharing and improved patient care.