Semantic interoperability—the ability of healthcare systems to exchange data by mapping diverse terms to shared meanings—is the key to building a stronger foundation for usable healthcare data. This can ultimately lead to improved quality of care and better patient outcomes.
Speaking the same clinical data language is critical. However, this is challenging because overlapping and diverse health specialties, standards, and code systems create many different languages that coexist. A lot of structure must be in place before machines can correctly interpret healthcare data. Mapping one-to-one equivalences between diverse terminologies is often impractical and requires a more specific approach.
Semantic interoperability, powered by AI and machine learning (ML), serves as the bridge between data consumers (healthcare systems, applications, and decision support tools) and data producers (clinicians and other healthcare professionals). It focuses on the meaning of the conveyed information and its context. It establishes a common framework for healthcare data interpretation, ensuring that information is transmitted and comprehended accurately.
Rather than focusing solely on standardization, semantic interoperability emphasizes the constant transformation and adaptation of conversations that occur within systems and between humans. It helps manage the multitude of clinical terminologies, facilitates data exchange across various specialties and domains, and enables data to be understood in the proper context.
Benefits of Semantic Interoperability
Semantic interoperability makes healthcare data meaningful to software applications and ensures it is usable by both downstream systems and humans. It facilitates such interactions in the context of diverse use cases, enabling data exchange that conveys relevant meaning and concepts. The technology simultaneously promotes standardization and interoperability and streamlines data exchange between healthcare providers, diagnostic services, laboratories, payers, public health systems, patients, and other entities. Key benefits include:
Usability: “Interoperability” may only exist between two specific systems. If the source system is unique— for example, a lab information system—the connection only works between that system and the upstream provider. This creates difficulties when working with additional providers because the process must be repeated with each unique system.
Using semantic interoperability and mapping data to a national standard like LOINC makes connecting to multiple upstream providers easier and helps create a seamless network of data that becomes more usable across the healthcare ecosystem.
Efficiency: Semantic interoperability increases the productivity and efficiency of healthcare professionals by making data more actionable and ready for use by AI and ML tools.
Computability: Data must be computable and machine-interpretable for the innovative new software tools to function correctly. Semantic interoperability ensures data is served in the form these digital tools expect, enabling results to become usable across a network of multiple EHRs and information systems.
Practical Steps for Adopting Semantic Interoperability
- Assess Standard Terminologies: Evaluate the terminologies currently in use and understand how proprietary data is employed in place of standard codes. Develop a strategy for the effective use of codes across the organization.
- Evaluate Data Usability: Determine how easily other healthcare facilities, systems, and applications can use your data. Identify your most valuable use cases and any barriers to data exchange.
- Identify Tools for Improving Data Usability: Develop a practical approach to overcome the barriers to extracting value from data. Familiarize yourself with the existing tools that help solve your challenges – from reducing the impact of duplicates to automation for improving staff productivity to applying technologies such as AI and ML.
- Look at the Complete Interoperability Picture: Understand the role semantic interoperability plays in your complete data interoperability picture, centered around use cases and goals of extracting value from data and improving outcomes, efficiency, and productivity. Develop an interoperability strategy that aligns with business goals and partnerships to help you execute.
Conclusion
Semantic interoperability is a fundamental shift in managing and leveraging healthcare data. The best value semantic has is when used in a complete interoperability suite, including other critical tools such as an integration engine and an enterprise master person index (EMPI). By adopting these solutions, healthcare organizations can build a stronger foundation for managing data effectively, applying AI and ML for better decision-making, and ensuring that critical healthcare systems speak the same clinical data language.
