Healthcare organizations collect and process vast amounts of information, ranging from structured data generated by machines like laboratory tests and vital signs to unstructured data, often provided by clinicians in narrative clinical notes, patient interactions, and self-reported patient outcomes, expressed in natural language.
This healthcare data contains valuable insights into a patient's health status, medical history, and treatments if the data makes sense to the person viewing it. Often, though, it lacks essential data quality (completeness, accuracy, structure, conformity, and consistency). Furthermore, when data is aggregated from multiple sources, it frequently contains duplications.
These duplications are not always obvious literal copies and can be references to the same information from different standpoints and may even contain contradictions. Data quality and usability are still challenges, and advanced interoperability solutions can help organizations derive value from their information stores.
To use healthcare data effectively, the industry must address these challenges. Semantic interoperability — the ability of healthcare systems to exchange data by way of mapping different terms to shared meanings — is the key to building a solid foundation for usable healthcare data. This can ultimately lead to improved quality of care and better patient outcomes.
Because clinical decisions can mean life or death, healthcare applications should not guess the meaning of a clinical note or concept, so speaking the same clinical data language is critical. However, a significant hurdle arises because overlapping and diverse health specialties, code systems, and standards create a multitude of different languages that coexist. A lot of structures must be in place before machines can correctly interpret healthcare data.
Mapping one-to-one equivalences between different terminologies is often impractical. Instead, it requires a more specific approach, which may involve adding terminology constructs or tools. As healthcare organizations connect to more information sources, the need for efficient data standardization and seamless integration has never been more critical. While standards like Fast Healthcare Interoperability Resources (FHIR), RESTful APIs, and terminologies like SNOMED and LOINC are advancing interoperability, the industry needs more.
For example, the need for more guidance on code usage and terminology overlap in standards like the USCDI creates obstacles. Terminologies like SNOMED may express the same clinical meaning in different ways, leading to disparities. This poses challenges for software applications that cannot recognize the many options for expressing the same notion and creating mappings and value sets may require expertise in clinical terminologies.
To address this, healthcare organizations must adopt more sophisticated tools to keep up with the evolving nature of healthcare terminologies. Semantic interoperability, powered by AI and machine learning (ML), solves these challenges. It serves as the bridge between data producers (clinicians and other healthcare professionals) and data consumers (healthcare systems, applications, and decision support tools) and establishes a common framework for healthcare data interpretation, ensuring that information is not just transmitted but also comprehended.
Rather than focusing solely on standardization, which may not always be feasible or desirable because of the diversity of healthcare specialties and use cases, semantic interoperability emphasizes the constant transformation and adaptation of conversations that occur within systems and between humans. It is about managing the multitude of clinical terminologies, facilitating data exchange across various specialties and domains, and enabling data to be understood in the proper context.
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