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Importance of Semantic Interoperability in Public Health Surveillance

Posted on July 26th, 2023   |   SSG

Semantic interoperability refers to the uninhibited exchange and interpretation of health information so all parties derive the same meaning from the data. Public health agencies need to use a health data management system with semantic interoperability to organize the information using a shared vocabulary. The program should automatically code the incoming data sets to create a uniform format with fewer communication errors.

Semantics help us infer meaning when communicating. The same idea applies to public health data exchange. Public health surveillance relies on the collection and analysis of patient information. However, the professionals diagnosing, treating, and monitoring patient symptoms often use different terms, systems, and applications to record and share this information. This process makes it difficult for public health agencies to collect the findings in a timely manner. After collecting the data, the agency must comprehend and interpret the information by coding it for analysis and evidence-based decision-making, adhering to the latest reporting guidelines.

The language of health is often complex and varied. It is full of localisms, synonyms, and terms that can have different meanings when applied to various specialties within the medical industry. Public health agencies can use a health data management program to automatically code and organize the data they collect using a shared, standardized vocabulary that allows for the unambiguous interpretation of information to help these organizations make informed and timely decisions.

Learn more about semantic interoperability and how it applies to public health surveillance.

What is semantic interoperability?

Semantic interoperability enables computer systems to exchange and interpret data with clear and consistent meaning. It ensures that all parties can derive the same meaning from the data.

Semantics is just one part of interoperability. It is defined as the free, uninhibited data exchange across disparate computer systems, programs, and devices. Semantic interoperability, however, is the free exchange of the meaning of data. It isn’t enough to make sure everyone has access to the data. They need to read and act on it in the same way. 

When applied to public health, the term refers to sharing and interpreting health information at the localized or state level. Public health administrators and workers must decipher the data they collect from organizations and facilities within their jurisdiction despite the lack of consensus among disparate providers and institutions. 

How can semantic interoperability be achieved?

Semantic interoperability is achieved by linking the data to a standardized vocabulary of medical terms, which is typically implemented at the national or international levels. The vocabulary should be recognizable to major institutions and government agencies, ensuring all users have a shared understanding of the data. This standardized vocabulary, commonly referred to as the terminology standard, facilitates data comprehension. The data is typically organized based on its level of detail, with lower-level detail data used for local or state-level decision-making, while higher-level detail data manages information at the patient level or on a case-by-case basis.

A standardized health data management software program automates the process of linking the data to the shared vocabulary or terminology standard. The terminology server receives and processes the data using the organization’s lexicon of terms. 

The original interface terminology may include patient symptoms, test results, or diagnostic information. The program will then cross reference these terms and words against the thesaurus to verify their meaning before uploading it to the database in the proper format. The original interface terminology turns into code that’s accessible and understandable to all users, also known as the terminology standard. The interface vocabulary can include both valid and invalid terms, including typos, to increase the platform’s recognition capacity while bridging the gap between the latest technological standards and how humans speak and write. If the server can’t interpret the text, it reverts to a manual review process so that a user with experience can classify the data and assign it appropriately. 

Public health surveillance systems should be compatible with a wide range of data types and sources, including written and spoken text, while compiling the terms into a single database using terms and inputs everyone can understand. The program should also be designed to deal with the consequences of implementing information and communication technologies, including typing and spelling errors that can lead to missing or incomplete fields. 

For example, a provider may enter “high blood pressure,” “HBP,” or “HB pressure” into their practice’s electronic health records (EHR) system. The terminology server will then code the entry as high blood pressure so that all entries are displayed in the same format with the same meaning. 

What are the benefits of semantic interoperability in healthcare?

Semantic interoperability in public healthcare increases access to health information within the agency while reducing the time it takes to code and decipher the data it collects. Workers do not need to know how to code terms or use a terminology server to create or use a health data management application for public health surveillance. The program automatically reclassifies and interprets the data the agency collects from various organizations and facilities to accelerate the reporting and decision-making process.

Organizing public health data using a shared vocabulary also reduces the impact of common provider and communication errors by either alerting the user to the mistake or by automatically eliminating the error by reclassifying the information in the proper format. 

By utilizing these tools, public health agencies can enhance their flexibility with the data they collect on the local population. They can spend more time and energy researching emerging trends or improving the social determinants of health by increasing or modifying the data it collects while making it easier for providers to comply with the new requirements by using technology designed to understand and recognize natural language patterns.  

Why is semantic interoperability important?

Semantic interoperability is crucial for the sharing and interpretation of health information. The standards were created to help public health agencies and organizations collect data using a shared set of terms, so every stakeholder will arrive at the same conclusion when looking at the results. 

Without semantic interoperability, each user may arrive at a different conclusion when looking at each piece of data, which makes it difficult for the agency to act on the information it collects. Not collecting or interpreting this data limits the government’s ability to respond to public health emergencies and improve social inequalities that affect health outcomes. 

Is semantic interoperability necessary for public health data exchange?

Yes, all public health departments and organizations need to use a data management system with semantic interoperability to successfully collect, analyze, and share health information. 

These standards are created and implemented by various standards development organizations (SDOs), including the Health Level Seven (HL7), which has become the standard for the healthcare industry in the U.S. 

Semantic interoperability continues to dominate the industry’s migration toward digital integration. Public health agencies can use technology to improve the way their system collects, identifies, and classifies incoming health information to improve user comprehension and reduce common communication errors. Contact SSG to learn more about our public health software and how it can benefit your organization.