Solving Data Quality Issues with Semantic Interoperability 

Updated on January 5, 2024

Healthcare organizations collect and process enormous amounts of information, ranging from structured data generated by machines (like laboratory tests and vital signs) to unstructured data provided by clinicians in the form of narrative clinical notes and patient interactions expressed in natural language. 

This collective healthcare data contains valuable insights into a patient’s medical history, health status, and treatments but, unfortunately, often lacks essential data quality (completeness, accuracy, consistency, structure, and conformity), making it hard to understand. Furthermore, when data is aggregated from multiple sources, it frequently contains duplications, which are not always obvious literal copies and can be references to the same information from different perspectives (and may contain contradictions). Such redundancies add a layer of complexity to data aggregation, presenting significant obstacles to leveraging the information effectively. 

Our industry must address these challenges to use healthcare data effectively. Semantic interoperability—the ability of healthcare systems to exchange data by mapping different terms to shared meanings—is the key to building a strong foundation for usable healthcare data. This can ultimately lead to improved quality of care and better patient outcomes.

Bridging the Gap with Semantic Interoperability 

Healthcare applications should never have to guess the meaning of a clinical note or concept, so speaking the same clinical data language is critical. However, a significant hurdle arises due to overlapping and diverse health specialties, standards, and code systems that 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. 

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), 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 on both ends. 

Rather than focusing solely on standardization, which may not always be feasible or desirable due to 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 right context.

Terms can be mapped and updated in real-time, ensuring that translations remain accurate and augmented with current content, like commonly used provider-friendly terms. Additionally, value sets, which maintain the integrity of essential concept groupings over time, can be created with the help of advanced semantic interoperability tools. This approach allows for various ways of processing and working with incoming data, surpassing the limitations of commonly used static lookup tables.

The 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, laboratories, diagnostic services, payers, patients, public health systems, and other entities. Key benefits include:

Usability: “Interoperability” sometimes exists only between two specific systems. If the source system is fairly unique—a lab information system, for example—it may have its data encoded in a particular manner. In this case, the connection only works between that system and the upstream provider. This can create difficulties when working with additional providers, as 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. Ultimately, it helps create a seamless network of data that becomes more usable across the healthcare ecosystem. 

Efficiency: The amount of data generated per patient will soon overwhelm clinicians. Making sense of all the information, determining what is relevant to a specific problem, and establishing meaningful connections to improve clinical outcomes are new challenges. Semantic interoperability increases the productivity and efficiency of healthcare professionals by making data more actionable and ready for use by technologies such as AI and ML algorithms.

Computability: A growing number of clinical decision support systems and AI tools are being integrated into EHRs or plugged into clinical workflows behind the scenes. Data must be computable and machine-interpretable for these innovative software tools to function correctly. Semantic interoperability ensures that data is served in the form these digital tools expect, enabling their results to become usable across a network of multiple EHRs and information systems. 

Real-World Use Cases

Clinical Decision Support Tools: Clinical decision support tools are bound to a rule system based on certain conditions to guide the user to a possible decision path. As a result, they rely on high-quality data to navigate complex medical conditions and suggest treatment options based on clear and codified healthcare information. As more clinicians rely on these tools, the challenge of effectively exchanging data becomes more profound. 

Semantic interoperability ensures clinical decision support tools have structured inputs with high data quality so relevant conditions are recognized and understood, enabling accurate decision support. Healthcare organizations can seamlessly integrate these new technologies and tools into their EHR systems, avoiding lengthy and complex data translation processes. The system automatically understands the data flow and starts reasoning, making the tool onboarding process more efficient. 

Building AI Models with Healthcare Data: Healthcare organizations increasingly use AI for clinical data analysis and precision medicine. However, AI models are only as good as the data they rely on, requiring structured, clean, reliable, and bias-free training data. 

The problem is that when the source data is disorganized, ​data scientists must spend most of their time preparing it for AI model training​. Clinical data is messy, EHR records are not standardized, and nobody uses the same codes. Before data scientists can build AI algorithms, they must create data pipelines that allow for data enrichment and augmentation, map text strings to standard codes, group and filter codes through value sets for efficient feature extraction, and prepare the results for model training. This is extremely time-consuming, and with the amount of training data needed, it becomes impractical to do manually.

Semantic interoperability is used to enrich and properly classify the data collected so that it feeds the AI development pipelines and dramatically simplifies the process by automating the data preparation.

Practical Steps for Adopting Semantic Interoperability 

Healthcare organizations should consider the following when preparing to adopt 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: Assess 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 develop partnerships to help you execute.

Conclusion

Semantic interoperability is not just a technical solution; it’s a fundamental shift in how healthcare data is managed and leveraged. It promises a future where quality care is the norm, and clinicians can trust external data included in longitudinal patient records and clinical research cohorts. 

The best value semantic has is when used in a complete interoperability suite, including other critical tools such as an integration engine and identity solutions like an enterprise master person index (EMPI). By adopting these solutions, healthcare organizations can build a strong 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. 

Evgueni Loukipoudis
Evgueni Loukipoudis
VP, Research & Emerging Technologies at Rhapsody

Evgueni Loukipoudis is VP, Research & Emerging Technologies for Rhapsody.