## 2 The TASIPPS Conceptual Framework
### The TASIPPS Framework Components
#### 2.1.2 The Semantic Interoperability Module
Each EHR, when initiating a connection or data request through the central interface, undergoes a strict authentication process. The interface prompts the user to input secure credentials, validating both their identity and authorization status. Simultaneously, the interface ensures the legitimacy of the originating system, safeguarding against any malicious system or user activities. This two-fold authentication mechanism guarantees the integrity of data exchanges, providing an additional layer of security at the point at which a connection is initiated. As stated earlier, the framework employs the AES256 encryption method. In essence, the framework not only secures the data during transmission but also rigorously authenticates and authorizes every connection attempt, enhancing the overall security of the interoperability ecosystem. How Semantic Interoperability Is Achieved  
The semantic search process begins with the retrieval of vectorized data from a specialized database. This database contains vectorized representations of diseases and symptoms from each of the connected prototype systems. The vector index dimension of 1536 was used in this work. The vectorization process involves converting raw text data into numerical vectors using techniques like word embedding, which captures the semantic meaning of the text. In this work, the open-source AI model OpenAI's text-embedding-3-small was utilized to generate these vector embeddings. This model was chosen for its ability to produce high-quality, semantically rich vector representations at a lower cost. The text embedding created by this model is stored in a vector database, such as PG Vector, which facilitates efficient mathematical operations and comparisons during the search process. The choice of model and vector index length is optimized to enhance the relevance of the search results. Longer vector indexes, such as 1536 (as used in this work), provide a more detailed representation of the text and are associated with increased relevance in search outcomes. Figure 4 captures the various steps involved in the vectorization used in this work. By selecting appropriate models and vector index lengths, the system can improve the accuracy and effectiveness of the semantic search process. The primary advantage of using a vectorized database over traditional ontology-based approaches lies in its ability to capture complex semantic relationships between medical terms without the need for exhaustive manual curation. Ontologies require extensive effort  
Figure 4: Vectorization flow (semantic search process). to create and maintain, as they depend on predefined relationships and categories, which can be limiting and are prone to becoming outdated. In contrast, vectorized databases automatically learn and represent the semantic nuances of medical data through training on large datasets. This not only reduces the workload associated with manual updates but also enhances the flexibility and scalability of the system. By embedding the medical terminologies and their descriptions into a vector space, the system can leverage elastic semantic search capabilities to understand and retrieve relevant medical records based on the context of the search queries. This approach ensures more accurate and contextually relevant search results, ultimately improving the interoperability and usability of EHRs across different healthcare platforms. When a requesting hospital initiates a search for a patient's medical history from the interoperability platform, it triggers the semantic search process. The requesting hospital (Hospital A) seeks to view the patient's medical history, which is stored in another hospital's (Hospital B) system. To facilitate this, the semantic search mechanism converts the patient's diagnosis and other relevant medical information from Hospital B's terminology into a format or terminology that Hospital A's system can understand and work with. Upon receiving the search request, the system first takes the search query and converts it into its own vector representation.