### Existing Interoperability Frameworks
They also implemented a distributed file system and two permission blockchain networks using the Interplanetary File System (IPFS) and Ethereum blockchain, respectively, to ensure transparency and security when sharing patients' medical files with various healthcare providers. Despite these promising features, the PbDinEHR framework exhibits certain limitations. Firstly, it lacks support for the right of erasure, a critical aspect defined in the General Data Protection Regulation (GDPR) that ensures privacy protection. Moreover, the frame work's security measures could be further enhanced by incorporating a robust encryption tool. Additionally, the level of user control provided by the framework is limited, and its scalability beyond the study's scope is questionable. To overcome these shortcomings, the current research proposes an alternative framework implementing progressive resistance against data breaches, employing dynamic data-masking techniques, and utilizing transparent database encryption. These measures are intended to address the identified limitations and strengthen the framework's overall effectiveness and security. The DEPLOYR interoperability framework, which was proposed by [21] at Stanford University (USA), offers a swift solution for deploying custom real-time machine learning models into EMRs. It serves as a technical tool to facilitate the seamless deployment and monitoring of researcher-created clinical ML models within widely used EMR systems. The framework aims to establish best practices for machine learning (ML) deployment and bridge the existing gap in model implementation. While this framework has been influential in shaping the current research, it has certain limitations that render it unsuitable for nationwide interoperability. Notably, it is tailored to integrate with Stanford Health Care's EMR vendor, necessitating further customization for institutions using different EMR systems. As a result, its replication in other locations is challenging. DEPLOYR relies on data from Stanford University's common data model for model training, which may not be feasible or applicable to all settings. Moreover, it supports the APIs of the common FHIR standard for interoperability to comply with U.S. regulatory mandates, but this might require upgrades to accommodate a broader range of clinical ML applications with diverse data modalities, creating additional challenges. Furthermore, the framework's applicability is constrained by its dependency on specific data models and EMR systems, demanding further customization and adaptation for use in different environments. These limitations need to be carefully considered when considering its implementation beyond its original context. Considering the above, the current research aims to propose an alternative framework that addresses the discussed limitations, making the framework more suitable for nationwide interoperability. An API-led integration framework was introduced by [22], aiming to improve the interoperability of patient health information amongst healthcare organizations. This framework is designed to maintain rigorous data privacy and security standards throughout its implementation. Central to its philosophy is the acknowledgment of the need for API integration to achieve the smooth and secure flow of data within the healthcare sector, facilitating the seamless exchange of data and functionality among various applications. The framework comprises a well-structured, three-tier architecture, with components that prioritize scalability, real-time capabilities, and orchestration. Emphasizing the potency of API-led connectivity, this framework has significant benefits, notably the reusability of the APIs, which contribute to the efficient development of its applications. In light of this, this research utilizes APIs to facilitate communication between disparate systems.