## 4 Data Silos and Fragmentation:
### Proposed Framework  
Precision medicine has emerged as a transformative approach to healthcare, tailoring medical treatments and interventions to individual patients based on their unique genetic, environmental, and lifestyle factors. However, the success of precision medicine relies heavily on accessing vast amounts of diverse patient data, which is often siloed within different healthcare institutions and electronic medical records (EMRs). To overcome these challenges and unlock revolutionary insights, we propose a novel framework that combines blockchain-enabled federated learning with EMRs. The potential of precision medicine to revolutionize healthcare is undeniable, but its implementation has been hindered by data access and privacy concerns. The conventional approach of aggregating patient data in a centralized database poses significant privacy risks and is often met with resistance from institutions. To address these challenges, our framework leverages blockchain technology to create a secure and decentralized platform for data sharing and federated learning. Federated learning allows multiple institutions to collaboratively train machine learning models while keeping their data localized. In our framework, we harness blockchain's immutability and transparency to establish a secure network where institutions can share model updates without compromising patient data. Each institution retains control over its data, and the federated model aggregates knowledge from all participants, fostering collective intelligence without centralizing sensitive information [25]. To ensure compliance and data governance, our framework utilizes smart contracts to enforce predefined rules for data access and sharing. Smart contracts enable the automatic execution of agreed-upon terms, ensuring that only authorized parties can access specific datasets for specific purposes. This approach empowers patients to have more control over their data, promoting transparency and building trust between patients, institutions, and researchers. Moreover, creating interoperability among diverse EMR systems is crucial for seamless data exchange. Our framework includes a standardized data format and communication protocol, enabling EMRs from various institutions to communicate efficiently and share relevant patient information securely. This interoperability not only improves data accessibility, but also enhances the overall quality of patient care. By enabling secure and privacy-preserving data sharing, our proposed framework unlocks vast datasets for medical research. Researchers can access diverse patient populations, leading to more comprehensive studies and breakthroughs in precision medicine. The federated learning approach also allows institutions to benefit from collective intelligence without compromising patient privacy, fostering a collaborative research ecosystem. The proposed framework that addresses the data challenges hindering the realization of precision medicine's full potential. By leveraging blockchain technology, federated learning, and interoperable EMRs, our proposed framework establishes a secure, privacy-preserving, and collaborative environment for data-driven healthcare research. Implementing this framework will pave the way for personalized, effective, and patient-centric precision medicine that promises to revolutionize healthcare on a global scale [26]. The proposed framework for the proposed framework operates through the following key steps:  
1. **Network Setup:** The framework begins by establishing a decentralized network using blockchain technology. Participating healthcare institutions join the network as nodes, each possessing locally stored electronic medical record (EMR) data [39]. 2. **Data Contribution:** Each healthcare institution securely contributes its EMR data to the network without exposing sensitive patient information. This is achieved through data anonymization techniques, such as differential privacy or secure multi-party computation, ensuring privacy preservation [27]. 3.