## 5 Problem Statement  
The utilization of electronic medical records (EMRs) for medical research and collaborative learning poses significant challenges related to data privacy, security, and the centralized nature of traditional data sharing models. Though EMRs hold immense potential for unlocking valuable medical insights and advancing patient care, the current approaches to sharing and analyzing these records are limited in their ability to address these critical concerns [30]. Data Privacy:Protecting patient privacy is of utmost importance when working with EMRs. The sensitive nature of medical data necessitates robust privacy measures to prevent unauthorized access and ensure patient confidentiality. Traditional centralized data sharing models raise concerns about data breaches, unauthorized use, and the potential for misuse of personal information [31]. 2. Security: The security of EMRs is another pressing concern. As the volume and value of medical data increase, so does the potential for cyberattacks and data breaches. Centralized storage and traditional data sharing models create attractive targets for malicious actors, putting patient data and the integrity of medical research at risk [32]. 3. Lack of Trust: In the realm of collaborative medical research, establishing trust among multiple stakeholders is crucial. Institutions and researchers may be reluctant to share their data due to concerns about data ownership, control, and the potential misuse of their contributions. This lack of trust hampers the ability to pool resources, collaborate effectively, and leverage the collective intelligence of diverse medical datasets [33]. 4. Limited Data Accessibility: Current data sharing models often face legal and regulatory hurdles that impede the seamless exchange of EMR data. Fragmented and siloed datasets hinder the ability to conduct comprehensive research and derive meaningful insights. Additionally, the complex and time-consuming process of obtaining permissions and navigating legal frameworks further hampers data accessibility and impedes progress in medical research [34]. Electronic medical records (EMRs) have revolutionized the healthcare industry by streamlining patient data management and facilitating efficient care delivery. However, the full potential of EMRs can only be harnessed through effective data sharing and analysis. The problem statement acknowledges the critical challenges in this realm, but would greatly benefit from the inclusion of specific statistics and illustrative examples to provide a more comprehensive understanding of the magnitude of these challenges. This article aims to address this need by delving deeper into the specific issues faced with traditional approaches to EMR data sharing and analysis. 1. The Challenge of Data Interoperability: Specific Statistics: According to a survey conducted by the Office of the National Coordinator for Health Information Technology (ONC), as of the last reporting period, only 46% of hospitals in the United States had achieved basic EMR data interoperability. This indicates that a significant portion of healthcare providers still face challenges in sharing patient data seamlessly across different systems. Illustrative Example: Consider a scenario where a patient with a complex medical history is referred from a primary care physician to a specialist at a different healthcare facility. The absence of data interoperability may lead to delayed diagnosis and treatment decisions as the specialist struggles to access critical information from the patient's EMR, resulting in potential adverse health outcomes [35]. 2. Privacy and Security Concerns: Specific Statistics: The Ponemon Institute's annual study on healthcare data breaches revealed that in the last year, 68% of healthcare organizations experienced a data breach, and 58% of those breaches involved unauthorized access or disclosure of patient data.