Improved and Secure Medical Record Management

The healthcare industry is witnessing a data revolution. Electronic health records (EHRs) hold a treasure trove of patient information with the potential to revolutionize diagnosis, treatment strategies, and personalized medicine. However, sharing this sensitive data raises significant privacy concerns. This chapter explores a novel approach that leverages cutting-edge technologies like blockchain and federated learning to address this challenge. The proposed system facilitates secure and collaborative analysis of anonymized healthcare data across institutions. Federated learning allows hospitals to train machine learning models on their local, anonymized EHR data without revealing raw information. Blockchain technology then acts as a secure platform for aggregating these encrypted model parameters, fostering the creation of a robust global model for improved healthcare insights. This approach empowers collaborative AI development while safeguarding patient data confidentiality. This chapter also discusses the benefits and limitations of this system, including enhanced medical insights, improved patient care, and secure data exchange. It highlights future research directions to address challenges such as computational overhead and the need for standardized data formats and secure communication protocols. By overcoming these limitations, this model holds immense potential to revolutionize healthcare data analysis and pave the way for a future where secure and collaborative AI development leads to more effective and personalized medical solutions.