An Approach for Refactoring System Healthcare Using CQRS, GoF, and Natural Language Processing

We presents a comprehensive approach for refactoring healthcare systems using natural language processing (NLP), Command Query Responsibility Segregation (CQRS), and Gang of four (GoF) design patterns. The proposed method aims to improve performance, scalability, and maintainability without altering the system’s external behavior. It encompasses three main phases: analysis, design, and implementation. The analysis phase identifies pain points and utilizes NLP to extract valuable information from the existing system. In the design phase, CQRS and GoF patterns are employed to model the new healthcare system. Finally, the implementation phase involves refactoring the legacy codebase. The approach is validated through a case study, demonstrating enhanced scalability, performance, and maintainability, with potential applications across various domains.