Orthopaedic Patient Data Management and Analysis Using Clinical Registries

Conventional orthopaedics patient data management are often fragmented across multiple platforms which adversely impact effective interoperability, data storage, retrieval, and clinical decision-making. The goal of this study is to design and implement an integrated and extensible Orthopaedic Patient Data Management and Analysis System using structured clinical registries that improve clinical workflows, patient care, and research readiness.

A web-based clinical registry system was developed using MERN (MongoDB, Express.js, React, Node.js) stack. The system supports configurable orthopaedic-specific clinical registries, flexible data entry full CRUD (Create, Read, Update, Delete) operations, role-based access control for data security, and multi-criteria filtering included for quick patient searches. Future versions will include data visualization tools and predictive analytics for better decision-making.

The implemented system provides improved data accessibility, reduces task duplication, and improves and enhances structured record keeping across orthopaedic subspecialties with multi-criteria filtering algorithms. Although advanced analytics and visualization are not currently deployed, the system architecture supports future integration of AI-driven predictive models and Power BI dashboards.

The developed Orthopaedic Patient Data Management and Analysis System is robust, secure, scalable, and extensible, enhancing administrative efficiency and enabling longitudinal outcome tracking. It also contributes to medical research via outcome tracking and trend analysis. The system acts as a fundamental step toward intelligent data-driven orthopaedic care through future predictive analytics and clinical decision support.