Developing an Electronic Health Records System Based on the National Identity by Using Angular Web Application Framework

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Mustafa Mohammed Rasheed
Muneera Alsaedi
Ahmed Al Ibraheemi

Abstract

Electronic Health Records systems provide a better management system for patients and the healthcare spiciest by providing an accurate, up-to-date, and complete information about patients at the point of care. Further, enabling fast access to patient records for more coordinated, efficient care. The core objective for this study is to connect the national identity with the electronic records for patients. This aims to obtain a secure and accurate identification and authentication of patients and their information as well as will enable information exchange between different healthcare centers. This research presents the design and implementation of an electronic health records (EHR) system with national ID integration to cover the mentioned significant objectives.  The system utilizes the following technologies: Angular, HTML, CSS, and Bootstrap for the frontend and NestJS, Prisma, and JWT for the backend, with a Postgres database. The project follows the Agile software development methodology and includes system analysis, design, implementation, testing, and evaluation. The system features separate interfaces for administrators, doctors, and patients with appropriate table relations, endpoints, and schemas. The system was tested using Jest and test Bed for functional, performance, and security testing. The results demonstrate that the EHR system with national ID integration can improve patient record management and access. Limitations and future improvements are also discussed. Overall, this project provides a valuable contribution to the healthcare industry and offers a solid foundation for further development and refinement.

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How to Cite
Mustafa Mohammed Rasheed, Muneera Alsaedi, & Ahmed Al Ibraheemi. (2023). Developing an Electronic Health Records System Based on the National Identity by Using Angular Web Application Framework. Applied Data Science and Analysis, 2023, 102–107. https://doi.org/10.58496/ADSA/2023/008
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