Leveraging Secure Medical Data to Combat Antimicrobial Resistance: A Data-Driven Approach

Antimicrobial resistance (AMR) poses a significant threat to public health, hindering the effective prevention and treatment of various infections and contributing to prolonged illness, disability, and mortality. This issue is particularly acute in countries like Bangladesh. The situation is exacerbated in this country due to ineffective healthcare practices, which include improper use of antibiotics, lack of awareness, an overburdened public health system, and limited access to quality healthcare services. To address this challenge, a web application aimed at digitizing the manual medical system prevalent in Jahangirnagar University medical center, Bangladesh, is proposed. The application focuses on tracking antibiotic usage for individual patients, thereby facilitating better management of antimicrobial treatments. A dataset on antimicrobial resistance (AMR) was generated through a testing campaign conducted at Jahangirnagar University. Subsequent machine learning and statistical analyses were conducted to identify significant patterns aimed at mitigating the impact of AMR. Given the sensitive nature of medical data, ensuring its security is paramount. Therefore, a robust encryption algorithm has been integrated into the system to safeguard medical data in transit and at rest. The proposed encryption algorithm is a hybrid model combining two strong algorithms, CBC (Cipher Block Chaining), and ECDH (Elliptic Curve Diffie-Hellman). Through the implementation of this encryption algorithm, the web application promises to significantly improve public health outcomes and enhance the security of medical data in Bangladesh, addressing the critical issue of antimicrobial resistance while ensuring patient confidentiality and data integrity.