Design & development of customizable web API for interoperability of antimicrobial resistance data

Area of Science:

• Public Health
• Bioinformatics
• Health Informatics

Background:

• Antimicrobial resistance (AMR) poses a significant global health threat.
• Effective AMR control necessitates robust, cost-effective surveillance systems.
• Existing hospital Laboratory and Hospital Information Systems (LIS-HIS) generate valuable AMR data, but in varied formats, hindering analysis.

Purpose of the Study:

• To address the challenge of fragmented AMR data by developing an automated data integration solution.
• To facilitate the analysis of AMR data for predictive modeling and national reporting.
• To introduce i-DIA, a Python-based web API designed for seamless AMR data interoperability.

Main Methods:

• Design and development of i-DIA, a Python-based web API for automated data import from CSV files.
• Integration and testing of i-DIA with the Indian Council of Medical Research's (ICMR) AMR surveillance network (i-AMRSS).
• Utilizing i-DIA to import clinical data from LIS & HIS into the i-AMRSS platform.

Main Results:

• i-DIA successfully automates the import of bulk medical data from CSV files into a generic data management system.
• The i-DIA tool has been integrated and validated within the ICMR's AMR surveillance network, i-AMRSS.
• The system is currently collecting data from 31 laboratories across India, with i-DIA facilitating direct data import from hospital systems.

Conclusions:

• i-DIA provides a crucial solution for overcoming data interoperability challenges in AMR surveillance.
• The developed framework enhances the ability to analyze and report on AMR trends nationally.
• Future work includes extending i-DIA into a comprehensive web-based framework with peer-to-peer architecture for broader application.