- Research Collaboration:
- Conducted by IIIT-Delhi, CHRI-PATH, Tata 1mg, and Indian Council of Medical Research (ICMR).
- Focus on developing AI-driven tools for antimicrobial resistance (AMR) surveillance.
Relevance : GS 2(Health ) , GS 3(Technology)
- Key Tool Developed – AMRSense:
- Utilizes routine hospital data (blood, sputum, urine cultures) for real-time AMR insights.
- Provides global, national, and hospital-level AMR trends.
- Cost-effective alternative to expensive genomic approaches.
- Findings from Six-Year Study (Published in The Lancet Regional Health – Southeast Asia):
- Analyzed data from 21 tertiary care centers under ICMR’s AMR surveillance network.
- Identified directional relationships between antibiotic pairs and resistance patterns.
- Rising resistance to one antibiotic can predict increased resistance to another over time.
- Innovations in AMR Surveillance:
- AMROrbit Scorecard:
- Visualizes hospital/department resistance trends against global medians and rates.
- Facilitates timely interventions by showing ideal resistance quadrants (low baseline, low rate of change).
- Awarded at the 2024 AMR Surveillance Data Challenge.
- AMROrbit Scorecard:
- AI’s Role in Public Health and Clinical Settings:
- Enhances antimicrobial stewardship through data-driven decisions.
- Compares AMR rates across hospitals, cities, and departments.
- Augments traditional surveillance systems with real-time data visualizations.
- Challenges & Limitations:
- AI models rely on consistent, digital surveillance data; limited in data-deficient regions.
- Environmental factors (e.g., antibiotic use in poultry, soil contamination) also influence AMR but are not fully integrated yet.
- Future Directions:
- Plan to integrate hospital data with antibiotic sales and environmental data for comprehensive AMR analysis.
- Aim to improve public health decision-making and policy formulation through expanded data integration.
- Reliability of Models:
- Models validated against historical data show accuracy in detecting AMR trends.
- Global studies confirm the increasing rate of AMR captured by the AI models.
This development aligns with global health goals to combat antimicrobial resistance through timely data-driven interventions and improved public health strategies.
