Syllabus: GS2/Health
Context
- IIIT-Delhi and Indian Council of Medical Research (ICMR) researchers developed AMRSense, an AI tool that analyzes hospital data for real-time antibiotic resistance insights.
About
- This initiative aims to enhance AMR surveillance at the global, national, and hospital levels.
- AMRSense leverages hospital-generated culture sensitivity test reports (blood, sputum, urine, pus, etc.) to construct AI-based pipelines, enhancing antimicrobial stewardship.
| What is Antimicrobial Resistance? – Antimicrobial Resistance (AMR) occurs when bacteria, viruses, fungi and parasites change over time and no longer respond to medicines making infections harder to treat and increasing the risk of disease spread, severe illness and death. – Antibiotic resistance is emerging as the threat to successful treatment of infectious diseases, organ transplantation, cancer chemotherapy and major surgeries. Causes for Antimicrobial Resistance – Overuse and Misuse of Antibiotics: The excessive and inappropriate use of antibiotics in humans and animals is a major driver of antimicrobial resistance. 1. A survey on prescribing trends for antibiotics released by the National Centre for Disease Control (NCDC) in 2023 found that 71.9% of patients coming to hospitals were prescribed antibiotics on average. – Inadequate Dosage and Duration: When antibiotics are not taken in the correct dosage and for the recommended duration, it can lead to incomplete eradication of the targeted microorganisms, allowing the surviving bacteria to develop resistance. – Self-Medication: Self-prescription without proper medical guidance contributes to the misuse of antibiotics. – Antibiotics in Food-Animals: Use of antibiotics as growth promoters in food animals and poultry is a common practice and later it evolves in the food chain. – Poor Sanitation: A significant proportion of sewage is discharged untreated into water bodies, leading to severe river contamination with antibiotic residues and antibiotic-resistant organisms. |
Role of AI in Combating AMR
- Early Outbreak Detection: AI-powered tools analyze large-scale hospital data to track emerging AMR trends, enabling proactive interventions.
- AMRSense predicts resistance patterns using routine hospital records, aiding in faster decision-making.
- Integration of Data: AI can combine hospital AMR data with antibiotic sales records, agricultural antibiotic use, and environmental factors to offer a holistic approach to AMR surveillance and control.
- Overcoming Limitations: Unlike genomic sequencing, which is expensive and time-consuming, AI models use routine hospital data to generate cost-effective, actionable insights.
Challenges
- Data Quality: The accuracy of AI models heavily depends on the quality and completeness of available data, which can be a significant challenge in healthcare settings.
- Model Accuracy & Validation: AI predictions depend on past trends; unexpected events (e.g., pandemics) can disrupt accuracy.
- Implementation: Hospitals and policymakers face challenges in adopting AI-based AMR strategies due to regulatory, ethical, and technical barriers.
Concluding remarks
- AI plays a pivotal role in AMR surveillance, prediction, and stewardship, transforming public health responses.
- With proper integration of AI-driven tools, evidence-based policies can help combat the rising threat of antimicrobial resistance effectively.
Source: TH
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