SmartABHub VietUSyd Initiative

The University of Sydney

SmartABHub VietUSyd Initiative

We integrate policy reform, rapid on-site quality assurance and AI-driven supply chain management to ensure timely access to quality antibiotics.

The University of Sydney Sydney, New South Wales 2006

AUS

Nonprofit

Challenge: Reducing Substandard and Falsified Antibiotics: Substandard and falsified (SF) antibiotics are a critical public health threat, particularly in low- and middle-income countries (LMICs), where weak regulatory frameworks and supply chain vulnerabilities enable their spread. The WHO estimates that 1 in 10 medical products in LMICs is substandard or falsified, with oral antibiotics being among the most affected. This costs $30.5B for SF in LMICs. This leads to treatment failures, antimicrobial resistance, and preventable deaths, up to 170,000 children dying annually from pneumonia due to ineffective antibiotics. In LMICs, up to 50% of antibiotics fail quality standards, costing the global economy $200B annually. In Vietnam, over 20% of antibiotics sampled from pharmacies were found to be substandard, due to poor storage conditions, improper handling, or counterfeiting. This problem increases in Vietnam’s rural pharmacies, where 40% of antibiotics sold are substandard or falsified. Key factors driving this crisis include inadequate regulatory oversight, lack of rapid quality verification, gaps in supply chain monitoring, and unreliable demand forecasting. Without urgent innovation to detect and mitigate SF antibiotics, these challenges will continue to compromise public health, accelerate AMR, and burden fragile health systems in Vietnam and many LMICs.

Our solution tackles the urgent challenge of substandard and falsified (SF) oral antibiotics by integrating policy reforms, rapid diagnostic technology, and AI-driven supply chain management to strengthen antibiotic quality assurance and improve access at the community level in Vietnam and beyond. Our rapid on-site quality assurance tool to identify SF antibiotics per pharmacopoeia standards (https://www.youtube.com/watch?v=fkt9w585Bao). This approach enables community pharmacists and regulators to conduct on-site testing, ensuring antibiotics meet quality standards before reaching patients. This empowers healthcare workers to provide quality medicines to enhance patient safety. This solution also leverages AI-driven-supply-chain management to optimise demand forecasting, inventory management, and distribution. By analysing historical sales, seasonal trends, prescription patterns, and disease outbreaks. Our AI models ensure the timely availability of high-quality antibiotics, reducing shortages and oversupply. Improving data accessibility provides policymakers and healthcare professionals with the necessary evidence to make informed, data-driven decisions. By combining the innovative technology with policy interventions, Large Language Model will be leveraged to streamline policy reform integration in healthcare by automating policy analysis, compliance checking, and decision-making processes. This approach strengthens regulatory oversight, reduces the prevalence of SF antibiotics use, and improves equitable access to high-quality treatments.

The public in Vietnam often struggles to access quality-assured antibiotics when they are most needed, leading to treatment failures and AMR. Our solution addresses this by enabling real-time quality verification and robust supply chain monitoring, ensuring timely access to safe, effective antibiotics for vulnerable populations, including children, the elderly, and critically ill patients. Community pharmacists in Vietnam lack rapid, affordable tools to verify antibiotic quality in real-time, data-driven systems for demand forecasting and inventory management. This leaves patients vulnerable to SF antibiotics, while pharmacists face risks to their practice and public trust. Our solution addresses these gaps by equipping pharmacists with on-site quality evaluation tools and AI-driven supply chain management. This enables immediate verification of antibiotic authenticity, optimises stock levels, reduces the circulation of SF antibiotics, and ensures patients receive safe, effective treatment. Policymakers in Vietnam lack timely, actionable data to guide antibiotic regulation and procurement. Our solution uses AI-driven forecasting, rapid quality assurance, and Large Language Model (LLM)-based monitoring to inform public health policies, strengthen regulatory frameworks, and prevent the spread of SF antibiotics. Prescribers will have more confidence in prescribing effective antibiotics, knowing that SF antibiotics have been removed from the market for better patient outcomes.