Optimising surveillance and treatment of infectious diseases using AI and Big Data

Infections pose a major risk to health globally. Antimicrobial resistance (AMR) threatens effective treatment of infection and healthcare associated infections (HCAIs) impact more than 10% of all hospital patients.

Advances in data availability and new artificial intelligence (AI) methods offer the chance to develop:
 

• More responsive, comprehensive, and automated HCAI/AMR surveillance generating better breadth and depth of intelligence to drive action and changes in practice to protect diverse populations at local, regional, and national levels.
• Predictive tools to improve care of individual patients and combat AMR.
• Methods, infrastructure and skills to optimally use rapidly-evolving electronic healthcare record and patient-contributed data, and emerging AI technologies. 

Several possible projects are available, including:
 

• Developing/testing automated electronic surveillance approaches for rapidly detecting changes in infections and identifying at-risk populations; and deploying these tools in hospitals and national systems
• Extending and piloting in hospitals predictions of personal AMR risk to optimise infection treatment, prevention and control, developing generalisable methods that can update over time/to new locations, and approaches for safely implementing them
• Pre-emptive surveillance, investigating which metrics of hospital processes (e.g. isolation/screening/diagnostic use/cleaning) are associated with HCAI/AMR to inform prevention