Antimicrobial resistance is widely recognised as one of the biggest long-term threats to modern healthcare. What gets far less attention is the information needed to understand and respond to it.
Every day, hospitals, pathology laboratories, aged care providers, pharmacies, general practices, researchers and government agencies generate data that contributes to our understanding of antimicrobial use and resistance. The challenge is not a lack of information. The challenge is understanding how that information is collected, where it flows, how it is used, and where opportunities exist to improve it.
Several years ago, I had the opportunity to work with CSIRO's Australian e-Health Research Centre on a project that set out to answer precisely those questions. The result was one of the first comprehensive reviews of Australia's human health antimicrobial use and resistance data landscape: a national view of the programs, organisations, data flows and information assets that contribute to antimicrobial surveillance across Australia.
Most healthcare challenges are not caused by a lack of data. They are caused by fragmented data.
The challenge of fragmented data
Australia has invested heavily in antimicrobial surveillance over many years, and numerous programs contribute valuable insights into use and resistance patterns. The problem is that they were established at different times, for different purposes and by different organisations. Each collects different information; reporting cycles vary, terminologies differ, and data exchange methods vary.
Viewed individually, each program makes sense. Viewed collectively, the landscape becomes far more complex. One of the most important outcomes of the project was demonstrating that antimicrobial surveillance is not a single system. It is an ecosystem.
Making the invisible visible
A key contribution of the project was a framework mapping how antimicrobial data flows through the Australian health system, examining relationships between data producers, data consumers and reporting organisations rather than looking at programs in isolation.
The resulting models showed that substantial volumes of antimicrobial data are already collected throughout Australia, that many organisations collect similar information for different purposes, and that significant opportunities exist to reduce duplication and improve consistency through better standards, interoperability and governance.
Why this work matters
The project's relevance reaches well past antimicrobial resistance. It looked at how health information moves through a complex national system, and the same problems show up in aged care, public health surveillance, genomics, chronic disease management and clinical quality reporting: inconsistent standards, mismatched terminology, and information architecture that hasn't kept pace.
I still work from the same principle: understand the ecosystem before designing new systems, understand what already exists before creating new data collections, and understand the information flows before investing in technology to support them.
Conclusion
Antimicrobial resistance is a growing problem for Australia and the rest of the world, and responding to it well depends on timely, high quality information and the ability to connect insights across clinical care, public health, research and policy.
The project's real value wasn't the landscape review itself. It was proof that information architecture isn't just a theoretical exercise: done well, it connects data to decisions, decisions to action, and action to results people can see.
Read more about the report at CSIRO's Australian e-Health Research Centre.