Direk Limmathurotsakul: AMR: local, national, global impact
To fight antimicrobial resistance, researchers at MORU utilise hospital data to assess global impact and guide interventions. By analysing data, they identify hospitals needing support which enables targeted interventions. Automation and simplification aid data utilization in low-middle-income countries. This approach, bridging implementation and epidemiological research, is crucial and has the potential to save many lives.
My name is Direk Limmathurotsakul, I'm a Professor at the Mahidol Oxford Research Unit, Faculty of Tropical Medicine, Mahidol University. My area of research is in antimicrobial resistance, which is the phenomenon when bacteria cannot be cured by antibiotics.
I focus on using the data because the antimicrobial resistance is a big issue for the whole world. But we don't know how many people die of antimicrobial resistance in each hospital, in each country, and also globally. However, the data is in each hospital, so my work is to use that data, analyse that data and find a way that data has an impact to the hospital itself, to the country, and to the world.
The big question is how the policy makers can save lives from antimicrobial resistance, mainly in low-middle-income countries. We know that it's very bad, but without the data, we don't know how we should intervene, or how we should act. Hospitals need more support, but we don't know which hospital is doing good, or which hospital is doing bad. Everyone says that they are doing their best, but by utilising the data, we can answer this question, we can tell which hospital is doing good, and which hospital needs more support, and with that, we can pinpoint the intervention and support the hospital, the country, and then the global level can better action on that.
We work in a way where we can see the big picture of the hospital as a whole, of the country, and then the policy makers and the executive levels of the hospital can save many, many lives together with better policies. For example, just by reinforcing the hand hygiene of the health care workers in the hospital, it might be able to save at least 100 lives in one hospital over a year. Without knowing that, and leaving everything just as usual, that's bad. So, my work saves lives by producing the data, going to the right action, and then take it back to the patients themselves.
I think that in the low-middle-income countries, we have the data, but no one is using it. My program starts from the automation and finds a way to bypass or overcome a lot of barriers, for example, privacy of the data, we don't have to touch the data. We understand the politics, and we make it easy for each hospital so that they can utilise the data without the expert. I'm sure that many of us who are listening to this video at the moment, a proportion may be able to use the statistical programs, but for many, just Excel is the best that they can do. If they can use PIVOT, table in Excel, okay, that's amazing. That's it. We generate the system in a way that fits with low-middle-income countries, and we do research to understand the problem within that system so that it goes on to save more lives.
I think it's a system that supports implementation research and the data research and going to the epidemiology research. No one is doing that, and that's why I think that this is the best project that the funders should fund.
This interview was recorded in January 2024.