OPTIMISING SURVEILLANCE

Theme lead: Professor David Eyre (University of Oxford) 

Theme co-lead: Dr Russell Hope (UKHSA)

Tracking Infections and Superbugs

We’re working on new ways to track, understand, and stop infections that happen in hospitals and infections with antibiotic-resistant bacteria. Here is how:

Smarter Ways to Track Infections

Using Health Records to Spot Infections

We are testing whether hospital records can help us automatically track infections, without needing someone to check everything manually. This includes using text searches and AI tools to read notes from doctors and nurses, and linking hospital and GP records.

Tracking Lab-Confirmed Infections

We’re combining lab results with hospital records to automatically report infections that have been confirmed by tests. The aim is to make national reporting of infections easier for hospitals and more accurate.

Tools for Infection Prevention and Control Teams

We’re testing a tool that helps hospital infection control teams see how infections might have spread between patients, using information on contacts between hospital patients and information from sequencing the genetic code or DNA of the bacteria causing infections. We want to test if this helps work out how infections are spreading and if it helps stop the spread of infections.

Talking to the Public and Health Workers

We want to make sure data collected on infections is useful for healthcare workers and the public - and it is presented in ways that are clear to understand. We’ll run workshops with the public and health staff to find the best ways to show and share information. This includes creating better ways to make comparisons between hospitals and ensuring comparisons are fair.

Tracking How Severe Infections Are

We’re also exploring how hospital records can be used to monitor how serious infections, as well as counting how common they are.

Making Sure Tracking is Fair

Different hospitals and patients are not all the same. We want to develop new ways to adjust how we track infections to account for things like population size, testing differences, and types of patients, so the results are fairer and more accurate.

Who’s Most at Risk?

Finding Risk Factors

We will use large national datasets to look at who gets hospital infections or antibiotic-resistant infections. We’ll look at things like where people live, their health needs, and whether they face health inequalities.

Targeting the Right Groups

We will also look at how to use this information to decide which patients or groups need help the most, by linking risk factors with how bad the outcomes are. This will help plan interventions and support to tackle infections.

Using Antibiotics Wisely

Predicting the Best Antibiotics for Each Patient

We are building new AI tools that can predict whether a specific person is at risk from antibiotic-resistant infections. This helps doctors choose the right antibiotics, especially for serious infections like those causing sepsis.

Antibiotic Budgets

We are developing a new idea called an “antibiotic budget.” This helps measure how well hospitals and GPs are using antibiotics based on resistance levels and the types of patients they see.

Antimicrobial Resistance (AMR) Footprints

We are also creating new measures (called “footprints”) that show how a hospital or GP practice is using antibiotics and how this might be contributing to future AMR in future.

Predicting Who is at Risk of Harm to Guide Antibiotic Treatments

We are also trying to predict not just who is at risk of AMR, but who might be harmed if antibiotics don’t work. We want to add this to the AI tools we are developing that help doctors decide which antibiotics to use.