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Government figures show that antimicrobial resistance continues to rise in the UK. One in fifteen patients admitted to hospital in the UK picks up an infection whilst in care. These so-called hospital acquired infections are a huge problem, causing patient suffering and costing the NHS a £1000,000,000 per year.

20% Preventable Infections

Most of the hospital stock in the UK dates from the 20th century with patients in multi-bed wards. Single bed rooms are in limited supply and cleaning standards are not easily evaluated. Conservatively, it is thought that 20% of infections are preventable through improved engineering design.

Increased Antimicrobial Resistance

It has been 30 years since the last class of antibiotics was discovered. Antimicrobial resistance is recognised by a £10 million UK challenge prize problem and a global problem. We propose that the patient's environment should be the first line of defense against infection.

Sub-Optimal Environment

Natural ventilation is the preferred method of creating a good indoor environment in the UK, but patients and healthcare workers are often too hot or too cold. This often happens in quick succession where the staff are often too hot when the patients are too cold. So windows are closed and air quality goes down. We propose that we can predict these changes and prevent them from happening, whilst keeping infection prevention at the centre of the project.

What We Will Do

Characterise Dynamic Hospital Environments

We will monitor the IAQ and take air and surface microbial samples from patient rooms at St James' University Hospital, Leeds and Hairmyres Hospital in Lanarkshire intensively and make follow-up visits. This will tell us about the relationship between the air and surfaces and whether simple sampling techniques can be used to test cleanliness standards. This will also tells us how the microbiome changes over time and how it spreads from one patient's room to another's. We will observed patient care and make computer models to make realistic representations of human behaviour. These will be used to predict risk of infection during care procedures and help optimise cleaning routines.

Predict IAQ

We will use high-speed computational fluid dynamics simulations of temperature and airflow patterns to compare against measured values. These will then run ahead in time and predict changes in patient comfort and increased risk of infection from high concentrations of airborne bacteria. These will be calibrated against experiment and fine-tuned. A prototype model will be built to act along side building management systems.

Implement And Optimise

We will make software to look at "what-if" scenarios of infection risk, hand hygiene, cleaning and patient comfort. This will tell help streamline the process of choosing where to locate newly admitted patients, which items of infection control care-bundles are most effective and how to ventilate rooms optimally. This means creating tools to optimise IAQ sensor location, creating automated windows controls and looking at the cost-benefit analyses.

Create Training Tools

We will create training tools for hospital staff to learn about the role of the environment in infection prevention. This will involve visual computer models of microorganism spread and ventilation patterns in hospital rooms. This well help us understand the role of direct and indirect contact transmission and how to reduce this through an optimum balance of cleaning and hand hygiene.