Award Title: Stewardship of Antimicrobials using Real-Time Artificial Intelligence (SamurAI) - Phase 1
Funder: Department of Health and Social Care

Abstract: Background: 20-30% of patients in hospital are taking antibiotics unnecessarily, or being treated with a suboptimal choice of drug, dose or duration.  This increases patients' risk of side-effects and future drug-resistant infections and prolongs hospital stay, which increases healthcare costs.  Stopping antibiotics early in patients who no longer need them can reduce antibiotic overuse, but most doctors – except experts in infection - find this challenging. SamurAI s goal is to give every doctor 24/7 access to an infection expert.  Aim: To prototype and test an AI device which identifies patients who could stop or change antibiotics, in order to reduce unnecessary antibiotic prescribing. The device will monitor every patient in hospital taking antibiotics, and alert doctors to patients who could stop or change antibiotics so they can be reviewed by an infection expert.  Methods: We will create a training dataset which links historic expert antibiotic review decisions to electronic health records for the corresponding prescribing episode. Using the dataset we will train a supervised classification model to predict an experts  prescribing review decision (stop, change, continue antibiotics). Model predictions will be validated in 700 randomly sampled cases by comparison with the experts  review decision in order to estimate Receiver Operating Characteristics (ROC) Area Under the Curve (AUC).  Safety data will also be collected.  Timeline: 0-2 months - training dataset; 2-6 months - classification model; 9 months - validation; 9-12 months – dissemination.  Impact: This study will provide validation data in a relevant environment (TRL5).  Dissemination will focus on raising awareness of SamurAI among clinicians, pharmacists, patients, policy makers, and NHS senior managers) and identifying sites to participate in a future multi-centre clinical evaluation. A patient public involvement group will oversee SamurAI s use of patient data and be involved in all stages of the research project.

Lay Abstract: The Problem   Antibiotics save lives, but the more they are used, the less effective they become. Infections learn how to resist them. More than 50,000 people in the UK have infections that resist antibiotics, and this number will increase if we do nothing.       In a typical hospital, around 150 patients are taking antibiotics on any one day. Research suggests that 30-50% of individual prescriptions could be avoided if they were reviewed by a specialist in antibiotics looking at things with the danger of over-use in mind. However, there aren t currently enough of these specialists - microbiologists and antibiotic pharmacists - to review every antibiotic prescription and provide feedback to every doctor. For last 2 years we have been working with infection specialists, health psychologists and designers to solve this problem.       The Solution   It s simple - use fewer antibiotics. But antibiotics seem so helpful that this hasn t worked, the use of antibiotics in UK hospitals is still growing. Our plan is to develop and test something we call SamurAI. It involves computers working in a way that copies human thinking ( artificial intelligence ) and it takes advantage of the introduction of electronic patient records into UK hospitals. The system will combine historical data for patients prescribed antibiotics with the findings of specialists in infection as they review prescriptions. Under specialist medical supervision, the computer system will  learn  what works best and when to start, stop or change the use of antibiotics. Once up and running the system should be able to identify and flag patients who should be reviewed by specialists and guide antibiotic treatment for individual patients. The research will start at one hospital in London, but our aim is to roll it out to 3 different hospitals within 4 years.       Evaluation and Safeguards   First, we will evaluate whether SamurAI can accurately and safely identify patients who can stop, change or switch antibiotics. We will compare SamurAI s predictions to what happens when experts review 200 patients taking antibiotics. If SamurAI s predictions and the experts mainly agree, we will apply for funding to further develop and test SamurAI through carefully designed trials. We expect it can reduce antibiotic use by 10-20%, benefitting patients by reducing their time in hospital and the risk of side-effects from antibiotic treatment. Reduced use will also prolong the lifespan of our existing antibiotics. Once we have tested the system in our three hospitals, we aim to introduce it in new hospitals by 2027.   We will involve patients, the public and healthcare workers at all stages of the project, to understand their concerns about using patient data and agree how to protect it.  We will hold a workshop to share research findings with patients.

Research Activity Codes: 7.3 MANAGEMENT AND DECISION MAKING
Health Categories: INFECTION