Research Fellow (Quantitative)

The postholder will join a team funded by an NIHR HSDR grant ‘Evaluating the impact of artificial intelligence triage in online consultations to reduce delays in urgent primary care’. GP practices in England provide over 30 million appointments per month, some of which are for medical conditions that require urgent treatment like infections or heart attacks. Delays in urgent care can lead to patient harm including hospital admission or death. A key challenge is to identify which patients require urgent help. Over the past two years, 96% of GP practices in England provided online consultations, which enable patients to request help from their healthcare teams by submitting forms over the internet. Online consultations come to the GP practice unprioritized and can potentially exacerbate delays in providing urgent care. One potential solution is for the online consultation system to automatically detect and highlight urgent and emergency requests as soon as they are submitted as an add-on feature (‘AI Triage’). Our aim is to evaluate the impact of introducing AI Triage to GP practices already using online consultations on delays in urgent and emergency primary care, healthcare resource use, and explore how these are influenced by organisational context and patient characteristics. We will recruit 20 intervention and 20 control GP practices in a modified Zelen design that have used an online consultation system (PATCHS) for at least 12 months. Intervention GP practices will receive an add-on AI Triage intervention (PATCHS+AI Triage). We will undertake a controlled interrupted time series analysis evaluating the impact of AI Triage on delays in completing urgent and emergency patient requests. Secondary analyses will evaluate its impact on the number of online consultations submitted by patients, A&E attendances, and emergency hospital admissions. We will also conduct a qualitative process evaluation based on five in-depth case studies, supplemented by interviews with up to 60 staff and 30 patients. We will conduct a quantitative process evaluation focusing on descriptive analysis of AI Triage implementation and prediction accuracy. We will create help guides and toolkits on how to use AI Triage safely and effectively for patients and GP practices to be delivered during online consultation deployments. Interviews will be conducted via Zoom on Thursday 29 February 2024.. What you will get in return: Fantastic market leading Pension scheme Excellent employee health and wellbeing services including an Employee Assistance Programme Exceptional starting annual leave entitlement, plus bank holidays Additional paid closure over the Christmas period Local and national discounts at a range of major retailers The School of Health Sciences is strongly committed to promoting equality and diversity, including the Athena SWAN charter for gender equality in higher education. The School holds a Silver Award which recognises their good practice in relation to gender; including flexible working arrangements, family-friendly policies, and support to allow staff achieve a good work-life balance. We particularly welcome applications from women for this post. Appointment will always be made on merit. For further information, please visit: www.bmh.manchester.ac.uk/about/equality Enquiries about the vacancy, shortlisting and interviews: Name: Dr Ben Brown Email: benjamin.brown@manchester.ac.uk This vacancy will close for applications at midnight on the closing date.