University of Bristol announces research projects to aide ease NHS winter pressures

Patients could benefit from a programme of 16 rapid research projects announced today [22 January] that will examine how to ease winter pressures faced by the NHS – compounded this year by COVID-19 and a record flu season, and the cost-of-living crisis.

Launched by Health Data Research UK (HDR UK) with funding from the National Institute for Health and Care Research (NIHR), the projects include studies on how to reduce ambulance wait times, understanding the effects of cold homes on health, and a University of Bristol-led project that will compare risk factors for hospitalisations and death from winter infections.

The programme will also cover a range of data-driven approaches to pin-point pressures in the health care system, understand their causes and develop ways to overcome or avoid them. They apply lessons from the pandemic on how to drive rapid-response research that generate results fast and have a direct impact on health policy and clinical care.

The projects include studies aiming to ease pressures on emergency services by using hospital data to speed up patient flow through and out of emergency departments, as well as a project using an analysis approach called ‘machine learning’ to predict peaks of infection with the common bug, Respiratory Syncytial Virus (RSV), that can cause serious illness in young children and put pressure on paediatric intensive care units. Other projects will investigate the impact of cold and damp homes on people’s health with the aim of informing policies to protect the most vulnerable and avoid knock-on impacts on the NHS.

Dr Venexia Walker, Research Fellow in Medical Statistics and Health Data Science at Bristol Medical School and lead of the University of Bristol study, said: “Our project will determine risk factors for hospitalisation and death from four winter infections: influenza, respiratory syncytial virus, pneumonia, and COVID-19. These risk factors can be used to identify populations at high risk who may benefit from enhanced monitoring and early interventions to prevent hospitalisation and death, hence reducing winter pressures.”

Professor Cathie Sudlow, Chief Scientist at HDR UK, the UK’s institute for health data science, which is delivering the projects, said: “As a doctor who has previously treated patients in the emergency department, I am all too aware of the enormous challenges faced by the healthcare system this winter. It’s critical that we use data rapidly, securely and responsibly to support the NHS, its workers, and the patients who rely on it for their care.

“By using existing data, research teams, and infrastructure these projects are able to respond rapidly to evolving pressures on the NHS. Within three months, they will have honed in on key pain points in the health service, and developed evidence-led recommendations on how best to manage resources and prevent unnecessary illness through the winter.”

Each project is designed to generate findings in just a few months so that they can be implemented for future winters. After being selected in December 2022, the studies will start in January, produce results by the end of March, and publish their findings later this year.

The research is possible thanks to the improved health data infrastructure that was developed during the COVID-19 pandemic, partly led by HDR UK with the support of UKRI and partners across the sector, to enable access to data faster, more securely and at a greater scale than has been previously possible.

Dr Martin Chapman, from King’s College London, is leading on a project to understand the impact of the cost-of-living crisis on public health and NHS capacity. He said: “There isn’t enough emphasis placed on the impact of the health of children and young people on the NHS during winter. Living in cold, damp and mouldy homes leads to chest conditions in children and mental health problems in adolescents, and rising energy costs mean more people than ever are living with heat poverty.

“We’re investigating the effectiveness of interventions like support for energy bills on the health of young people by using Artificial Intelligence to digitally mimic their household environments and evaluate the impact of simulated interventions. This will help guide future policy changes to improve health conditions, reduce inequalities, and in turn reduce pressures on NHS services.”

Dr Mary De Silva, Deputy Chief Scientific Advisor at the Department for Health and Social Care (DHSC) which sponsored the projects, said: “Research plays a key role in helping us predict and understand the pressures our health and social care services face. Winter is an especially busy time for the NHS, and these projects aim to harness the power of routinely collected healthcare data to understand what is causing the pressures, and crucially to provide new solutions that can be swiftly turned into working practice.”

“We’re also testing new methods of funding and managing research, learning from how we rapidly delivered research results which saved lives during the pandemic. For these projects, it was just three weeks from launching the call for proposals to awarding the grants. Every project is ‘buddied up’ with Government analysts working in the DHSC, the Office for National Statistics and the UK Health Security Agency, to ensure that the results feed immediately into policy and practice.”

Several projects, like Professor Elizabeth Sapey’s, Director of PIONEER which is the UK’s health data research hub for acute care, tackle backlogs faced by emergency care departments which can leave patients in considerable distress while they wait for care.

She said: “Same Day Emergency Care (SDEC) has been proposed as a model of care to reduce hospital backlogs, where patients who need emergency care can be reviewed and treated without admission to a hospital bed.

“Selecting the right patients for Same Day Emergency Care is crucial, but the current systems to do so have been developed mainly on rural populations of White males and are unable to accurately select appropriate patients from diverse, urban populations.

“Our work will involve applying machine learning techniques to highly detailed hospital data from a diverse population to develop a better model to identify patients for Same Day Emergency Care, ultimately reducing inequalities in care and relieving pressure on emergency services.”