Health Data Research UK (HDR UK) has launched a programme of rapid research projects to examine how to ease winter pressures faced by the NHS. The studies, which include projects aiming to help reduce ambulance wait times and understand the effects of cold homes on health, are supported with funding from the National Institute for Health and Care Research (NIHR).
The 16 projects cover a range of data-driven approaches to pinpoint pressures in the healthcare 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 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.
Professor Cathie Sudlow, Chief Scientist at HDR UK says: “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. Results are expected by the end of March, with findings published later this year.
The 16 projects are:
• Early and safe identification of the right bed for the right patient on exiting an Emergency Department setting
• Machine learning to forecast the peak and magnitude of winter healthcare pressures due to respiratory syncytial virus
• Improving patient flows through acute medical departments through better patient selection for Same Day Emergency Care
• Using rare disease phenotype models to identify people at risk of COVID-19 related adverse outcome
• Understanding demand for emergency care using regional routine data from emergency department and acute hospital admissions
• Which combinations of Multiple Long-Term Conditions (MLTC) are associated with the greatest risk of hospital admission over the winter season, and to what extent does COVID-19 or influenza vaccination modify this risk
• Improving characterisation, prediction and intervention for COVID- and influenza-related morbidity and mortality
• DS4SmartDischarge: Data Science Informing Complex Discharge Winter Policy
• Describing, characterising and predicting winter respiratory accident and emergency attendances, hospital and intensive care unit admissions and deaths
• Predicting Hospital Length of Stay in Acute Respiratory Infections Patients (PHLOSARIP)
• DIAPHRAM Data Intensive Action on Winter Pressures through Healthcare Resourcing and Access in Cheshire & Merseyside - focused on children, social prescribing and Telecare
• Identifying groups at high risk of hospitalisation and death during the winter
• Comparison of risk factors for hospitalisations and death from winter infections
• Data science for winter pressures in primary care in the context of COVID-19 recovery: using data to detect local problems, mitigate risk, and understand the impact on patient outcomes
• Using AI to understand how preventative interventions can improve the health of children in the UK and reduce winter pressures on the NHS
• SIREN Winter Pressure Study.
