Ten innovative projects selected to be part of programme to improve analytical capability in health and care services

The 10 local, innovative and ambitious projects will provide lessons for the wider health and care system, with ideas that can be replicated in other areas or specialisms. 

Good quality analysis and the ability to use information effectively is an essential element in any learning health care system. Analysis can help shape care for individual patients as well as inform decisions for services or across organisations and health systems. 

The Advancing Applied Analytics programme is part of the Health Foundation’s work on data analytics and follows on from our report, Understanding analytical capability in health care. This fourth round of the programme is supporting good quality projects that address the capability deficiencies we identified in this report.

Each project is led by a local or regional NHS or social care provider or commissioning organisation, and will run for up to 15 months, with each team receiving up to £75,000 of funding to support the implementation, evaluation and dissemination of findings from their work. The projects will start by September 2020.

For the first time, this round of the Advancing Applied Analytics programme will be supported by NHSX, which is driving digital, analytical and tech maturity in local NHS organisations in order to improve care. NHSX will provide support to the projects to facilitate sustained improvement and spread of innovation across health and social care. The Health Foundation will continue to lead and manage each project with the same approach as all previous rounds. 

Ellen Coughlan, Programme Manager at the Health Foundation, said:

'Analysis can help shape care for individual patients, as well as inform decisions for services or across organisations and health systems. 

'Our Advancing Applied Analytics programme aims to increase capability and capacity with regards to analytics. This fourth round of the programme is focusing on addressing the capability deficiencies that we have previously identified. The 10 projects we have selected will demonstrate how better analysis can lead to better patient care.

Dr Sarah Culkin, head of analytics at NHSX, said:

'At NHSX we want to encourage the use of analytics across the NHS and social care as we know it can help local organisations plan and provide better services for their patients and staff. 

'The ten successful projects could each bring huge benefits to the NHS and social care and we look forward to supporting teams as their work progresses.'

The projects are:

• Delivering insight for decision makers: Improving safety and patient flow in intensive care 
  University Hospitals Bristol and Weston NHS Foundation Trust
• An open-source suite to enable and advance analyst engagement in population health management
  Healthier Together (Bristol, North Somerset and South Gloucestershire Sustainability and Transformation Partnership)
• Application of machine learning in recognising and treating asthma
  Heathrow Medical Centre
• AuguR: A scalable open-source interactive cancer analytics web application for routinely collected data
  Leeds Teaching Hospitals NHS Trust
• Putting the R into pharmacy: Developing open-source analytics for better pharmacy stock control
  Nottinghamshire Healthcare NHS Foundation Trust
• Modelling the quality and availability of regulated health and social care services using geospatial-visualisation
  Regulation and Quality Improvement Authority (Northern Ireland)
• Developing a patient event based analytical framework to track and identify variation in clinical processes and patient outcome
  Royal Berkshire Hospitals NHS Trust
• Moving beyond bean counting: Improving analytical capability in health visiting – national scoping and local application
  Institute of Health Visiting and Southern Health NHS Foundation Trust
• Predicting inpatient flow and improving experiences with machine learning algorithm
  Surrey and Borders Partnership NHS Foundation Trust
• Predicting length of stay on NHS mental health adult inpatient wards using machine learning
  Sussex Partnership NHS Foundation Trust