Transformation Directorate

Digital productivity fund

The need to improve productivity in the NHS and social care is more important than ever as we recover from the pandemic.

Digital technologies have been proven to help increase productivity. Their efficiency and effectiveness frees up valuable staff time, enabling better care and support for patients and relieving pressure on the system. Productivity gains also reduce variations in clinical practice, patient outcome measures and financial efficiency.

Healthcare organisations are increasingly turning to digital solutions and we are responding to support them to adopt productivity-improving digital technologies.

Digital Productivity Fund

NHS England is providing funding of over £12m to unlock the potential of digital technologies to support the delivery of care.

This capital funding will help level up digital maturity, supporting organisations and systems to accelerate the adoption of proven digital technologies.

The Digital Productivity Programme will be supporting the spread and scale of automation capability, the application of automated identification and data capture technology, and extended reality in healthcare.


We have invested £7.5m to support 45 projects across 32 sites - from ICSs and CCGs to mental health and community organisations and acute trusts and local authorities - to scale their automation capability.

Our focus is to bring an economy of scale and unified approach to automate business function processes. Over 317 processes will be automated across HR, finance, recruitment, admin and clinical functions.

Alongside time saved to reinvest in patient care, key benefits identified by organisations include reduced burden on administrative staff, improved staff satisfaction and wellbeing, improved data quality and cost reductions associated with printing costs, staff retention and reductions in the use of agency staff.

Case study: Northampton General Hospital NHS Trust

Selected to become an NHS Centre of Excellence (CoE), Northampton General Hospital’s Automation Accelerator programme has identified over 500 viable automation opportunities across ten NHS organisations. The potential automation ideas are projected to repurpose around 115,000 hours per year - that’s over half a million hours over the next 5 years. This is expected to greatly increase as more Trusts work with the CoE.

Real time location systems

We have invested over £1.6m to support 10 NHS organisations to implement or expand their use of real time location systems (RTLS) including radio-frequency identification (RFID). These organisations include ambulance, community and acute trusts across the country with varying applications of the technology.

Their use of these systems ranges from medical device tracking, stock and inventory management to patient flow management within a hospital. Key benefits identified by successful sites include reduced costs associated with reduced waste, over ordering and lost items, improved patient and staff experience and time saved, leading to increased efficiencies.

Case study: Cambridge University Hospitals NHS Foundation Trust

Cambridge University Hospitals NHS Foundation Trust (CUH) has implemented medical equipment tracking. This includes automated reporting for clinical engineers and significant time savings with the average time taken to supply a device to a ward down to 12 minutes, and audit times have been reduced from 90 to just 8 minutes per ward. This has led to greater efficiency, device utilisation and a better patient experience.

Extended reality (XR)

We have invested £2m to support 14 organisations to use XR technology such as assisted reality, virtual reality, augmented reality and mixed reality.

These organisations include acute trusts, ambulance trusts and community services using XR across the following application areas:

  • patient education
  • health professional education and training
  • mental health and wellbeing
  • clinical communication
  • physiotherapy and rehabilitation
  • image guided surgery.

Some of the benefits we expect to measure include reduced costs as a result of treating patients in different ways, improved patient satisfaction, increased accessibility to care for patients and increased productivity as a result of reducing length of stay and improving patient outcomes more quickly.

Insights and evidence gathered will be used to identify where XR can offer the greatest benefits to the NHS, and contribute to developing a national vision for the safe and effective use of XR in healthcare.

Case study: Torbay and South Devon NHS Foundation Trust

Torbay and South Devon NHS Foundation Trust’s Digital Futures programme allows staff to be exposed to digital technology and empowers them to develop their own digital solutions within patient pathways, to improve patient experience and support health outcomes. So far this includes the use of mixed reality headsets for remote consultation for breast wound care, and virtual reality to distract from pain or anxiety in toenail removal surgery, saving £40,000 a year. The program is also developing digital solutions in adolescent mental health and intensive care rehabilitation pathways and remote multiple sclerosis consultations.

Other productivity-improving technologies

We have invested £1.3m to support 11 projects to use labour saving digital technologies. These include mental health and community organisations, acute trusts, local authorities and voluntary and community sector organisations.

The types of labour saving technologies include:

  • SurgeryPods allowing patients to take their own measurements
  • No-code development tools
  • Erostering, scheduling and care planning tools
  • Remote monitoring
  • Digital dictation
  • Digital front door
  • Digital mental health programmes.

Through use of these types of technologies, we expect to see benefits such as reductions in demand on frontline services, time savings for staff to reinvest in value-added services (like patient care, improved staff and patient satisfaction), as well as timely access to care and cost reductions.