NHS Machines: the utilisation of high-value capital equipment at NHS Trusts

December 31, 2016 6:00 AM

To read the research paper, click here

For the full dataset, click here

With the large real-terms budget increases the NHS has become accustomed to no longer possible given the state of the public finances, the NHS desperately needs become more efficient.

In the 2014 NHS Five Year Forward View, NHS England said that in the absence of further funding (flat in real terms) and efficiencies, a £30 billion a year mismatch between resources and patient needs would open up by 2020-21.

In response, NHS Chief Executive Simon Stevens challenged the NHS to find £22 billion of efficiency savings and the government said they would increase NHS funding by £8 billion by 2020-21. However bodies representing NHS organisations such as NHS Providers have already started claiming that the additional £8 billion is not enough and that the £22 billion savings target is “too ambitious”.

It is more vital than ever that the NHS makes full use of the resources already at its disposal, particularly as an aging population looks set to put further demands on hospitals, doctors and nurses.

However when it comes to the utilisation of expensive machines, many NHS trusts are not sweating their assets. If NHS Trusts are to achieve genuine efficiency, the management of machines must be improved as part of a greater efficiency drive.

The note looks at the utilisation of five different types of machines in NHS trusts in England:

  • Computerised Tomography (CT) scanners which provide a detailed view of different tissue types not available with traditional x-rays.
  • Magnetic Resonance Imaging (MRI) which are is a safe means of producing detailed internal scans useful in diagnosis and treatment. 
  • Linear accelerators (LINACs) which play a critical role in cancer care
  • Lithotripters which use ultrasound shock waves to break up kidney stones. 
  • Positron Emission Tomography (PET) scanners which are useful in effectively diagnosing and treating cancer.

To read the research paper, click here

For the full dataset, click here

 

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