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Statistical and Mathematical Modelling to Develop a Single Cancer Pathway

To understand the capacity and demand of the component steps( e.g. outpatients, access to diagnostics and treatment)within cancer pathways, both at a local and National level

  • Idea
  • 2018

Meet the team: 1000 Lives Improvement Service

Also:

  • Wales Cancer Network

Background to the Single Cancer Pathway (SCP)

The clinical community, the 3rd sector, CEOs and the Cancer Implementation Group in Wales all accept that moving towards a single cancer pathway  is ‘the right thing to do’ as it will:

  • Accurately reflect patient experience

  • Accurately reflect the pressures in the diagnostic system

  • Drive service improvement to address system delays

The objective

Sophisticated capacity and demand modelling is required to support NHS Wales in their work to refine and modify patient pathways to deliver this new objective.  Statistical and mathematical modelling methods are required to analyse patient activity data and feed into queueing and simulation (discrete event) models that capture patient pathways.  These tools will permit scenario analyses and to evaluate resource needs and performance of the SCP under different target times. 

This proposal is to work in partnership with NHS Wales colleagues and Academic experts to understand the gap in capacity and demand, both at a local and National level, broken down by pathway components e.g. outpatients, endoscopy, imaging and treatment and the relative contributions to the causes of increased demand.

How we will use the funding?

The aim of this work is to improve patient care and outcomes by reducing time to diagnosis and treatment times for those with cancer.  1000 Lives in partnership with Academic and NHS experts will use this funding to support additional research capacity to support the building and testing of mathematical models to inform and support the overall success of this project.

To inform this work it is planned to analyse the rich intelligence available through sophisticated tracking software that is being used in some of the Health Boards in Wales where the capacity to meet this forecast demand and improved performance will be required and the causes of this demand at a local and National level.

Innovation

The idea and innovation in this work is to develop highly sensitive mathematical models that can inform:

  • Cancer pathway redesign and improvement work

  • Support service planning models to account for additional referrals and activity each year.

  • Through accurately modified pathways of care, minimise the time for patients from the point of suspicion of cancer, through to the commencement of treatment.

Single Cancer Pathway

The Single suspected Cancer Pathway (SCP) is the proposed new Cancer Waiting Times (CWT) target for NHS Wales.  The SCP is being considered to replace the current cancer waiting time targets in Wales that have been a national performance measure for cancer since 2004.

Currently the two targets are:

  • Urgent suspected cancer (USC): Patients referred from primary care as having a suspected cancer, fulfilling specific criteria, and accepted as suspected cancer, should start treatment within 62 days of the receipt of the original referral. It has a compliance target of 95%.

     

  • Not Urgent Suspected Cancer (nUSC): This is for all patients diagnosed with cancer by other referral routes e.g. via A&E, or a surprise finding on an investigation for something else etc. It is currently measured from the time the patient accepts their treatment plan. It has a compliance target of 98%.

     

  • The proposed target is:

 

  • Single Suspected Cancer Pathway (SCP): Would measure all patients suspected of having cancer from the point of suspicion of cancer, accepting treatment should commence within 62 days from this point.

 

  • All Health boards from January 2018 will begin to measure performance against the SCP so waiting times for all cancer patients can be measured in the same way. 

How you can contribute

  • The sharing of similar data capacity and demand models and research on diagnostic services that others have tested with success.