Advancing Applied Analytics

This funding programme aims to improve analytical capability in support of health and care services

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This programme is open for application until 26 February 2019.

  • Our Advancing Applied Analytics is aimed at improving analytical capability in support of health and care services.
  • We have £750,000 of funding to support up to 12 project teams across the UK over 15 months.
  • The programme is now open for applications and the closing date is 26 February 2019.

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 informing decisions for services or across organisations and health systems.

Aims

The Health Foundation’s Advancing Applied Analytics programme aims to improve analytical capability in support of health and care services.

Through this programme we support analysts who are working on local innovative and ambitious projects that can demonstrate how they will improve analytical capability in support of health and care services and provide lessons for the wider care system. 

We don't focusing on specific themes but consider applications from a broad range of areas, provided these can demonstrate improvements in analytical capability. We are especially interested in projects that address the capability deficiencies we identified in our report Understanding analytical capability in health care.

Application process

In the third round of this programme we have £750,000 of funding to support up to 12 good quality projects that: 

  • improve analytical capability and can show how this will support better care in the future
  • are led by a local/regional NHS or social care provider or commissioning organisation including local authorities, though partnerships with other groups and sectors are welcome
  • could benefit from funding of up to £75,000 per project 
  • starts no later than September 2019 for a maximum duration of 15 months.

We are willing to consider applications on a broad range of topics, provided these can demonstrate how they will lead to improvements in analytical capability. We are interested in projects that address the capability deficiencies we identified in our report Understanding analytical capability in health care. For example:

  • better understand and structure the problems faced by managers/clinicians  
  • access and understand the evidence and information that can be brought to bear on a problem 
  • apply appropriate and robust methods to manipulate information and data 
  • communicate findings accurately and clearly.

For this round, we are especially interested in:

  1. Projects that help senior managers and clinicians understand the value of better analytics. So, for example we are keen to support initiatives that test out innovative approaches to engaging senior staff in data analytics. 
  2. Projects that demonstrate a clear path from the initial project to longer-term and sustainable changes beyond the life of the individual project.  

Read about our current Advancing Applied Analytics projects, below. 

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How to apply

If you are interested in applying for the Advancing Applied Analytics programme, please go through the following steps to complete your application

1. Important supporting information

All applicants must download and read the following three supporting documents in full before applying for this programme:
-    Notes for applicants - Information about the programme, selection criteria and timelines
-    Frequently asked questions 
-    Application form guidance - guidance for applicants on completing the application form

2. The application process

The online application form will require you to supply information to meet all the selection criteria outlined in the Notes for applicants. 

Applicants must also provide a declaration of signatures from the lead organisation. This needs to be completed and uploaded to section 8 of the online application.

All applications must be completed and submitted via our online system AIMS

Full guidance on accessing, completing and submitting your application via AIMS can be found in the Applicants user guide. We recommend that you keep a copy of this manual to hand to refer to while you complete your application.

3. Deadlines
The deadline for online applications is noon, Tuesday 26 February 2019. The online application portal will not accept submissions after this time. 

Shortlisted candidates will be invited to a telephone interview and must be available from Monday 3 June to Friday 7 June 2019. Interviewed applicants will be advised of the outcome by Friday 28 June 2019. 

Please note: If you have any queries about the programme or application process, and any technical issues please email applied.analytics@health.org.uk.

Projects

In the second round of this programme we are supporting 11 teams with £750,000 of funding. The projects aim to improve analytical capability and show how this will support better care in the future.

They are led by a local/regional NHS or social care provider or commissioning organisation, including local authorities, though partnerships with other groups and sectors.

Each project benefits from funding of up to £75,000 and will run for a maximum duration of 15 months.

    ABCi Mathematical Modelling and Analytics Academy

    Improvement project

    This project will involve developing a training programme for frontline staff in data analysis and mathematical modelling techniques, to overcome barriers to the adoption of modelling and analytical t...

    Use of novel modelling techniques and routinely collected data to explore responses to winter pressures

    Improvement project

    This project will involve refining a novel modelling technique to simulate patient flow using readily available routine data in order to provide new flow models to support decision making and change m...

    Personalised medicine analytics dashboard for triaging patients in emergency departments

    Improvement project

    This project will involve developing a probabilistic early warning system based on machine learning/Artificial Intelligence technologies to promptly identify high-risk patients needing urgent care.

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