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Eighteen local partnerships design adult social care services of the future with £1.4m funding

20 September 2018: New funding will demonstrate how predictive analytics and digital information sharing can improve care and support for people needing social care services.

New funding will demonstrate how predictive analytics2 and digital information sharing can improve care and support for people needing social care services.

NHS Digital has awarded eighteen councils a share of £1.4m to develop digital projects that support social care.  Successful bids include Westminster Council’s plan to use apps and Skype to share appropriate information when discharging from hospital into care and Wolverhampton Council’s proposal to improve intervention by using predictive analytics for early identification of adults with complex morbidities.

The successful local authorities are:3

Improving the flow of health information into social care (over £820,000 in total)

  • Westminster City Council
  • Newham Council
  • Rutland County Council
  • Haringey Council
  • Lincolnshire County Council
  • Lewisham Council
  • North Tyneside Council
  • Manchester City Council
  • Milton Keynes Council

Improving the flow of social care information into health (nearly £200,000 in total)

  • Sutton Council
  • Hull City Council
  • Nottinghamshire County Council

Using predictive analytics for early intervention and prevention (nearly £350,000 in total)

  • Islington Council
  • City of Wolverhampton Council
  • Central Bedfordshire Council
  • Worcestershire County Council
  • Nottinghamshire County Council
  • Luton Borough Council

The funding is aimed at supporting collaborative partnerships between local authorities, the third sector, health partners and academia.

Caroline Dinenage, Minister for Care said: “Digital technology has the potential to transform the way we deliver care, improving the experience for those receiving it and freeing up staff time so they can spend more with those in their care.

“This funding will enable councils and their partners delivering social care to make information sharing fast, secure and accurate, and will make the journey as smooth as possible for some of the most vulnerable in our society as they move between care settings.”

James Palmer, Head of the Social Care Programme at NHS Digital said: “The successful projects span a wide range of areas and give a glimpse into the future of social care. From giving care providers access to hospitals’ electronic patient records in real time, reducing delayed discharges, to providing health care professionals with live social care alerts.

“There is great potential for these projects to be replicated easily to deliver benefits quickly for the system and pave the way for a truly integrated future.

“The work on predictive analytics is significant given its potential to support people at earlier stages which may help to reduce the need for long-term social care. Through the use of predictive models that forecast service need and target interventions, we have the chance to help people remain independent, in their own homes, for longer.”

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Notes to editors

  1. NHS Digital is the national information and technology partner of the health and care system.  Our team of information analysis, technology and project management experts create, deliver and manage the crucial digital systems, services, products and standards upon which health and care professionals depend.  Our vision is to harness the power of information and technology to make health and care better.
  2. Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behaviour and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models on the likelihood of a particular event happening in the future.  Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment.
  3. A full list of the successful local authorities and their projects:
Digital social care demonstrators of health information into adult social care






Discharge from hospital to adult social care (ASC) homecare and residential services by using apps, Skype



Pilot in 2 nursing homes clinical information access via EMIS Web, Data Security and Protection (DSP) Toolkit and NHSMail



Improve digital maturity of ASC Providers. Proposal includes WIFI to 28 residential homes, SKYPE, DSP Toolkit, TPP's EPR solution



Create a Health to Adult Social Care Provider Data Standard



Support and training to residential homes to access NHSMail, DSP toolkit. Pilot with 40 digitally engaged care providers



Create a portal for GP's and District Nurses to access Local Authority systems and 2 electronic forms for GP's and nurses to share information and create referrals

North Tyneside


Using Medical Discharge Summary to discharge to care homes via secure email (NHSMail)



Giving adult social care teams access to the trusts EPR system in real time to reduce Delayed Transfers of Care

Milton Keynes


Discharging and referring patients by building an on-line form (FirmStep) that the Discharge Team can use on the local authority website  



Digital social care demonstrators of adult social care information into health






Digitisation and transmission of ‘This is Me’ content held on Person Centred Software system at a Care Home



Social Care Intervention radar – a live intervention list for health care professionals, showing patients that have social care interventions or alerts triggered



System to system messaging, from data held on Adult Social Care System (Mosaic) to Emergency Department



Machine learning/predictive analytics data use for adult social care early intervention and prevention






Build a novel dataset for structured/unstructured data to identify key predictors of ASC needs using predictive models



Use predictive analytic models for early identification of adults with complex morbidities to design/improve intervention programmes

Central Beds


Use predictive analytic models for early identification of adults with complex needs (e.g. frailty/Learning Disability) to design/improve intervention programmes and optimise services

Worcestershire County Council


Develop predictive analytic models to predict demand for council funded social care and self-funder pick-ups to target advice effectively for self-funders



Develop predictive analytic models to identify people at an early stage of high risk of care admission, to reduce long term residential care and prolong people living at home longer with targeted interventions



Support prevention via an e-Frailty index with relevant data from telehealth /telecare systems that can populate adult social care system (LiquidLogic)

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Last edited: 31 May 2022 11:56 am