Newton: The Future of Adult Social Care

This report, developed by Newton in collaboration with the County Councils Network (CCN) sets out an evidence‑based vision for reforming adult social care. It argues that the sustainability of adult social care depends not only on funding reform, but on redesigning services around independence, prevention and personalised outcomes.

The report highlights growing demand driven by demographic change, rising complexity of need and long‑term underinvestment following a decade of austerity.

Adult social care now accounts for a significant proportion of local authority spending, particularly for CCN member councils, while reliance on informal carers remains critical to system viability.

Covid‑19 exposed structural weaknesses but also demonstrated the benefits of integrated working, rapid innovation and strengthened community responses.

An ‘optimised delivery model’, put forward in the report, places promoting independence at its core, supported by strengths‑based practice, effective short‑term services, strong partnerships with providers, investment in digital capability and outcomes‑focused commissioning.

Evidence suggests that widespread adoption of this model could significantly improve outcomes for adults while slowing the growth of long‑term expenditure.

The CCN emphasises that these benefits can only be realised alongside long‑term funding certainty, better national–local alignment and a shared performance framework focused on outcomes rather than process.

Recommendations to government include:

• Establish a long‑term, sustainable funding settlement for adult social care.
  
• Embed independence, prevention and strengths‑based practice as system priorities.
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• Expand access to effective short‑term services such as reablement.
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• Strengthen integration with health through shared outcomes and pathways Invest in digital, data and analytical capability across councils.
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• Professionalise, reward and retain the adult social care workforce.
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• Shift commissioning towards outcomes‑based and place‑led models.