Case studies

Joining up care for people with complex long-term condition

People with multiple long-term conditions often face fragmented care, affecting outcomes, experience and service efficiency. Between December 2024 and April 2026, North Central London Health Alliance tested a more integrated, proactive model of care, bringing hospital specialists closer to primary care.

The programme tested whether proactive case finding and case management could improve outcomes, reduce planned care activity and deliver value, particularly for people in areas of high deprivation.

The challenge

Long-term conditions place growing pressure on patients, carers and services.

  • One in four adults in England has at least two health conditions.
  • Fragmented services can deepen inequalities for people with multiple conditions.
  • Long-term conditions account for around 70% of healthcare spend, 50% of GP appointments, 64% of hospital appointments and 70% of hospital bed days.

In North Central London, demand is rising quickly.

  • 430,000 people in North Central London live with at least one long-term condition.
  • Complex long-term conditions are rising 7% faster than overall population growth.
  • Each year, around 150,000 people experience more than 10 acute care encounters.
  • The number of people with long-term conditions is expected to grow by 8% by 2030.

Multiple specialist appointments can be confusing, time-consuming and disconnected from what matters most to patients.

Patients and carers in the lived experience advisory group described feeling “in a plane with no pilot”.

The solution

Clinicians, operational leaders and patients co-designed a coordinated care model that aimed to:

  • identify patients most likely to benefit, using primary and secondary care data
  • connect primary care networks with hospital consultants and specialist advisors
  • support care planning without unnecessary referrals
  • agree shared care plans across primary, acute and community

This shifted care from reactive and siloed to proactive and joined up.

Key features

  • Case finding: A refined method identified people with complex multimorbidity most likely to benefit from coordinated support.
  • Coordination: Seven hospital consultants worked with primary care networks, bridging GPs and specialist services.
  • Case management: Coordinators created cross-system summaries, tracked actions and updated patient records.

Patient involvement

A lived experience advisory group helped ensure the service reflected the needs of patients and carers.

Key findings

  • 1,149 patients had care record reviews; 922 were case managed across 98 multidisciplinary team meetings.
  • 51% had opportunities for better coordination, including unnecessary or poorly timed appointments.
  • 85% were not treated to target; more than 50% had medication changes.
  • 49% faced barriers to prioritising their health, prompting links to wider support.
  • At £153 per case, projected gross savings were £193,000 per 1,000 patients, plus £678,000 in estimated net benefit from treatment optimisation.

Read a summary of key insights and learnings

Read an evaluation of the development and early intervention phase of the service.

Learning

The programme has shared learning across London, with neighbouring systems and through national forums, including NHS Providers. It has also featured in the Kings Fund, the HSJ, LinkedIn blogs and at NHS Confederation Expo.

Scaling success

Following promising results, the programme secured funding to expand in 2025/26, moving from individual primary care networks to neighbourhood footprints in two boroughs.

The programme is now being embedded in Haringey and Camden as part of their neighbourhood approach for 2026/27.

It also showed how linked primary and secondary care data can help clinicians identify patients for proactive support.

Two digital projects for 2026/27 will strengthen case finding and communication for neighbourhood multidisciplinary teams. One project will develop a digital app using linked care records and machine learning to bring key patient and service-use data together. The second will improve how teams coordinate actions from neighbourhood multidisciplinary team meetings.

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