Beyond the hospital: How boards can lead the digital shift to neighbourhood working
Data for strategic planning
Successful neighbourhood working means recognising that data is a valuable organisational resource that drives decision-making and can generate tangible financial or operational benefits. Whilst in trusts today a lot of data is captured, it is mostly for recording performance and for reporting purposes rather than for truly driving strategy.
Population health analytics analyse data from a range of sources including health records, social care, housing, and demographic statistics to generate a detailed, granular understanding of a specific population’s health profile. These analytics can help to segment the local population into groups with similar health needs or risks, enabling neighbourhood teams to accurately identify and prioritise vulnerable cohorts. This evidence-based approach allows neighbourhoods to move from reactive treatment to proactive management of community and population health. It will help them allocate resources more efficiently, reduce inequalities, prevent disease, and measure the effectiveness of interventions at scale.
In order to support this population health approach, boards:
- Should look to their existing systems, including electronic patient records for reporting and tracking health inequalities and ensure this data is used to inform clinical and operational decision making.
- Consider where they might have analytics capability or resource within the trust that can be utilised across organisations.
Case study: Population health
People living in the more deprived areas of the Bristol, North Somerset and South Gloucestershire Integrated Care System (ICS) are more likely to die earlier and on average have 10 fewer years of good health. By using new techniques such as segmentation and risk stratification, the ICS found 100 people aged 40 to 69 living in high levels of deprivation in Bristol inner city, who also had a combination of obesity, high blood pressure, depression and anxiety. These patients were offered a medication review, a healthy hearts group consultation and an appointment with a social prescriber to help with exercise, mental health and lifestyle issues. By addressing factors affecting their health beyond healthcare, it is hoped the project will improve patients’ understanding of their health and improve their physical and mental health, with wider positive implications including a reduction in unnecessary appointments and admissions to A&E, outpatients, and general practice.
Looking after patients remotely
Remote monitoring and patient-generated data are important in shifting care from reactive to proactive and personalised, supporting people in their homes and reducing unnecessary and unplanned admissions. They transform traditional care from reactive, episodic interventions that use the expensive and finite NHS resources to data-driven, coordinated management of populations managing them outside of hospitals that unlocks both clinical and operational benefits along with a greatly improved patient experience.
Capturing data from patients' own devices such as smart phones, watches, blood pressure or blood sugar devices means key measurements can be made at home and trends followed to enable data-driven coordinated management.
In order to support this, boards should:
- View remote monitoring as a core part of your service delivery.
- Look to new technologies, such as artificial intelligence, to speed up assessments, reduce clinical workload and improve the capacity to care (but be careful to ensure any new tooling is well integrated into existing workflows).
Case study: Population health management and remote monitoring
Frimley Integrated Care System (ICS) uses a combination of Population Health Management analytics and Remote Monitoring to support CORE20PLUS5 and wider inequalities programmes, informing service redesign and resource allocation rather than direct patient‑facing care alone.
Frimley ICS identified specific cohorts across its population that could be managed at home through remote monitoring and enrolled over 3,500 high‑need patients. They reported around a 38–39% reduction in A&E attendances and about a 54% reduction in hospital admissions compared with similar patients not yet enrolled. Outpatient attendances fell by roughly 27%, with GP appointments down by about 19% and marked falls in 111 and 999 contacts (around 36–37% reductions).