Population Health Analytics

The shift toward neighborhood care outlined in the NHS 10-Year Plan, published in July 2025, creates new opportunities for healthcare organizations across England to empower local communities to take a more active role in their population’s health. We are already starting to see this shift take shape, with health systems increasingly looking to move care closer to home and a drive towards neighborhood-based services to address growing pressure on health and care services.

How to make neighborhood health work

The shift towards neighborhood health focuses on bringing care closer to home and building services around the needs of the people who live there. The goal is to make better use of the services and resources already in place, while identifying where different types of more tailored and localized support are needed.

As we see Integrated Neighborhood Teams (INTs) forming and the shift towards neighborhood-based care gathering pace, health and care organizations across the country are looking at how best to understand and respond to the needs of their local populations. INTs are bringing together people and organizations from across health, care, councils, the voluntary sector and the wider community to work around the needs of their local populations. The aim is to join up services and help people stay well while reducing the need for hospital care.

To do this effectively, INTs need a clear picture of the populations they serve. Population health analytics tools integrate disparate datasets from across different settings, and risk stratification and segmentation tools can help identify patterns of need and provide insights that teams can use to target support and organize services.

These insights can help teams identify people who have complex needs, may be at greater risk of deterioration and understand where unmet need lies. In turn, this can help improve access and equity, reduce avoidable hospital use and support more proactive, coordinated care closer to home.

Every neighborhood is different: understanding your population

Moving care from the hospital to the neighborhood means identifying and addressing patient needs earlier. It means care is reframed around prevention and proactive support for people with complex needs and establishing how these patients can be treated in appropriate community settings.

In order to organize care more effectively around people in a neighborhood, health and care systems first need to understand the population: who lives there, what they need and where those needs are greatest.

However, no two neighborhoods are the same. Different neighborhoods will have their own mix of health and care needs, with varying levels of chronic disease, multimorbidity, health inequalities, demographics and social needs. These differences can also be shaped by wider social and environmental factors that affect access to care and health outcomes, including social risk factors, proximity to hospitals, housing and transport. So, how do you understand those differences and organize services and care pathways around them?

How population health analytics helps

Population health analytics solutions stratify and segment patient populations based on structured clinical and demographic data from across a wide range of settings. The Johns Hopkins ACG System is unique in its patient-centered approach, considering the whole person and all of their activity across primary and acute care settings over a 12-month timeframe, rather than just specific diseases or events driving healthcare risk.

Using this comprehensive methodology, the ACG System’s Patient Needs Groups (PNGs) categorize patients into clinically meaningful cohorts, allowing teams to identify people with similar needs and target care appropriately. But segmentation is just one part of the picture. The ACG System can also provide insights into care coordination, care density – measuring shared patients between clinicians, polypharmacy risk and risk of hospitalization and ED admission.

The ACG System measures care coordination risk by looking at patterns of a patient’s healthcare use across different clinicians and services. Understanding this risk is crucial for INT teams who may be able to intervene to improve care coordination and more equitable access to primary care, community and social services.

Related to this, the care density metric shows how well a patient’s care is connected across clinicians and care settings. Lower care density can indicate fragmented care and highlight opportunities to strengthen collaboration between clinicians and improve continuity of care.

As polypharmacy can increase treatment complexity, medication burden and the risk of adverse effects, identifying this risk early and analyzing medication burden can help engage people who may benefit from structured medication reviews.

The takeaway is that using multiple risk markers together is a more effective way to build a complete picture of neighborhood risk and unmet need. By combining clinical complexity with health care utilization, medication burden, social needs and indicators of care coordination risk, teams can identify people whose needs are changing, understand what may be driving that risk and target more personalized, proactive interventions.

From insight to action – a common but adaptable approach to neighborhood care

Many organizations in England are already using the ACG System to generate cohort-based insights, helping teams identify unmet clinical or social needs and design more targeted, proactive care pathways.

You can read more in our eGuide, Bridging the Hospital to Community Shift, which sets out how intelligent population segmentation can turn the neighborhood health ambition into operational reality, and also our new 10 Quick Wins guide, which shares how the Johns Hopkins ACG System can help teams achieve meaningful population health results.

To learn more about the ACG System, visit hopkinsacg.org or contact us at acginfo@jh.edu. If you are a current ACG System customer, please reach out to your Account Manager.