On a population health scale, time and again some of the most preventable illnesses and conditions occur within patients who sit between the brackets of “well” and “unwell”.

This group represents the Emerging Risk Population.

These people often appear in healthcare systems through subtle patterns rather than diagnoses. Typically, they might attend urgent care for low-acuity issues, receive frequent prescriptions without review, or regularly miss appointments.

Others become vulnerable after leaving the hospital, especially when follow-up is inconsistent or support isn’t coordinated.

Without early intervention, much of this emerging risk population deteriorates, leading to emergency admissions and diagnosis at a later stage.

 

Why this emerging risk exists

Due to a lack of integration between services across healthcare providers, risk signals are spread across GP records, hospital activity, community services, and social care. This often means no single team has full visibility of a patient.

These gaps occur for several reasons:

• Inconsistent coding and recording can delay the recognition of emerging conditions.
• Risk thresholds often prioritise current severity over long-term trajectory, meaning action is triggered only after deterioration has occurred.
• Social barriers - including deprivation, housing instability, or limited access to services - can suppress demand until a crisis point is reached.

 

Why proactive care fails without system-wide intelligence

Many population health approaches rely on risk stratification methods alone. These have their limitations when used in isolation based purely on out-of-date historical data.

Risk stratification depends heavily on known diagnoses, established long-term condition registers, and historic utilisation. This means it can miss people who are not yet diagnosed, inconsistently coded, or under-engaged with services. In other words, they can identify the need too late. Using risk stratification in combination with wider data, in near real time can really enable shift towards proactive care.

Proactive care requires the model to move upstream. Systems need:

• Near real-time insight into emerging risk
• Data linked across organisations
• Outputs that are actionable and aligned to health workflows

Without these foundations, “proactive care” often remains an aspiration rather than an operational reality.

 

What CIPHA changes: from disconnected records to actionable population intelligence

CIPHA, the Combined Intelligence for Population Health Action platform, was created to address the lack of joined-up care.

In simple terms, CIPHA links health and care datasets across organisations and presents them through analytics and dashboards designed for system action.

By bringing together data from primary care, secondary care, community services, and beyond, CIPHA enables teams to see patterns that would otherwise remain hidden.

CIPHA originated in Cheshire and Merseyside as a cross-system collaboration and has now scaled to support a population of around 17 million.

Used across 11 Integrated Care Systems (ICSs), it provides a single, joined-up intelligence layer for population health, prevention, shared care data, operational planning and early intervention.

CIPHA’s growth demonstrates how population intelligence can be applied consistently across an entire system, rather than being restricted to siloed initiatives.

Read more about CIPHA.

 

Spotting emerging risks with prevention-first case finding

CIPHA supports a prevention-first approach by identifying early signs of risk and unmet need before escalation occurs.

A) Undiagnosed or early-stage conditions

CIPHA helps identify people who may not yet have a formal diagnosis but are showing consistent signs of risk. This includes patterns such as:

• Hypertension risk through inconsistent or rising readings, missed reviews, or lack of coded diagnosis.
• Early chronic obstructive pulmonary disease (COPD) or asthma signals shown by repeated inhaler prescriptions, cough medications, or low-acuity A&E attendances.
• Diabetes risk identified through HbA1c trends, BMI patterns, or relevant clinical history.
• Chronic kidney disease risk through declining eGFR trajectories, medication patterns, and comorbidities.
• Frailty indicators such as falls, polypharmacy, or interactions with social care services.

B) Worsening long-term conditions before escalation

CIPHA can also identify people whose long-term conditions are starting to worsen, even though they have not yet met formal escalation thresholds.

This includes missed monitoring, rising risk scores, increased use of rescue medications, or repeated contacts that fall short of admission criteria.

C) Care gaps linked to access and inequality

For many, the issue is not a lack of need but difficulty accessing care in a way that works for them. Patterns such as repeated DNAs, low screening uptake, poor continuity, or uneven vaccination coverage often point to practical, social, or systemic barriers rather than disengagement.

CIPHA helps teams make sense of these patterns and act on them. During the COVID-19 pandemic, it was used operationally across Cheshire and Merseyside to identify underserved groups, target outreach, and support access to testing and vaccination.

More recently, the same approach has been applied to fuel poverty: by linking health and care data for 2.6 million residents with deprivation and fuel poverty indicators, the system was able to pinpoint over 1,000 high-risk people and coordinate proactive support.

Read more about CIPHA’s impact in Cheshire & Merseyside

 

Using social and demographic overlays to reveal inequities

Clinical risk alone rarely tells the full story. Social and demographic overlays are often the difference between identifying risk and delivering the right support.

CIPHA places health data alongside indicators such as deprivation, housing instability, and learning disability flags. This allows systems to spot overlapping risks, such as young carers with repeated missed appointments, or communities facing deprivation alongside low uptake and rising clinical risk.

These insights enable:

• Place-based targeting of interventions.
• Culturally appropriate outreach.
• Better-informed service redesign and resource allocation.

 

From insight to action: What proactive care looks like operationally

Proactive population health depends on a clear, operational model. In practice, this follows a closed-loop approach:

• Detect emerging signals and care gaps using linked data and overlays.
• Validate risk through light-touch clinical review.
• Segment people based on the type of support required.
• Intervene through appropriate pathways such as outreach, remote monitoring, medication review, or social prescribing.
• Measure and iterate to assess whether risk has reduced and engagement has improved.

CIPHA supports this flow by producing intelligence that fits into everyday workflows. It has been used to support waiting list stratification using population data, optimise long-term condition management through virtual wards and remote monitoring, and enable pulse oximetry and virtual ward models that intervene earlier and more effectively.

 

Trust, governance, and public confidence

Proactive population health must be trusted to work.

CIPHA operates within a clear governance framework, using pseudonymised data for secondary purposes and providing clear explanations of what data is transformed or removed.

Access is role-appropriate; there is no automated decision-making, and formal data sharing agreements and DPIAs support safe collaboration across partners.

This means that systems can act earlier while maintaining transparency and public confidence.

 

Conclusion: CIPHA as essential infrastructure for prevention

Reactive care treats the visible tip of the iceberg. The greatest opportunity for prevention sits below the surface.

Proactive care requires a whole-system view to identify risk early, particularly for those who fall outside traditional priority lists. By linking data across organisations and combining clinical, social, and demographic insight, CIPHA enables earlier, fairer interventions and measurable improvement in population health outcomes.

CIPHA is not just a reporting tool; it’s essential infrastructure for prevention-led care.

To learn how CIPHA can help your system identify the emerging risks and intervene earlier, contact Graphnet Health.