Overview: Cardiovascular disease (CVD) remains a major cause of premature mortality, morbidity, and healthcare utilisation across Wales, accounting for a substantial proportion of avoidable NHS demand despite the availability of effective preventive interventions. Population health analytics identified a significant gap in the early detection and management of cardiovascular risk, with many high-risk individuals either remaining undiagnosed or not receiving evidence-based lipid-lowering therapies and risk-reduction measures.
In response, the TriTech Institute, in partnership with Swansea University, Swansea Bay University Health Board, and Amgen, developed an innovative, data science enabled population health programme designed to translate risk intelligence into targeted clinical action. Harnessing large scale linked health data and advanced population analytics, the programme systematically identified individuals at elevated cardiovascular risk and prioritised them for proactive review through pharmacist-led intervention clinics. This approach enabled timely optimisation of treatment pathways, improved adherence to clinical guidelines, and facilitated earlier preventive intervention.
The programme demonstrates how the integration of population health intelligence, academic expertise, and clinical innovation can support more proactive, community-based models of care, improving outcomes for patients while reducing the long-term burden of cardiovascular disease on the healthcare system.
“Data-driven risk stratification enables healthcare systems to move from reactive treatment to proactive prevention, identifying high-risk patients before adverse events occur.”
The challenge: Cardiovascular disease (CVD) remains a leading cause of death and ill-health in Wales, accounting for approximately 30% of all deaths, with ischaemic heart disease being the most common cause. CVD also places a significant burden on the healthcare system, contributing to 7% of all inpatient admissions and 9% of emergency admissions annually. Despite well-established clinical guidelines, significant variation remains between recommended standards of care and real-world delivery, resulting in missed opportunities for prevention and risk reduction.
- Growing Cardiovascular Risk: ASCVD and diabetes prevalence continue to rise across Wales, increasing the number of patients requiring long-term cardiovascular management and prevention.
- Significant Treatment Gaps: Use of lipid-lowering therapies, LDL-C monitoring, and achievement of treatment targets have declined, leaving many high-risk patients suboptimally managed.
- Missed Prevention Opportunities: Many patients experience a first heart attack or stroke despite having identifiable and treatable risk factors, highlighting the need for earlier intervention.
- Data-Driven Risk Stratification Needed: Advanced risk prediction tools, such as the REACT Score, can support earlier identification of high-risk patients, improve clinical decision-making, and enable more targeted, high-value cardiovascular care.
In short, the system was managing the consequences of cardiovascular disease rather than preventing its occurrence.
The approach: The Tritech Institute coordinated a two-phase programme designed to move from understanding the problem to testing solutions in real care settings.
1. Population-level insight: Using the SAIL databank, the team analysed health records across Wales to:
- Identify high-risk patient groups
- Understand trends in treatment and outcomes
- Quantify the gap between guidelines and delivery.
2. Targeted intervention in practice: The insights were translated into action through pilot CVD prevention clinics across:
- Secondary care hubs
- Community hubs
- GP practices.
Key features of the model:
- Pharmacist-led, multidisciplinary delivery
- Standardised treatment protocols (lipids and blood pressure)
- Regular monitoring over 12 months
- Integrated lifestyle and behavioural support
- Use of point-of-care testing for faster decision-making.
Patients were proactively identified and managed, rather than waiting for deterioration or events.
Findings
Risk is visible but not acted on early enough: Population health data identified clear unmet cardiovascular risk, yet treatment optimisation was not consistently delivered.
Treatment gaps are widening: Prescribing of lipid-lowering therapies and routine monitoring declined over time, even as disease prevalence increased.
Prevention requires focused resource: Dedicated clinic models achieved results that routine services struggled to deliver under pressure.
Risk stratification can be improved: The REACT tool demonstrated the value of more precise, personalised risk prediction, particularly in secondary prevention.
Outcomes
Clinical outcomes: Patients in the intervention clinics experienced measurable improvements:
- LDL-C cholesterol reduced by ~0.9–1.18 mmol/L
- Systolic blood pressure reduced by ~16–30 mmHg
- 81% achieved blood pressure control (<140/90 mmHg)
- 37% achieved LDL-C ≤1.8 mmol/L.
These changes are clinically significant, with evidence suggesting:
- ~20% reduction in 10-year CVD risk
- Lower likelihood of major cardiovascular events
Patient experience
- 69% completed the full 12-month programme
- Patients reported feeling supported, listened to, and more in control
- The service identified previously missed risks and prompted lifestyle change.
System and economic impact
- Cost-effective intervention model: Secondary care hub: ~£3,087 per QALY (well below NICE thresholds)
- Per-patient costs between £438 and £756 with strong lifetime value
- Demonstrated potential to reduce demand downstream by preventing acute events.
Operational learning
- Dedicated, ring-fenced services improve outcomes
- Integrated data and clinical delivery are essential
- Information governance and fragmented systems remain barriers to scale.
What This Means Going Forward
This programme provides a compelling example of how population health data can be translated into targeted clinical intervention at scale.
The next steps are clear:
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Scale Data-Driven Prevention Across Wales
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Embed Advanced Risk Prediction Tools, such as REACT, into Routine Clinical Practice
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Aligned with Wales’ Community by Design Programme, shift investment towards community-based prevention and earlier intervention
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Streamline Data Access and Governance through DHCW and local Digital teams to Accelerate Population Health analytics and Delivery.
The question is no longer whether this model works, but how rapidly it can be adopted at scale. Having demonstrated its effectiveness in practice, the next phase is to embed the approach as business as usual across Hywel Dda University Health Board, Swansea Bay University Health Board, and Cardiff and Vale University Health Board, creating a data-driven prevention model that identifies risk earlier, improves outcomes, and reduces the long-term burden of cardiovascular disease.