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Stage 4: Measure and drive the impact we're aiming for
Building continuous improvement into prevention models
For targeted proactive prevention at scale, the follow-up stage transforms initial interventions into sustained impact. This critical phase establishes systematic feedback mechanisms that validate effectiveness, identify adjustments needed, and build the evidence base for scaling.
On this page you can find out:
- the steps involved in this stage
- case studies from local authorities already doing this in practice
- implications for evidencing prevention
- potential barriers you might face and how to overcome these
Local authorities implementing successful prevention models consistently emphasise two complementary approaches:
Step 1: Follow up with individuals
Effective follow-up processes track individual progress while identifying both successes and implementation barriers. This allows for timely adaptations to support plans and provides valuable insights for service improvement.
Key elements of effective follow-up:
Gathering structured feedback on which interventions have been accessible and effective creates a comprehensive picture of real-world implementation challenges and successes.
Tracking progress against SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound) provides concrete measures of advancement toward independence and improved wellbeing.
Systematically assessing when individuals are prepared to transition to independence or community-based support ensures resources remain focused where they deliver maximum impact.
Local authorities typically implement one of two follow-up approaches based on individual needs and available resources:
Involves scheduled check-ins to assess intervention access, effectiveness, and outcomes. For example, after connecting an individual to falls prevention services, a follow-up call might identify transport barriers that weren't apparent during initial assessment, allowing for targeted solutions that preserve intervention effectiveness.
This approach is particularly valuable when:
- Trust needs to be reinforced after previous negative service experiences
- Complex needs require nuanced understanding
- Practical barriers might prevent engagement with services
Utilise peer networks, digital tools, or light-touch professional oversight to monitor progress while encouraging independence. For instance, community groups might implement buddy systems with regular wellbeing check-ins, or digital applications might track self-reported outcome measures with escalation pathways for concerning changes.
This approach is most appropriate when:
- Independence is a principal intervention goal
- Digital or peer support options align with individual preferences
- Resource constraints necessitate efficient monitoring approaches
Step 2: Use evidence to iterate the model
Leading authorities establish formal mechanisms to aggregate individual-level feedback into actionable system insights. This process transforms anecdotal evidence into robust improvement cycles.
Key approaches to system learning:
Regular practitioner forums, such as monthly learning sessions, create opportunities to identify patterns across individual cases. For example, multiple reports of transport barriers might trigger system-level partnerships with community transport schemes to align schedules with prevention programmes.
Combining qualitative feedback with quantitative metrics creates a comprehensive evidence base for both operational adjustments and strategic decisions.
Improvement cycle options:
Local authorities typically adopt one of two improvement approaches based on their organisational context:
Utilise real-time data and regular review meetings to implement frequent, incremental adjustments. This approach enables rapid response to emerging issues but requires robust data systems and analytical capacity.
Conduct comprehensive reviews at set intervals, allowing for more detailed analysis and large-scale adjustments. While this approach may delay some improvements, it often enables more fundamental system changes.
Many successful authorities combine elements of both approaches, using operational dashboards for weekly adjustments while conducting quarterly strategic reviews to reassess the overall prevention model.
Stage 1: Data collection
Accurate, timely, and complete data capture is essential for improvement. Local authorities are encouraged to focus on collecting actionable data rather than expending effort on compiling incomplete or inconsistent datasets.
Example: A council improving referral throughput automates data collection through case management systems. Key metrics include:
- Referral volumes.
- Time taken to process referrals.
- Outcomes for residents accessing prevention services.
Automation reduces manual effort, ensuring reliable and actionable data.
Stage 2: Data analysis
Data analysis can generate insights that guide decisions. Dashboards and clear reports are useful to highlight trends and support rapid, evidence-based actions.
Example: Dashboards track metrics like referral volumes and time-to-triage. Weekly reports help the prevention team:
- Spot delays or bottlenecks.
- Compare performance to targets, such as processing referrals within 48 hours.
- Identify trends in demand.
This approach ensures decisions are grounded in facts, not assumptions.
Stage 3: Continuous improvement meetings
Regular meetings translate data insights into action. These meetings should emphasise accountability, resource allocation, and progress tracking.
Example: Weekly meetings review key metrics and prioritise actions. For instance, if triage delays are identified, the team might:
- Temporarily increase staffing levels.
- Adjust workflows to improve response times.
Clear responsibilities and deadlines are assigned for follow-up.
Stage 4: Problem solving
Problem solving is central to continuous improvement. Councils can benefit from building a culture of feedback where teams have the tools and skills to address issues sustainably.
Example: Using root cause analysis, the team identifies that incomplete referral forms are delaying triage. Solutions include revising the form to make it user-friendly and providing training to referring partners. Regular feedback ensures sustainable improvements.
Barriers and mitigations to implementing this stage of the delivery model
Limited Capacity for Follow-Ups and Ongoing Support
- Challenge: workforce constraints, high caseloads, and limited funding may prevent authorities from maintaining regular follow-ups with individuals. Without ongoing engagement, some people may disengage from support, reducing the impact of prevention efforts.
- Mitigation: authorities overcoming this issue implement a tiered follow-up model, combining hands-on support for high-need individuals with lower-intensity approaches like digital check-ins, peer-led follow-ups, or self-management tools. They strengthen partnerships with VCFSE organisations to extend follow-up capacity and build sustainable support networks, potentially through workshop approaches with VCSE and families using tools such as Working/Not Working or the 4plus1 questions approach.
Limited Capacity of Local Services
- Challenge: local services, including statutory, VCFSE and support provider organisations, may face resource constraints, making it difficult to meet the growing demand for support, particularly when multiple individuals require tailored services simultaneously. This can result in delays or reduced quality of support.
- Mitigation: authorities overcoming this challenge strengthen partnerships with VCFSE and support provider organisations to increase service capacity and flexibility. They identify and address service gaps through proactive mapping and engage in resource-sharing agreements. They explore alternative delivery models such as peer support, community-led initiatives, or digital platforms to supplement traditional services. Additionally, they align capacity planning with predicted demand to ensure adequate resources are available, and prioritise services based on the most critical needs and build in flexibility for high-demand periods.
Lack of Systematic Data Collection and Evidence Use
- Challenge: inconsistent or incomplete data collection may limit the ability to track outcomes, assess intervention effectiveness, and refine prevention models. Staff may also lack the time, training, or tools to collect and analyse meaningful evidence.
- Mitigation: authorities overcoming this challenge embed automated data collection within case management systems to reduce manual workload. They train staff on data-informed decision-making, ensuring they can interpret and apply insights effectively. They use AI-driven analytics to streamline trend analysis, providing real-time insights into intervention effectiveness.
Difficulty in Defining and Measuring Prevention Impact
- Challenge: unlike acute services, prevention outcomes are often long-term and indirect, making it difficult to quantify success. Authorities may struggle to build a robust business case for prevention without clear impact metrics.
- Mitigation: authorities overcoming this issue develop a clear prevention outcomes framework, incorporating both short-term indicators (e.g., service uptake, improved wellbeing) and long-term impact measures (e.g., reduced hospital admissions, increased independence). They pilot SMART goal tracking to assess individual progress, while using system-level data to demonstrate cost-effectiveness and long-term benefits.
By proactively addressing these barriers, local authorities can strengthen their targeted, proactive prevention approach - ensuring individuals receive sustained, impactful support while continuously refining the model to maximise long-term outcomes.
Implications of this stage for evidencing prevention
Robust measurement and improvement systems significantly strengthen the prevention evidence base by providing:
- Comprehensive baseline data: Establishing clear starting points for demand, expenditure, and outcomes creates the foundation for demonstrating impact.
- Verifiable improvement trajectories: Systematic tracking of key indicators provides compelling evidence of positive change attributable to interventions.
- Implementation insights: Understanding operational effectiveness and delivery challenges enables refinement of the prevention model and enhances replicability.
- Partnership value demonstration: Evidence of cross-system impact strengthens the case for continued engagement and resource alignment from key stakeholders.
Summary of key points
- Ensure consistent follow-up: Develop tailored follow-up approaches that balance hands-on support with self-led management, meeting individual needs while building resilience.
- Embed a culture of continuous learning: Foster adaptability by embedding continuous improvement cycles and leveraging real-time data and staff insights.
- Balance agility and rigour: Combine iterative refinement with periodic in-depth evaluations to create a sustainable and evidence-driven model.
- Collaborate early and often: Work with partners to share evidence, align goals, and plan next steps collaboratively, ensuring a unified approach to scaling prevention efforts.
- Leverage evidence to drive sustainability: Use the insights gained to demonstrate the impact of prevention efforts, supporting funding and partner buy-in to scale interventions effectively.
