Framework
The intervention value model
A shared structure for exploring how interventions affect events, cost, and value.
Core question
The central question is what would need to be true for an intervention to generate value in a real system.
The key question is not simply whether an intervention sounds worthwhile, but what would need to be true for it to generate value in a real system.
The lab is designed to help structure an intervention idea, translate it into explicit assumptions, and clarify the conditions required for value to appear before ideas harden into static business cases.
Seven routes to system value
Prevent Need
Reduce avoidable events before they happen.
Detect Earlier
Shift patients into earlier and less costly states.
Stabilise Risk
Reduce deterioration and unplanned activity.
Improve Access
Reduce delay, backlog, and misallocation.
Redesign Flow
Remove inefficiency within pathways.
Shift Care Setting
Substitute higher-cost care with lower-intensity care.
Improve Decisions
Improve triage, prioritisation, and clinical choices.
Read more about the seven routes↓
Why the routes matter
Most healthcare transformation strategies create value through a small number of recurring mechanisms. The routes help make that logic more explicit, so teams can ask not just whether an intervention sounds promising, but how it is expected to generate value.
1. Prevent Need
This route focuses on reducing avoidable events before they happen. The value case depends on whether the cost of prevention is offset by meaningful reductions in downstream acute activity.
2. Detect Earlier
This route creates value by shifting patients into earlier, less acute, and potentially less costly states. The key question is whether earlier detection genuinely changes outcomes and total pathway cost.
3. Stabilise Risk
This route focuses on proactive management to reduce deterioration and unplanned activity. The value logic is about preventing crisis events and avoiding the highest-cost parts of the system.
4. Improve Access
This route focuses on reducing delay, backlog, and friction in access to care. The strategic question is whether an intervention creates real system value or simply increases throughput without reducing downstream pressure.
5. Redesign Flow
This route aims to remove inefficiency within existing care pathways. The focus is not just on doing more, but on changing the pathway so that activity, follow-up burden, or bed use are reduced.
6. Shift Care Setting
This route generates value by substituting higher-cost care with lower-intensity alternatives where appropriate. The value case depends on whether care can genuinely be shifted without recreating cost elsewhere.
7. Improve Decisions
This route focuses on improving triage, prioritisation, and clinical choice. It is often cross-cutting, because better decisions can improve how patients are routed, matched, and managed across several other routes.
Why categorising value helps
Categorising an intervention by route helps make its value logic more testable. It moves the conversation from a general improvement ambition to a clearer statement of how the intervention is actually expected to create system value.
The intervention value model
A shared structure for testing thresholds, assumptions, and where a case is strong or fragile.
Across the lab, value is explored through a shared model. A population is defined, baseline risk is estimated, an intervention is applied, and the effect on events, system savings, and delivery cost is examined.
This creates a consistent way to test thresholds, compare assumptions, and see where a case is strong or fragile.
Population
Define the eligible population and the proportion reached.
Risk
Estimate baseline event risk without intervention.
Effect size
Model the expected reduction in events.
Impact and value
Translate events avoided into savings, cost, and net value.
Population
Define the eligible population and the proportion reached.
Risk
Estimate baseline event risk without intervention.
Effect size
Model the expected reduction in events.
Impact and value
Translate events avoided into savings, cost, and net value.
Read more about the intervention value model↓
1. Defining scale: population and coverage
The model begins with the population at risk: the total group eligible for an intervention. Because no service reaches every patient, the model then applies an intervention coverage assumption, representing the realistic proportion of that population the programme is expected to reach.
Together, these assumptions define the practical ceiling for the intervention’s impact.
2. Measuring potential: baseline risk and effect size
Once scale is defined, the model considers baseline event risk: the expected frequency of the negative outcome without intervention. That might be a fall, an admission, a crisis event, or another form of system activity.
Effect size then represents the anticipated reduction in those events attributable to the intervention. This allows users to test whether value depends more on targeting high-risk groups, achieving a strong effect, or some combination of both.
3. Calculating impact: events avoided and gross savings
The interaction between scale, risk, and effect produces the modelled number of events avoided. This is the primary system impact measure.
To translate that impact into economic terms, the model applies a cost per event. This produces gross system savings: the estimated avoided cost associated with reducing activity.
4. The economic reality: intervention cost and net value
The model then introduces intervention cost, representing the resource required to deliver the programme at the chosen scale.
Net value is the result of gross savings minus intervention cost. This gives a bottom-line view of whether the intervention appears cost-saving, cost-neutral, or likely to require net investment under the selected assumptions.
Why this matters for strategy
The purpose of the model is not to produce a single definitive answer. It is to create a decision sandbox in which strategy teams can test assumptions, explore break-even points, and clarify the specific conditions required for value to appear.
In practice, that means making it easier to ask better questions earlier: how much effect is needed, how much cost is tolerable, where the fragility sits, and what should be validated next.
From idea to decision
The same decision structure is applied across different intervention contexts in the live sandbox library.
The framework is applied across the live library of sandboxes. Each module explores a different intervention context, such as falls prevention, earlier diagnosis, waiting list strategy, pathway redesign, frailty support, or cardiovascular prevention.
What changes from module to module is the intervention context. What stays the same is the decision structure: assumptions, system impact, thresholds, uncertainty, and interpretation.
Boundaries of use
These tools are exploratory and illustrative. They are designed to support earlier-stage thinking and make assumptions more transparent, not to replace formal economic evaluation or local validation.