IAPN: a simple framework for evaluating whether a population-based risk stratification tool will be cost-effective before implementation

Abstract

Abstract Risk prediction tools are widely used in healthcare to identify individuals at high risk of adverse events who may benefit from proactive interventions. Traditionally, these tools are evaluated primarily on statistical performance measures—such as sensitivity, specificity, discrimination, and positive predictive value (PPV)—with minimal attention given to their cost-effectiveness. As a result, while many published tools report high performance statistics, evidence is limited on their real-world efficacy and potential for cost savings. To address this gap, we propose a straightforward framework for evaluating risk prediction tools during the design phase, which incorporates both PPV and intervention effectiveness, measured by the number needed to treat (NNT). This framework shows that to be cost-effective, the per-unit cost of an intervention (I) must be less than the average cost of the adverse event (A) multiplied by the PPV-to-NNT ratio: I < A*PPV/NNT. This criterion enables decision-makers to assess the economic value of a risk prediction tool before implementation.

Keywords

• Risk prediction
• Positive predictive value
• Number needed to treat
• Cost
• Intervention
• Design stage evaluation