Cost-Effectiveness Analysis for SaMD and AIaMD
A cost-effectiveness analysis (CEA) is a more complex economic evaluation where the financial impact and health effects of an intervention are assessed relative to the comparator(s).
The common rationale for conducting these analyses is when the new intervention has a proven higher clinical benefit than an alternative(s) but is likely to be more expensive. Therefore, NICE recommends that digital health technologies be supported by a CEA when they are considered to pose a high financial risk, as stated in Standard 18 of the Evidence Standards Framework for Digital Health Technologies.
The CEA approach provides evidence of an intervention's value for money since both clinical and financial measures are included. Typical clinical measures for a CEA are life years, quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs). Since CEAs account for the impact interventions have on outcomes such as survival, model time horizons are often assumed to be equivalent to patient lifetime.
Using a CEA, an incremental cost-effectiveness ratio (ICER) can be derived: ICER = CostA- CostB / EffectA- EffectB. This ICER is used by health technology assessment bodies to determine whether an intervention is cost-effective or not. For example, in the UK NICE deem most interventions to be cost-effective if they generate an additional QALY for a cost under £20,000 to £30,000.
Best Practices for Cost-Effectiveness Analysis
Align with internationally recognised standards.
There are six key steps in the development of a cost-effectiveness analysis:
Evidence review: systematically review relevant clinical and economic published literature.
Conceptualisation (2 parts): firstly, using findings from the evidence review identify the decision problem using the PICO framework. Secondly, choose the optimal analytical method e.g. decision tree or Markov model.
Data gathering: validate the conceptual framework of the evaluation through key opinion stakeholders’ engagement and extract data from clinical and real-world studies.
Model build: create model in the preferred modelling software e.g. Microsoft Excel.
Model results: analyse the results of the model.
Accounting for uncertainty: test the model results against uncertainty using sensitivity analysis.
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