Portfolio frontier analysis: Applying mean-variance analysis to health technology assessment for health systems under pressure.

Abstract

The COVID-19 pandemic is challenging how healthcare technologies are evaluated, as new, more dynamic methods are required to test the cost effectiveness of alternative interventions during use rather than before initial adoption. Currently, health technology assessment (HTA) tends to be static and a priori: alternatives are compared before launch, and little evaluation occurs after implementation. We suggest a method that builds upon the current pre-launch HTA procedures by conceptualizing a mean-variance approach to the continuous evaluation of attainable portfolios of interventions in health systems. Our framework uses frontier analysis to identify the desirability of available health interventions so decision makers can choose diverse portfolios based upon information about expected returns and risks. This approach facilitates the extension of existing methods and assessments beyond the traditional concern with pre-adoption data, a much-needed innovation given the challenges posed by COVID-19.