On experts and priorities

Priorización de intervenciones sanitarias. Revisión de criterios, enfoques y rol de las agencias de evaluación

Often I hear that prioritisation of benefits could be solved by evaluation agencies and the appropriate application of cost-effectiveness analysis. As times goes by, I'm convinced that this is a way to avoid if we consider how priorities should be set. In other words, leaving this issue to a technical perspective is not enough. There is a need for a deliberative way to tackle the complexities of prioritisation.
Anyway, if you want to know a review that takes as given the experts view, check this article. If you want to understand the whole issue from a broader perspective, then read the book I quoted in this post some months ago.

 Carlos Diaz at Sala Parés

Challenges in Cost-Effectiveness Analysis of genomic tests

From: Cost-effectiveness analyses of genetic and genomic diagnostic tests (Must read)

Type of challenge	Example of challenge	Description of challenge
Methodological	Selecting the appropriate evaluative framework	Is the standard extra-welfarist view and use of CEA appropriate, or should the distinct theoretical approach reflecting the welfarist view and use of CBA be adopted to allow consequences other than health gain, such as the value of diagnostic information from the genomic-targeted diagnostic test, to be valued?
	Relevant study perspective	Is the standard recommendation to focus on the use of health-care services appropriate when the genomic-targeted diagnostic test may provide information that affects the use of other services, such as education or employment?
	Relevant time horizon	Is a lifetime sufficient when the impact of a genomic-targeted diagnostic test may extend to infinite time horizons that are not limited by the lifespan of one individual?
	Defining the relevant study population	Is the standard definition of a patient (the person receiving the technology) appropriate when there could be spillover effects to family members (currently alive or to be born) as a result of information from a genomic-targeted diagnostic test?
	Valuing consequences	Is identifying and measuring the impact on health status alone sufficient to capture the (good and bad) consequences of a genomic-targeted diagnostic test?
Technical	Variation in the individual characteristics of the relevant study population	The use of cohort state transition Markov models, sometimes combined with decision trees, cannot easily capture the impact of individual patient variation within a population with different genotypes and phenotypes
	Number of diagnostic and, if appropriate, subsequent treatment pathways	The use of cohort state transition Markov models, sometimes combined with decision trees, cannot easily account for multiple comparators often needed when evaluating a new genomic-targeted diagnostic test
	Capturing impact of reduced time to diagnosis	The use of cohort state transition Markov models, sometimes combined with decision trees, cannot account for the impact of reduced time to achieve a diagnosis, which is often a proposed benefit of a genomic-targeted diagnostic test
	Capturing impact of capacity constraints	Decision analytic model-based CEA currently assumes limitless capacity within health-care systems, which is often not a reasonable assumption when introducing a genomic-targeted diagnostic test to populations for whom a diagnosis was not previously available
Practical	Availability of data	There is often a lack of data available to populate decision analytic model-based CEA
	National tariff of test cost	No national tariff for genomic-targeted tests exist
Organizational	Complex health-care systems	Decision analytic model-based CEA assumes that money saved and benefits accrued are transferable, but this is often challenging in complex health-care systems that comprise an overarching funding mechanism (public, private, insurance), a service and staffing model for providing care for different sectors (community, general practice, hospital, specialist) and a means of allocating funding to these different sectors
	Generalizability of results	Decision analytic model-based CEA is relevant only to the defined decision problem, and decision-makers who want to use the results must decide whether the focus of the analysis is relevant to their own jurisdiction
	Expensive nature of health technology assessment	Decision analytic model-based CEA conducted within national health technology assessment processes requires considerable funding and expertise that are not available to all, which may contribute to the inequity in access to new genomic-targeted diagnostic tests across the world

1. CBA, cost-benefits analysis; CEA, cost-effectiveness analysis.