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Deriving optimal value from each system
Author(s) -
Muir Gray,
Mara Airoldi,
Gwyn Bevan,
Peter McCulloch
Publication year - 2017
Publication title -
journal of the royal society of medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 81
eISSN - 1758-1095
pISSN - 0141-0768
DOI - 10.1177/0141076817711090
Subject(s) - computer science , value (mathematics) , data science , machine learning
From a population perspective, the first stage in optimising value is the resource allocation process. Allocative value is optimised when it is not possible to switch resources from one budget to another and get more health for the population as a whole. As emphasised in a previous article, resources are traditionally allocated to institutions, to health centres and hospitals for example, but increasingly resource allocation to different subgroups of the population is coming up the agenda, driven in no small part by the Commissioning for Value Packs of NHS RightCare. Allocating resource to programmes allows a much clearer understanding of what happens when resources are switched from one programme to another, using the method called marginal analysis the origin of which is entertainingly described in the free RAND book called How much is enoug

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