title: Value of information analysis in healthcare: a review of principles and applications.
abstract: BACKGROUND
  Economic evaluations are increasingly utilized to inform decisions in healthcare; however, decisions remain uncertain when they are not based on adequate evidence. Value of information (VOI) analysis has been proposed as a systematic approach to measure decision uncertainty and assess whether there is sufficient evidence to support new technologies.
  
  
  METHODS
  The objective of this paper is to review the principles and applications of VOI analysis in healthcare. Relevant databases were systematically searched to identify VOI articles. The findings from the selected articles were summarized and narratively presented.
  
  
  RESULTS
  Various VOI methods have been developed and applied to inform decision-making, optimally designing research studies and setting research priorities. However, the application of this approach in healthcare remains limited due to technical and policy challenges.
  
  
  CONCLUSIONS
  There is a need to create more awareness about VOI analysis, simplify its current methods, and align them with the needs of decision-making organizations.
authors.affiliation: Griffith Health Institute, Griffith University, Gold Coast, QLD, Australia, and Centre for Applied Health Economics, School of Medicine, Griffith University , Meadowbrook, QLD , Australia.
authors.forename: Haitham W
authors.initials: HW
authors.lastname: Tuffaha
authors.forename: Louisa G
authors.initials: LG
authors.lastname: Gordon
authors.forename: Paul A
authors.initials: PA
authors.lastname: Scuffham
mesh_terms: D035843:Biomedical Research / Q000191:economics / Q000458:organization & administration*; D003362:Cost-Benefit Analysis; D003657:Decision Making; D015276:Health Care Rationing; D006302:Health Services Research / Q000191:economics / Q000458:organization & administration*; D006801:Humans; D018803:Models, Economic; D012107:Research Design
keywords: Decision uncertainty; Decision-making; Research prioritization; Trial design; Value of information