title: The role of the expected value of individualized care in cost-effectiveness analyses and decision making.
abstract: OBJECTIVE
  To explore the feasibility and potential role of the expected value of individualized care (EVIC) framework.
  
  
  METHODS
  The EVIC quantifies how much benefits are forgone when a treatment decision is based on the best-expected outcomes in the population rather than in the individual patient. We have reviewed which types of patient-level attributes contribute to the EVIC and how they affect the interpretation of the outcomes. In addition, we have applied the EVIC framework to the outcomes of a microsimulation-based cost-effectiveness analysis for glaucoma treatment.
  
  
  RESULTS
  For EVIC outcomes to inform decisions about clinical practice, we need to calculate the parameter-specific EVIC of known or knowable patient-level attributes and compare it with the real costs of implementing individualized care. In the case study, the total EVIC was €580 per patient, but patient-level attributes known at treatment decision had minimal impact. A subgroup policy based on individual disease progression could be worthwhile if a predictive test for glaucoma progression could be developed and implemented for less than €130 per patient.
  
  
  CONCLUSIONS
  The EVIC framework is feasible in cost-effectiveness analyses and can be informative for decision making. The EVIC outcomes are particularly informative when they are (close to) zero. When the EVIC has a high value, implications depend on the type of patient-level attribute. EVIC can be a useful tool to identify opportunities to improve efficiency in health care by individualization of care and to quantify the maximal investment opportunities for implementing subgroup policy.
authors.affiliation: University Eye Clinic, Maastricht University Medical Center, Maastricht, The Netherlands.
authors.forename: Aukje
authors.initials: A
authors.lastname: van Gestel
authors.forename: Janneke
authors.initials: J
authors.lastname: Grutters
authors.forename: Jan
authors.initials: J
authors.lastname: Schouten
authors.forename: Carroll
authors.initials: C
authors.lastname: Webers
authors.forename: Henny
authors.initials: H
authors.lastname: Beckers
authors.forename: Manuela
authors.initials: M
authors.lastname: Joore
authors.forename: Johan
authors.initials: J
authors.lastname: Severens
mesh_terms: D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D003657:Decision Making; D018450:Disease Progression; D005901:Glaucoma / Q000191:economics* / Q000503:physiopathology / Q000628:therapy*; D006801:Humans; D018803:Models, Economic; D057240:Patient Preference; D012107:Research Design