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.
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: Janneke P C Grutters; Mark Sculpher; Andrew H Briggs; Johan L Severens; Math J Candel; James E Stahl; Dirk De Ruysscher; Albert Boer; Bram L T Ramaekers; Manuela A Joore Journal: Pharmacoeconomics Date: 2013-02 Impact factor: 4.981
Authors: An Tran-Duy; Annelies Boonen; Wietske Kievit; Piet L C M van Riel; Mart A F J van de Laar; Johan L Severens Journal: Pharmacoeconomics Date: 2014-10 Impact factor: 4.981