Anirban Basu1, David Meltzer. 1. Section of General Internal Medicine, Department of Medicine, Center for Health and Social Sciences, University of Chicago, Chicago, Illinois 60637, USA. abasu@medicine.bsd.uchicago.edu
Abstract
BACKGROUND: Cost-effectiveness analysis traditionally focuses on identifying when treatments are cost-effective based on their average benefits and costs in the population. However, there may be considerable value in identifying when treatments are cost-effective for individual patients given their preferences or other personal attributes. OBJECTIVES: To present a theoretical framework to assess the potential value of identifying cost-effective treatments for individual patients given their preferences and to compare the value of individualized treatment decisions with the value of treatment decisions based on traditional population-level cost-effectiveness analysis. Methods . The authors calculate the expected value of individualized care (EVIC), which represents the potential value of providing physicians information on the preferences of individual patients, such as quality-of-life (QOL) weights, so as to make individualized treatment decisions. They also show how EVIC varies with insurance structures that do not internalize relative costs of treatments. They illustrate this theory using an example in which physicians make treatment choices for 65-year-old prostate cancer patients. RESULTS: The value of identifying cost-effective treatments at the individual level for 65-year-old prostate cancer patients in the United States is about $70 million annually. This is more than 100 times the $0.7 million annual value of identifying the cost-effective treatment on average for this population. However, failure to internalize costs almost eliminates the value of individualized care. CONCLUSIONS: The value of individualizing care can be far greater than the value of improved decision making at the group level. However, this can vary immensely with insurance. EVIC can provide a guide as to when the high value of individualized care may make population-level decision making especially at risk of providing poor guidance for coverage decisions. Future studies of the value of individualized care should also consider baseline levels of individualization of care.
BACKGROUND: Cost-effectiveness analysis traditionally focuses on identifying when treatments are cost-effective based on their average benefits and costs in the population. However, there may be considerable value in identifying when treatments are cost-effective for individual patients given their preferences or other personal attributes. OBJECTIVES: To present a theoretical framework to assess the potential value of identifying cost-effective treatments for individual patients given their preferences and to compare the value of individualized treatment decisions with the value of treatment decisions based on traditional population-level cost-effectiveness analysis. Methods . The authors calculate the expected value of individualized care (EVIC), which represents the potential value of providing physicians information on the preferences of individual patients, such as quality-of-life (QOL) weights, so as to make individualized treatment decisions. They also show how EVIC varies with insurance structures that do not internalize relative costs of treatments. They illustrate this theory using an example in which physicians make treatment choices for 65-year-old prostate cancerpatients. RESULTS: The value of identifying cost-effective treatments at the individual level for 65-year-old prostate cancerpatients in the United States is about $70 million annually. This is more than 100 times the $0.7 million annual value of identifying the cost-effective treatment on average for this population. However, failure to internalize costs almost eliminates the value of individualized care. CONCLUSIONS: The value of individualizing care can be far greater than the value of improved decision making at the group level. However, this can vary immensely with insurance. EVIC can provide a guide as to when the high value of individualized care may make population-level decision making especially at risk of providing poor guidance for coverage decisions. Future studies of the value of individualized care should also consider baseline levels of individualization of care.
Authors: Mark Helfand; Sean Tunis; Evelyn P Whitlock; Stephen G Pauker; Anirban Basu; Jon Chilingerian; Frank E Harrell; David O Meltzer; Victor M Montori; Donald S Shepard; David M Kent Journal: Clin Transl Sci Date: 2011-06 Impact factor: 4.689
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: Rena Conti; David L Veenstra; Katrina Armstrong; Lawrence J Lesko; Scott D Grosse Journal: Med Decis Making Date: 2010-01-04 Impact factor: 2.583