title: Combining individual-level discrete choice experiment estimates and costs to inform health care management decisions about customized care: the case of follow-up strategies after breast cancer treatment.
abstract: OBJECTIVE
  Customized care can be beneficial for patients when preferences for health care programs are heterogeneous. Yet, there is little guidance on how individual-specific preferences and cost data can be combined to inform health care decisions about customized care. Therefore, we propose a discrete choice experiment-based approach that illustrates how to analyze the cost-effectiveness of customized (and noncustomized) care programs to provide information for hospital managers.
  
  
  METHODS
  We exploit the fact that choice models make it possible to determine whether preference heterogeneity exists and to obtain individual-specific parameter estimates. We present an approach of how to combine these individual-specific parameter estimates from a random parameter model (mixed logit model) with cost data to analyze the cost-effectiveness of customized care and demonstrate our method in the case of follow-up after breast cancer treatment.
  
  
  RESULTS
  We found that there is significant preference heterogeneity for all except two attributes of breast cancer treatment follow-up and that the fully customized care program leads to higher utility and lower costs than the current standardized program. Compared with the single alternative program, the fully customized care program has increased benefits and higher costs. Thus, it is necessary for health care decision makers to judge whether the use of resources for customized care is cost-effective.
  
  
  CONCLUSIONS
  Decision makers should consider using the results obtained from our methodological approach when they consider implementing customized health care programs, because it may help to find ways to save costs and increase patient satisfaction.
authors.affiliation: Institute of Health Policy & Management, Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, The Netherlands. benning@bmg.eur.nl
authors.forename: Tim M
authors.initials: TM
authors.lastname: Benning
authors.forename: Merel L
authors.initials: ML
authors.lastname: Kimman
authors.forename: Carmen D
authors.initials: CD
authors.lastname: Dirksen
authors.forename: Liesbeth J
authors.initials: LJ
authors.lastname: Boersma
authors.forename: Benedict G C
authors.initials: BG
authors.lastname: Dellaert
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000191:economics / Q000628:therapy*; D002755:Choice Behavior; D003362:Cost-Benefit Analysis; D003657:Decision Making; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D008962:Models, Theoretical; D057240:Patient Preference; D057285:Precision Medicine / Q000191:economics / Q000379:methods*