Discrete choice experiments for complex health-care decisions: Does hierarchical information integration offer a solution?
Health Economics , Volume 18 - Issue 8 p. 903- 920
This paper describes an application of hierarchical information integration (HII) discrete choice experiments. We assessed theoretical and construct validity, as well as internal consistency, to investigate whether HII can be used to investigate complex multi-faceted health-care decisions (objective 1). In addition, we incorporated recent advances in mixed logit modelling (objective 2). Finally, we determined the response rate and predictive ability to study the feasibility of HII to support health-care management (objective 3). The clinical subject was the implementation of the guideline for breast cancer surgery in day care, which is a complex process that involves changes at the organizational and management levels, as well as the level of health-care professionals and that of patients. We found good theoretical and construct validity and satisfactory internal consistency. The proposed mixed logit model, which included repeated measures corrections and subexperiment error scale variations, also performed well. We found a poor response, but the model had satisfactory predictive ability. Therefore, we conclude that HII can be used successfully to study complex multi-faceted health-care decisions (objectives 1 and 2), but that the feasibility of HII to support health-care management, in particular in challenging implementation projects, seems less favourable (objective 3).
|Discrete choice experiments, Hierarchical information integration, Integrated choice experiments, Multi-faceted health-care decisions|
|Organisation||Erasmus Research Institute of Management|
van Helvoort-Postulart, D, Dellaert, B.G.C, van der Weijden, T, von Meyenfeldt, M.F, & Dirksen, C.D. (2009). Discrete choice experiments for complex health-care decisions: Does hierarchical information integration offer a solution?. Health Economics, 18(8), 903–920. doi:10.1002/hec.1411