Widely used in various educational and vocational assessment applications, computerized adaptive
testing (CAT) has recently begun to be used to measure patient-reported outcomes Although successful in reducing respondent burden, most current CAT algorithms do not formally consider it as part of the item selection process. This study used a loss function approach motivated by decision theory to develop an item selection method that incorporates respondent burden into the item selection process based on maximum Fisher information item selection. Several different loss functions placing varying degrees of importance on respondent burden were compared, using an item bank of 62 polytomous items measuring depressive symptoms. One dataset consisted of the real responses from the 730 subjects who responded to all the items. A second dataset consisted of simulated responses to all the items based on a grid of latent trait scores with replicates at each grid point. The algorithm enables a CAT administrator to more efficiently control the respondent burden without severely affecting the measurement precision than when using MFI alone. In particular, the loss function incorporating respondent burden protected respondents from receiving longer tests when their estimated trait score fell in a region where there were few informative items.