Predicting the discharge destination of rehabilitation patients using a signal detection approach

Objective: To predict the discharge destination of rehabilitation patients using signal detection analysis. Design: Cross-sectional and follow-up studies. Subject: The subjects were 324 patients discharged from a hospital in Fukuoka, Japan, between April 2005 and March 2006 and 313 patients discharged from tile same hospital between 1 April and 31 October 2006. Methods: The discharge destinations of the 324 patients were predicted using signal detection analysis. As a validation study, 7 variables identified in the first analysis were used to categorize 313 patients, organized retrospectively into 8 groups, and to calculate the home discharge rate in each group. Results: A patient's activities with respect to daily living, key person preference, dementia, age, route taken to hospitalization, residence before hospitalization, and gender were significant predictors of his or her discharge destination. Signal detection analysis established 8 subgroups, with 17.9-99.1% of the patients returning home after discharge. As a validation study, the actual and expected rates in the 8 subgroups were compared, and no significant difference was observed between the rates in any subgroup. Conclusion: Signal detection analysis is a useful technique for predicting the discharge destination of rehabilitation patients.