OBJECTIVE: Multivariate statistical methods were used to identify patient-related variables that predicted length of stay in a single psychiatric facility. The study investigated whether these variables remained stable over time and could be used to provide individual physicians with data on length of stay adjusted for differences in clinical caseloads and to detect trends in the physicians' practice patterns. METHODS: Data on all patients discharged over two six-month periods were collected at an acute psychiatric inpatient facility. Stepwise multiple regression analyses were conducted on the two datasets. RESULTS: The results from both analyses revealed that five variables significantly predicted length of stay and were stable over time. They were a primary diagnosis of schizophrenia, the number of previous admissions, a primary diagnosis of a mood disorder, age, and a secondary diagnosis of an alcohol- or other drug-related disorder. For some physicians, the mean length of stay of their patients differed significantly from the length predicted by the regression model--generally, it was shorter. CONCLUSIONS: The results demonstrate that patient-related predictors of length of stay in a single psychiatric hospital can be identified using relatively simple statistical procedures and can be consistent across a large dataset and over time.
OBJECTIVE: Multivariate statistical methods were used to identify patient-related variables that predicted length of stay in a single psychiatric facility. The study investigated whether these variables remained stable over time and could be used to provide individual physicians with data on length of stay adjusted for differences in clinical caseloads and to detect trends in the physicians' practice patterns. METHODS: Data on all patients discharged over two six-month periods were collected at an acute psychiatric inpatient facility. Stepwise multiple regression analyses were conducted on the two datasets. RESULTS: The results from both analyses revealed that five variables significantly predicted length of stay and were stable over time. They were a primary diagnosis of schizophrenia, the number of previous admissions, a primary diagnosis of a mood disorder, age, and a secondary diagnosis of an alcohol- or other drug-related disorder. For some physicians, the mean length of stay of their patients differed significantly from the length predicted by the regression model--generally, it was shorter. CONCLUSIONS: The results demonstrate that patient-related predictors of length of stay in a single psychiatric hospital can be identified using relatively simple statistical procedures and can be consistent across a large dataset and over time.
Authors: Antonella Gigantesco; Giovanni de Girolamo; Giovanni Santone; Rossella Miglio; Angelo Picardi Journal: BMC Public Health Date: 2009-08-22 Impact factor: 3.295