Objectives
To examine whether discharge destination is a useful predictor
variable for the length of admission within psychiatric intensive care
units (PICUs).
Methods
A clinician-led process separated PICU admissions by discharge
destination into three types and suggested other possible variables
associated with length of stay. Subsequently, a retrospective study
gathered proposed predictor variable data from a total of 368 admissions
from four PICUs. Bayesian models were developed and analysed.
Results
Clinical patient-type grouping by discharge destination displayed
better intraclass correlation (0.37) than any other predictor variable
(next highest was the specific PICU to which a patient was admitted
(0.0585)). Patients who were transferred to further secure care had the
longest PICU admission length. The best model included both patient type
(discharge destination) and unit as well as an interaction between those
variables.
Discussion
Patient typing based on clinical pathways shows better predictive
ability of admission length than clinical diagnosis or a specific tool
that was developed to identify patient needs. Modelling admission
lengths in a Bayesian fashion could be expanded and be useful within
service planning and monitoring for groups of patients.
Conclusion
Variables previously proposed to be associated with patient need did
not predict PICU admission length. Of the proposed predictor variables,
grouping patients by discharge destination contributed the most to
length of stay in four different PICUs.