Predictors of length of stay in psychiatry: analyses of electronic medical records

Abstract

BackgroundLength of stay is a straightforward measure of hospital costs and retrospective data are widely available. However, a prospective idea of a patient’s length of stay would be required to predetermine hospital reimbursement per case based on patient classifications. The aim of this study was to analyse the predictive power of patient characteristics in terms of length of stay in a psychiatric hospital setting. A further aim was to use patient characteristics to predict episodes with extreme length of stay.MethodsThe study included all inpatient episodes admitted in 2013 to a psychiatric hospital. Zero-truncated negative binomial regression was carried out to predict length of stay. Penalized maximum likelihood logistic regressions were carried out to predict episodes experiencing extreme length of stay. Independent variables were chosen on the basis of prior research and model fit was cross-validated.ResultsA total of 738 inpatient episodes were included. Seven patient characteristics showed significant effects on length of stay. The strongest increasing effects were found in the presence of affective disorders as main diagnosis, followed by severity of disease and chronicity of disease. The strongest decreasing effects were found in danger to others, followed by the presence of substance-related disorders as main diagnosis, the daily requirement of somatic care and male gender. The squared correlation between out-of-sample predictions and observed values was 0.14. The root-mean-square-error was 40 days.ConclusionProspectively defining reimbursement per case might not be feasible in mental health because length of stay cannot be predicted by patient characteristics. Per diem systems should be used.

Impact of the carer on length of hospital stay for mental health: Results from two Australian surveys

“…In terms of the SHIP analysis for carer status, our findings were consistent with the small number of previous similar studies (Blais et al 2003;Brakoulias et al 2013;Compton et al 2006;Draper & Luscombe 1998;Jacobs et al 2015;Wolff et al 2015;Zhang et al 2011). Our analysis was particularly robust as it was conducted in a large, nationally representative sample of adults with psychosis; it also investigated length of stay in different types of hospitals (private vs public; null finding as seen in Appendix S2), which has been rarely considered in previous studies.…”

“…; Wolff et al . ). Most of these studies did not find a significant effect of family involvement on length of stay after adjustment for confounders.…”

“…Other studies have examined the impact of any family involvement, not just that of informal carers, including whether the patient was brought into hospital by a family member (Blais et al 2003;Brakoulias et al 2013;Compton et al 2006;Cyr & Haley 1983) and whether they live with a family member (Creed et al 1997;Wolff et al 2015). Most of these studies did not find a significant effect of family involvement on length of stay after adjustment for confounders.…”

Patient and Service-Level Factors Affecting Length of Inpatient Stay in an Acute Mental Health Service: A Retrospective Case Cohort Study

“…Other studies have looked at both psychopathology and ICD-10 diagnostic criteria as factors for in uencing length of stay [6,7] , however due to poor and inconsistent documentation on the electronic patient record the researchers were unable to be as stringent in their data gathering for diagnosis, instead relying on broader categories. In addition, we did not record comorbidities and it is likely that a high proportion of patients may have had several diagnoses which have not been captured by this study.…”

“…Attempts to develop an accurate model to predict length of stay have yielded poor results due to the complexity of mental health admissions [7] . Patient factors have not fully explained variations in length of stay, suggesting that other factors exert an in uence.…”