Exploring correlates of high psychiatric inpatient utilization in Switzerland: a descriptive and machine learning analysis.

Abstract

Background: This study investigated socio-demographic, psychiatric, and psychological characteristics of patients with high versus low utilization of psychiatric inpatient services. Our objective was to better understand the utilization pattern and to contribute to improving psychiatric care.

Methods: One-hundred and twenty inpatients of the University Psychiatric Clinics (UPK) Basel, Switzerland, participated in this cross-sectional study. All patients were interviewed using different clinical scales. As target variables we investigated the number of days of psychiatric inpatient treatment within a 30-month period.

Results: Despite including multiple relevant patient variables and using elaborate statistical models (classic univariate und multiple regression, LASSO regression, and non-linear random forest models), the selected variables explained only a small percentage of variance in the number of days of psychiatric inpatient treatment with cross-validated R ² values ranging from 0.16 to 0.22. The number of unmet needs of patients turned out to be a meaningful and hence potentially clinically relevant correlate of the number of days of psychiatric inpatient treatment in each of the applied statistical models.

Conclusions: High utilization behavior remains a complex phenomenon, which can only partly be explained by psychiatric, psychological, or social/demographic characteristics. Self-reported unmet patient needs seems to be a promising variable which may be targeted by further research in order to potentially reduce unnecessary hospitalizations or develop better tailored psychiatric treatments.