Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ BMC Psychiatryarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
BMC Psychiatry
Article . 2024 . Peer-reviewed
License: CC BY
Data sources: Crossref
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
BMC Psychiatry
Article . 2024
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
PubMed Central
Other literature type . 2024
License: CC BY
Data sources: PubMed Central
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
BMC Psychiatry
Article . 2024
Data sources: DOAJ
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
versions View all 5 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

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

Authors: Jaffé, Mariela E.; Weinmann, Stefan; Meyer, Andrea H.; Stepulovs, Helen; Luethi, Regula; Borgwardt, Stefan; Lieb, Roselind; +3 Authors

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

Abstract

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 R2 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.

Keywords

Male, Adult, Hospitals, Psychiatric, Mental Health Services, High utilization, RC435-571, Machine Learning, Revolving door, Severe mental illness, Humans, Re-hospitalisation, Psychiatric inpatient services, Aged, Psychiatry, Inpatients, Research, Mental Disorders, Female [MeSH] ; Aged [MeSH] ; Adult [MeSH] ; Psychiatric inpatient services ; Humans [MeSH] ; Severe mental illness ; Middle Aged [MeSH] ; Re-hospitalisation ; Cross-Sectional Studies [MeSH] ; Hospitalization/statistics ; Mental Health Services/statistics ; Revolving door ; High utilization ; Inpatients/statistics ; Male [MeSH] ; Switzerland [MeSH] ; Mental Disorders/epidemiology [MeSH] ; Research ; Patient Acceptance of Health Care/statistics ; Inpatients/psychology [MeSH] ; Machine Learning [MeSH] ; Mental Disorders/therapy [MeSH] ; Hospitals, Psychiatric/statistics, Middle Aged, Patient Acceptance of Health Care, Hospitalization, Cross-Sectional Studies, Female, Switzerland

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
gold