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

Wolff J, McCrone P, Patel A, Kaier K, Normann C - BMC Psychiatry (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's Health Economics, King's College London, De Crespigny Park, SE5 8AF, London, United Kingdom. jan.wolff@kcl.ac.uk.

ABSTRACT

Background: Length 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.

Methods: The 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.

Results: A 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.

Conclusion: Prospectively 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.

No MeSH data available.


Related in: MedlinePlus

Exponentiated coefficients of zero-truncated negative binomial regression
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Fig1: Exponentiated coefficients of zero-truncated negative binomial regression

Mentions: Figure 1 shows the exponentiated coefficients of independent variables derived from the zero-truncated negative binomial regression. The strongest significantly positive effect in patient-related variables was found for the presence of affective disorders as main diagnosis. Its exponentiated coefficient of 1.31, i.e. the incidence rate ratio, represents a 31 % increase in length of stay associated with a main diagnosis of affective disorders, holding all other variables constant. Further significantly positive association of patient-variables with length of stay were found in severity of disease and chronicity of disease. The service-variable readmission after formal discharge was associated with an increase in length of stay of 67 %.Fig. 1


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

Wolff J, McCrone P, Patel A, Kaier K, Normann C - BMC Psychiatry (2015)

Exponentiated coefficients of zero-truncated negative binomial regression
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4597607&req=5

Fig1: Exponentiated coefficients of zero-truncated negative binomial regression
Mentions: Figure 1 shows the exponentiated coefficients of independent variables derived from the zero-truncated negative binomial regression. The strongest significantly positive effect in patient-related variables was found for the presence of affective disorders as main diagnosis. Its exponentiated coefficient of 1.31, i.e. the incidence rate ratio, represents a 31 % increase in length of stay associated with a main diagnosis of affective disorders, holding all other variables constant. Further significantly positive association of patient-variables with length of stay were found in severity of disease and chronicity of disease. The service-variable readmission after formal discharge was associated with an increase in length of stay of 67 %.Fig. 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Psychiatry, Psychology & Neuroscience, King's Health Economics, King's College London, De Crespigny Park, SE5 8AF, London, United Kingdom. jan.wolff@kcl.ac.uk.

ABSTRACT

Background: Length 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.

Methods: The 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.

Results: A 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.

Conclusion: Prospectively 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.

No MeSH data available.


Related in: MedlinePlus