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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

Receiver operating characteristic curves of episodes with long stays. a Discrimination of long stays in the complete sample. b Out-of-sample prediction of long stays
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Fig2: Receiver operating characteristic curves of episodes with long stays. a Discrimination of long stays in the complete sample. b Out-of-sample prediction of long stays

Mentions: Figure 2a shows the ability of the independent variables for discrimination of episodes experiencing a very long stay in the complete sample. Figure 2b shows the ability for out-of-sample predictions of very long stays. A second model (admission model) was plotted for each analysis with all variables from the full model except of discharge against medical advice/referral to another hospital and readmissions, in order to model solely on the basis of variables that were documented at admission. The presented ROCs plot the percentage of episodes rightly classified as long stays against the percentage of episodes falsely classified as long stays for any given cut of point of calculated probabilities. Figure 3 illustrates the corresponding results for the prediction of very short stays.Fig. 2


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)

Receiver operating characteristic curves of episodes with long stays. a Discrimination of long stays in the complete sample. b Out-of-sample prediction of long stays
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Receiver operating characteristic curves of episodes with long stays. a Discrimination of long stays in the complete sample. b Out-of-sample prediction of long stays
Mentions: Figure 2a shows the ability of the independent variables for discrimination of episodes experiencing a very long stay in the complete sample. Figure 2b shows the ability for out-of-sample predictions of very long stays. A second model (admission model) was plotted for each analysis with all variables from the full model except of discharge against medical advice/referral to another hospital and readmissions, in order to model solely on the basis of variables that were documented at admission. The presented ROCs plot the percentage of episodes rightly classified as long stays against the percentage of episodes falsely classified as long stays for any given cut of point of calculated probabilities. Figure 3 illustrates the corresponding results for the prediction of very short stays.Fig. 2

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