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

BMC Psychiatry

Claus Normann²  Klaus Kaier³  Anita Patel¹  Paul McCrone⁴  Jan Wolff⁵ 

[1] Barts and the London School of Medicine and Dentistry, Centre for Primary Care and Public Health, Queen Mary University of London, 58 Turner Street, London, E12AB, United Kingdom;Department of Psychiatry and Psychotherapy, Medical Centre- University of Freiburg, Hauptstrasse 5, Freiburg, 79106, Germany;Institute of Medical Biometry, Medical Centre-University of Freiburg, Hugstetter Strasse 49, Freiburg, 79106, Germany;Institute of Psychiatry, Psychology & Neuroscience, King’s Health Economics, King’s College London, De Crespigny Park, London, SE5 8AF, United Kingdom;Department for Management and Controlling, Medical Centre-University of Freiburg, Hugstetter Strasse 49, Freiburg, 79106, Germany

关键词: Prospective payment systems;    Costs and cost analysis;    Hospitals, length of stay;    Mental health;   

Others  :  1230267
DOI  :  10.1186/s12888-015-0623-6

 received in 2015-02-13, accepted in 2015-09-30,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Wolff et al.

【 图 表 】

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