Literature DB >> 1997749

The Computerized Psychiatric Severity Index as a predictor of inpatient length of stay for psychoses.

C Stoskopf1, S D Horn.   

Abstract

The Computerized Psychiatric Severity Index (CPSI), developed by Susan D. Horn and colleagues at The Johns Hopkins University School of Hygiene and Public Health, was used to study the severity of mental illness of 304 patients with discharge diagnosis of schizophrenia or affective disorder, and no secondary psychiatric diagnoses (DRG 430). The CPSI explained 13.7% of the variation in length of stay for these patients (14.31% of the variation in length of stay for affective disorders and 10.04% for the schizophrenia). Length of stay was an appropriate measure of the dependent variable resource use since it correlated 0.96 with total charges.

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Year:  1991        PMID: 1997749     DOI: 10.1097/00005650-199103000-00001

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  9 in total

1.  Needs-based planning: evaluation of a level-of-care planning model.

Authors:  J Durbin; J Cochrane; P Goering; D Macfarlane
Journal:  J Behav Health Serv Res       Date:  2001-02       Impact factor: 1.505

2.  Physical therapy activities in stroke, knee arthroplasty, and traumatic brain injury rehabilitation: their variation, similarities, and association with functional outcomes.

Authors:  Gerben DeJong; Ching-Hui Hsieh; Koen Putman; Randall J Smout; Susan D Horn; Wenqiang Tian
Journal:  Phys Ther       Date:  2011-10-14

3.  Developing a New Zealand casemix classification for mental health services.

Authors:  Kathy Eagar; Phillipa Gaines; Philip Burgess; Janette Green; Alison Bower; Bill Buckingham; Graham Mellsop
Journal:  World Psychiatry       Date:  2004-10       Impact factor: 49.548

4.  Lessons from evaluating an automated patient severity index.

Authors:  R F Gibson; P J Haug; S D Horn
Journal:  J Am Med Inform Assoc       Date:  1996 Sep-Oct       Impact factor: 4.497

5.  Predicting length of stay for psychiatric diagnosis-related groups using neural networks.

Authors:  W E Lowell; G E Davis
Journal:  J Am Med Inform Assoc       Date:  1994 Nov-Dec       Impact factor: 4.497

6.  Profiling hospitals for length of stay for treatment of psychiatric disorders.

Authors:  Jeffrey S Harman; Brian J Cuffel; Kelly J Kelleher
Journal:  J Behav Health Serv Res       Date:  2004 Jan-Mar       Impact factor: 1.505

7.  Predicting length of stay for patients with psychoses.

Authors:  C Stoskopf; S D Horn
Journal:  Health Serv Res       Date:  1992-02       Impact factor: 3.402

8.  Observed-predicted length of stay for an acute psychiatric department, as an indicator of inpatient care inefficiencies. Retrospective case-series study.

Authors:  Rosa E Jiménez; Rosa M Lam; Milagros Marot; Ariel Delgado
Journal:  BMC Health Serv Res       Date:  2004-02-17       Impact factor: 2.655

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

Authors:  Jan Wolff; Paul McCrone; Anita Patel; Klaus Kaier; Claus Normann
Journal:  BMC Psychiatry       Date:  2015-10-07       Impact factor: 3.630

  9 in total

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