Literature DB >> 1737707

Predicting length of stay for patients with psychoses.

C Stoskopf1, S D Horn.   

Abstract

The Computerized Psychiatric Severity Index (CPSI) and 22 patient variables were used to predict length of hospitalization for 304 psychiatric patients in DRG 430 who were diagnosed with schizophrenia or affective disorder and had no secondary diagnoses. Length of stay, which correlated .96 with total charges, was used as the dependent variable (measure of resource use). The patient variables and CPSI score explained 32.5 percent of the variation in length of stay for all of DRG 430 (27.5 percent for affective disorder patients and 70.3 percent for schizophrenia patients). Addition of the treatment variable "receipt of ECT" (electroconvulsive therapy) permitted the regression models to explain 40.9 percent of the variation in length of stay (36.24 percent for affective disorder and 71.22 percent for schizophrenia). In each regression model, maximum CPSI score was significant, indicating that much heterogeneity in DRG 430 can be explained by CPSI. Using one payment for such a diverse group places health care institutions at great risk of financial loss. Our study indicates that a continuing need exists for research in the area of case-mix measures for psychiatric inpatients.

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Year:  1992        PMID: 1737707      PMCID: PMC1069854     

Source DB:  PubMed          Journal:  Health Serv Res        ISSN: 0017-9124            Impact factor:   3.402


  10 in total

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Journal:  Med Care       Date:  1989-01       Impact factor: 2.983

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Journal:  Med Care       Date:  1975-06       Impact factor: 2.983

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Journal:  J Consult Clin Psychol       Date:  1983-08

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Journal:  Med Care       Date:  1978-03       Impact factor: 2.983

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

Authors:  C Stoskopf; S D Horn
Journal:  Med Care       Date:  1991-03       Impact factor: 2.983

10.  The Severity of Illness Index as a severity adjustment to diagnosis-related groups.

Authors:  S D Horn; R A Horn; P D Sharkey
Journal:  Health Care Financ Rev       Date:  1984
  10 in total
  6 in total

1.  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

2.  Neurogenetics: advancing the "next-generation" of brain research.

Authors:  Huda Y Zoghbi; Stephen T Warren
Journal:  Neuron       Date:  2010-10-21       Impact factor: 17.173

3.  Patient-related risk factors that predict poor outcome after total hip replacement.

Authors:  C H MacWilliam; M U Yood; J J Verner; B D McCarthy; R E Ward
Journal:  Health Serv Res       Date:  1996-12       Impact factor: 3.402

Review 4.  Challenges in the treatment of major depressive disorder with psychotic features.

Authors:  Anthony J Rothschild
Journal:  Schizophr Bull       Date:  2013-04-18       Impact factor: 9.306

5.  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

6.  Use of data mining techniques to determine and predict length of stay of cardiac patients.

Authors:  Peyman Rezaei Hachesu; Maryam Ahmadi; Somayyeh Alizadeh; Farahnaz Sadoughi
Journal:  Healthc Inform Res       Date:  2013-06-30
  6 in total

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