Literature DB >> 3095640

Prospective payment for psychiatry--feasibility and impact.

D N Schumacher, M J Namerow, B Parker, P Fox, V Kofie.   

Abstract

Although 15 diagnosis-related groups (DRGs) have been proposed for psychiatric hospital patients, psychiatric hospitals are currently exempt from the DRG prospective payment system. We investigated the ability of the psychiatric DRGs to predict the hospital length of stay and costs by retrospectively analyzing the charts of 8816 randomly selected patients from 32 psychiatric hospitals throughout the United States. In addition, we developed other grouping systems to see whether they would have been better predictors of length of stay. We found that grouping the patients in the 15 psychiatric DRGs reduced the total variance in length of stay by only 3.9 percent. Furthermore, our best alternative grouping--based on major diagnostic categories, whether the patient was transferred from another facility, age, and psychiatric complications and comorbidities--reduced the variance by only 7.8 percent. We conclude that DRGs do not adequately predict length of stay or costs in psychiatric hospitals. We identified factors other than diagnosis that predicted the length of stay better, but all the models we tested would create large financial "winners" and "losers" and thus introduce inappropriate incentives into the care of patients in psychiatric hospitals.

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Year:  1986        PMID: 3095640     DOI: 10.1056/NEJM198611203152106

Source DB:  PubMed          Journal:  N Engl J Med        ISSN: 0028-4793            Impact factor:   91.245


  10 in total

1.  Variables affecting length of psychiatric inpatient treatment.

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2.  DRGs and other patient-, service- and area-level factors influencing length of stay in acute psychiatric wards: the Veneto Region experience.

Authors:  Riccardo Pertile; Valeria Donisi; Laura Grigoletti; Andrea Angelozzi; Giuseppe Zamengo; Grazia Zulian; Francesco Amaddeo
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2010-05-15       Impact factor: 4.328

3.  A retrospective study of determinants of length of stay in a geropsychiatric state hospital.

Authors:  E D Parks; N Josef
Journal:  Psychiatr Q       Date:  1997

4.  Assessment of psychiatric outcomes in Japan based on diagnostic procedure combination information.

Authors:  Takeru Abe; Koji Ikeda; Kenji Kuroda; Akihito Hagihara
Journal:  Psychiatr Q       Date:  2011-06

5.  Does psychopathology at admission predict the length of inpatient stay in psychiatry? Implications for financing psychiatric services.

Authors:  Ingeborg Warnke; Wulf Rössler; Uwe Herwig
Journal:  BMC Psychiatry       Date:  2011-07-29       Impact factor: 3.630

6.  A comprehensive payment model for short- and long-stay psychiatric patients.

Authors:  B E Fries; P W Durance; D R Nerenz; M L Ashcraft
Journal:  Health Care Financ Rev       Date:  1993

7.  Characteristics of high staff intensive Medicare psychiatric inpatients.

Authors:  Jerry Cromwell; Jan Maier; Barbara Gage; Edward Drozd; Deborah Osber; Erin Richter; Leslie Greenwald; Howard Goldman
Journal:  Health Care Financ Rev       Date:  2004

8.  Utility of the Health of the Nation Outcome Scales (HoNOS) in Predicting Mental Health Service Costs for Patients with Common Mental Health Problems: Historical Cohort Study.

Authors:  Conal Twomey; A Matthew Prina; David S Baldwin; Jayati Das-Munshi; David Kingdon; Leonardo Koeser; Martin J Prince; Robert Stewart; Alex D Tulloch; Alarcos Cieza
Journal:  PLoS One       Date:  2016-11-30       Impact factor: 3.240

9.  Utility of functioning in predicting costs of care for patients with mood and anxiety disorders: a prospective cohort study.

Authors:  Conal Twomey; Alarcos Cieza; David S Baldwin
Journal:  Int Clin Psychopharmacol       Date:  2017-07       Impact factor: 1.659

Review 10.  A systematic review of the predictors of health service utilisation by adults with mental disorders in the UK.

Authors:  Conal D Twomey; David S Baldwin; Maren Hopfe; Alarcos Cieza
Journal:  BMJ Open       Date:  2015-07-06       Impact factor: 2.692

  10 in total

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