Literature DB >> 16979244

Diagnostic variability for schizophrenia and major depression in a large public mental health care system dataset.

David P Folsom1, Laurie Lindamer, Lori P Montross, William Hawthorne, Shahrokh Golshan, Richard Hough, John Shale, Dilip V Jeste.   

Abstract

Administrative datasets can provide information about mental health treatment in real world settings; however, an important limitation in using these datasets is the uncertainty regarding psychiatric diagnosis. To better understand the psychiatric diagnoses, we investigated the diagnostic variability of schizophrenia and major depression in a large public mental health system. Using schizophrenia and major depression as the two comparison diagnoses, we compared the variability of diagnoses assigned to patients with one recorded diagnosis of schizophrenia or major depression. In addition, for both of these diagnoses, the diagnostic variability was compared across seven types of treatment settings. Statistical analyses were conducted using t tests for continuous data and chi-square tests for categorical data. We found that schizophrenia had greater diagnostic variability than major depression (31% vs. 43%). For both schizophrenia and major depression, variability was significantly higher in jail and the emergency psychiatric unit than in inpatient or outpatient settings. These findings demonstrate that the variability of psychiatric diagnoses recorded in the administrative dataset of a large public mental health system varies by diagnosis and by treatment setting. Further research is needed to clarify the relationship between psychiatric diagnosis, diagnostic variability and treatment setting.

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Year:  2006        PMID: 16979244     DOI: 10.1016/j.psychres.2005.12.002

Source DB:  PubMed          Journal:  Psychiatry Res        ISSN: 0165-1781            Impact factor:   3.222


  7 in total

1.  Using electronic medical records to determine the diagnosis of clinical depression.

Authors:  Nhi-Ha T Trinh; Soo Jeong Youn; Jessica Sousa; Susan Regan; C Andres Bedoya; Trina E Chang; Maurizio Fava; Albert Yeung
Journal:  Int J Med Inform       Date:  2011-04-22       Impact factor: 4.046

2.  Stigma experienced by people using mental health services in San Diego County.

Authors:  Andrew Sarkin; Rachel Lale; Marisa Sklar; Kimberly C Center; Todd Gilmer; Chris Fowler; Richard Heller; Victoria D Ojeda
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2014-11-19       Impact factor: 4.328

3.  Predisposing, enabling, and need factors associated with high service use in a public mental health system.

Authors:  Laurie A Lindamer; Lin Liu; David H Sommerfeld; David P Folsom; William Hawthorne; Piedad Garcia; Gregory A Aarons; Dilip V Jeste
Journal:  Adm Policy Ment Health       Date:  2012-05

4.  Development and validation of a mental health subscale from the Quality of Well-Being Self-Administered.

Authors:  Andrew J Sarkin; Erik J Groessl; Jordan A Carlson; Steven R Tally; Robert M Kaplan; William J Sieber; Theodore G Ganiats
Journal:  Qual Life Res       Date:  2012-10-27       Impact factor: 4.147

5.  AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units.

Authors:  Fatema Mustansir Dawoodbhoy; Jack Delaney; Paulina Cecula; Jiakun Yu; Iain Peacock; Joseph Tan; Benita Cox
Journal:  Heliyon       Date:  2021-05-12

6.  The elderly in the psychiatric emergency service (PES); a descriptive study.

Authors:  Yves Chaput; Lucie Beaulieu; Michel Paradis; Edith Labonté
Journal:  BMC Psychiatry       Date:  2011-07-15       Impact factor: 3.630

7.  Aggressive behaviors in the psychiatric emergency service.

Authors:  Yves Chaput; Lucie Beaulieu; Michel Paradis; Edith Labonté
Journal:  Open Access Emerg Med       Date:  2011-03-04
  7 in total

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