Literature DB >> 8590114

A computer-based model for analyzing staffing needs of psychiatric treatment programs.

R M Atkins1.   

Abstract

The author describes a computer-based model that derives nonnursing staffing requirements from quantifiable performance expectations for structured psychotherapy and rehabilitation therapy. Input variables include treatment mode, contact hours, patient volume (average daily census and average length of stay), hours of structured therapy per week, assessment and discharge activities, and ancillary staff activities. Output variables are number of staff needed, weekly productivity ratios such as hours of direct service per full-time-equivalent position and new patients served per full-time-equivalent position, and hours of structured therapy per day per patient. Initial validation of the model compared its predictions of staffing requirements with predictions based on staffing ratios derived from the literature and with actual staffing levels at the author's institution. The model was used in an analysis of two hypothetical scenarios showing the effects of managed care on variables that influence staffing. The results showed that as average length of stay decreases under managed care, reimbursement based on number of patients served at one time decreases, although costs of providing treatment increase because the program needs more staff to handle the increased number of admissions and discharges.

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Year:  1995        PMID: 8590114     DOI: 10.1176/ps.46.12.1272

Source DB:  PubMed          Journal:  Psychiatr Serv        ISSN: 1075-2730            Impact factor:   3.084


  2 in total

Review 1.  Computers in psychiatry: a review of past programs and an analysis of historical trends.

Authors:  Amar K Das
Journal:  Psychiatr Q       Date:  2002

2.  A Need-based model for determining staffing needs for the public sector outpatient mental health service system.

Authors:  David Elisha; Daphna Levinson; Alexander Grinshpoon
Journal:  J Behav Health Serv Res       Date:  2004 Jul-Sep       Impact factor: 1.505

  2 in total

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