Toward Cost-Effective Staffing Mixes for Veterans Affairs Substance Use Disorder Treatment Programs

Background

In fiscal year (FY) 2008, 133,658 patients were provided services within substance use disorders treatment programs (SUDTPs) in the U.S. Department of Veterans Affairs (VA) health care system. To improve the effectiveness and cost-effectiveness of SUDTPs, we analyze the impacts of staffing mix on the benefits and costs of specialty SUD services. This study demonstrates how cost-effective staffing mixes for each type of VA SUDTPs can be defined empirically.

Methods

We used a stepwise method to derive prediction functions for benefits and costs based on patients’ treatment outcomes at VA SUDTPs nationally from 2001 to 2003, and used them to formulate optimization problems to determine recommended staffing mixes that maximize net benefits per patient for four types of SUDTPs by using the solver function with the Generalized Reduced Gradient algorithm in Microsoft Excel 2010 while conforming to limits of current practice. We conducted sensitivity analyses by varying the baseline severity of addiction problems between lower (2.5 %) and higher (97.5 %) values derived from bootstrapping.

Results and conclusions

Compared to the actual staffing mixes in FY01-FY03, the recommended staffing mixes would lower treatment costs while improving patients’ outcomes, and improved net benefits are estimated from $1472 to $17,743 per patient.

Full article at:  http://goo.gl/mfHN2V

By: Jinwoo J. Im¹²³, Ross D. Shachter², John W. Finney³ and Jodie A. Trafton^34*

¹Management of Innovation Program, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, South Korea

²Department of Management Science and Engineering, Stanford University, Stanford 94305, CA, USA

³Center for Health Care Evaluation, VA Palo Alto Healthcare System, Menlo Park 94025, CA, USA

⁴Department of Psychiatry and Behavioral Sciences and Center for Health Policy, Stanford University School of Medicine, 795 Willow Road (152-MPD), Stanford 94305, CA, USA

 

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