Measuring costs of guideline-driven mental health care: the Texas Medication Algorithm Project
✍ Scribed by T. Michael Kashner; A. John Rush; Kenneth Z. Altshuler
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 104 KB
- Volume
- 2
- Category
- Article
- ISSN
- 1091-4358
No coin nor oath required. For personal study only.
✦ Synopsis
Background: Algorithms describe clinical choices to treat a specific disorder. To many, algorithms serve as important tools helping practitioners make informed choices about how best to treat patients, achieving better outcomes more quickly and at a lower cost. Appearing as flow charts and decision trees, algorithms are developed during consensus conferences by leading experts who explore the latest scientific evidence to describe optimal treatment for each disorder. Despite a focus on 'optimal' care, there has been little discussion in the literature concerning how costs should be defined and measured in the context of algorithmbased practices. Aims of the study: This paper describes the strategy to measure costs for the Texas Medication Algorithm project, or TMAP. Launched by the Texas Department of Mental Health and Mental Retardation and the University of Texas Southwestern Medical Center at Dallas, this multi-site study investigates outcomes and costs of medication algorithms for bipolar disorder, schizophrenia and depression. Methods: To balance costs with outcomes, we turned to costeffectiveness analyses as a framework to define and measure costs. Alternative strategies (cost-benefit, cost-utility, cost-of-illness) were inappropriate since algorithms are not intended to guide resource allocation across different diseases or between health-