Measuring costs of guideline-driven mental health care: the Texas Medication Algorithm Project

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 algorithm-based 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 cost-effectiveness 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- and non-health-related commodities. ‘Costs’ are operationalized consistent with the framework presented by the United States Public Health Service Panel on Cost Effectiveness in Medicine.

Patient specific costs are calculated by multiplying patient units of use by a unit cost, and summing over all service categories. Outpatient services are counted by procedures. Inpatient services are counted by days classified into diagnosis groups. Utilization information is derived from patient self-reports, medical charts and administrative file sources. Unit costs are computed by payer source. Finally, hierarchical modeling is used to describe how costs and effectiveness differ between algorithm-based and treatment-as-usual practices. Discussion: Cost estimates of algorithm-based practices should (i) measure opportunity costs, (ii) employ structured data collection methods, (iii) profile patient use of both mental health and general medical providers and (iv) reflect costs by payer status in different economic environments. Implication for health care provision and use: Algorithms may help guide clinicians, their patients and third party payers to rely on the latest scientific evidence to make treatment choices that balance costs with outcomes. Implication for health policies: Planners should consider consumer wants and economic costs when developing and testing new clinical algorithms. Implications for further research: Future studies may wish to consider similar methods to estimate costs in evaluating algorithm-based practices. Copyright © 1999 John Wiley & Sons, Ltd.