TY - JOUR
T1 - An empirical analysis of cost outcomes of the Texas Medication Algorithm Project
AU - Kashner, T. Michael
AU - Rush, A. John
AU - Crismon, M. Lynn
AU - Toprac, Marcia
AU - Carmody, Thomas J.
AU - Miller, Alexander L.
AU - Trivedi, Madhukar H.
AU - Wicker, Annie
AU - Suppes, Trisha
PY - 2006/5
Y1 - 2006/5
N2 - Objective: Disease management systems that incorporate medication algorithms have been proposed as cost-effective means to offer optimal treatment for patients with severe and chronic mental illnesses. The Texas Medication Algorithm Project was designed to compare health care costs and clinical outcomes between patients who received algorithm-guided medication management or usual care in 19 public mental health clinics. Methods: This longitudinal cohort study for patients with major depression (N=350), bipolar disorder (N=267), and schizophrenia (N=309) applied a multipart declining-effects cost model, outcomes were assessed by the Inventory of Depressive Symptomatology and the Brief Psychiatric Rating Scale. Results: Compared with patients in usual care, patients in algorithm-based care incurred higher medication costs and had more frequent physician visits, although these differences often became smaller with time. For major depression, algorithm-based care achieved better outcomes sustainable with time but at higher agency and nonagency costs (mixed cost-effective). For bipolar disorder, patients in algorithm-based management achieved better outcomes at lower agency costs (cost-effective). For schizophrenia, patients in algorithm-based care achieved better outcomes that diminished with time, with no detectable difference in health care costs (cost-effective). Conclusions: Cost outcomes of algorithm-based care and usual care varied by disorder and over time. For bipolar disorder and schizophrenia, algorithm-based care improved outcomes without higher costs for health care services. For major depression, substantively better and sustained outcomes were obtained but at greater costs.
AB - Objective: Disease management systems that incorporate medication algorithms have been proposed as cost-effective means to offer optimal treatment for patients with severe and chronic mental illnesses. The Texas Medication Algorithm Project was designed to compare health care costs and clinical outcomes between patients who received algorithm-guided medication management or usual care in 19 public mental health clinics. Methods: This longitudinal cohort study for patients with major depression (N=350), bipolar disorder (N=267), and schizophrenia (N=309) applied a multipart declining-effects cost model, outcomes were assessed by the Inventory of Depressive Symptomatology and the Brief Psychiatric Rating Scale. Results: Compared with patients in usual care, patients in algorithm-based care incurred higher medication costs and had more frequent physician visits, although these differences often became smaller with time. For major depression, algorithm-based care achieved better outcomes sustainable with time but at higher agency and nonagency costs (mixed cost-effective). For bipolar disorder, patients in algorithm-based management achieved better outcomes at lower agency costs (cost-effective). For schizophrenia, patients in algorithm-based care achieved better outcomes that diminished with time, with no detectable difference in health care costs (cost-effective). Conclusions: Cost outcomes of algorithm-based care and usual care varied by disorder and over time. For bipolar disorder and schizophrenia, algorithm-based care improved outcomes without higher costs for health care services. For major depression, substantively better and sustained outcomes were obtained but at greater costs.
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U2 - 10.1176/ps.2006.57.5.648
DO - 10.1176/ps.2006.57.5.648
M3 - Article
C2 - 16675759
AN - SCOPUS:33646443079
SN - 1075-2730
VL - 57
SP - 648
EP - 659
JO - Psychiatric Services
JF - Psychiatric Services
IS - 5
ER -