Implementation of the Texas Medication Algorithm Project patient and family education program.

OBJECTIVE: This article describes the implementation and utilization of the patient and family education program (PFEP) component of the Texas Medication Algorithm Project (TMAP). The extent of participation, types of psychoeducation received, and predictors of receiving at least a minimum level of education are presented. METHOD: TMAP included medication guidelines, a dedicated clinical coordinator, standardized assessments of symptoms and side effects, uniform documentation, and a PFEP. The PFEP includes phased, multimodal, disorder-specific educational materials for patients and families. Participants were adult outpatients of 1 of 7 community mental health centers in Texas that were implementing the TMAP disease management package. Patients had DSM-IV clinical diagnoses of major depressive disorder, with or without psychotic features; bipolar I disorder or schizoaffective disorder, bipolar type; or schizophrenia or schizoaffective disorder. Assessments were administered by independent research coordinators. Study data were collected between March 1998 and March 2000, and patients participated for at least 1 year. RESULTS: Of the 487 participants, nearly all (95.1%) had at least 1 educational encounter, but only 53.6% of participants met criteria for "minimum exposure" to individual education interventions. Furthermore, only 31.0% participated in group education, and 42.5% had a family member involved in at least 1 encounter. Participants with schizophrenia were less involved in the PFEP across multiple indicators of utilization. Diagnosis, intensity of symptoms, age, and receipt of public assistance were related to the likelihood of exposure to minimum levels of individual education. CONCLUSION: Despite adequate resources and infrastructure to provide PFEP, utilization was less than anticipated. Although implementation guidelines were uniform across diagnoses, participants with schizophrenia experienced less exposure to psychoeducation. Recommendations for improving program implementation and modification of materials are discussed.

Comparison of self-report and clinician ratings on two inventories of depressive symptomatology.

OBJECTIVE: This study evaluated the concordance between the self-report and the clinician-rated versions of the Inventory of Depressive Symptomatology (IDS-30) and between the two versions of the briefer 16-item Quick Inventory of Depressive Symptomatology (QIDS-16). METHODS: Data were gathered for 544 adult outpatients with psychotic (N = 106) or nonpsychotic (N = 438) major depressive disorder at 14 public sector mental health clinics in the Texas Medication Algorithm Project. Data for the QIDS-16 were extracted from the IDS-30. Baseline scores and scores from the final study visit at or before month 12 were analyzed. The clinician-rated and the self-report versions of each scale were compared in their identification of response to treatment and remission. RESULTS: The average baseline IDS-SR-30 total score was 2.2 points higher (indicating greater severity) than the IDS-C-30 score; the average QIDS-SR-16 total score was only .3 points higher than the QIDS-C-16 score. The IDS-SR-30 and the IDS-C-30, as well as the QIDS-C-16 and QIDS-SR-16, agreed substantially in classifying response and remission for patients, regardless of whether the patients had psychotic features. None of a large number of clinical and demographic features accounted for differences between the QIDS-SR-16 and QIDS-C-16 total scores. CONCLUSIONS: Either the IDS-30 or the QIDS-16 self-report adequately assesses depressive symptom severity among public-sector outpatients with major depressive disorder. The briefer QIDS-16 may be preferred to save time and cost.

An empirical analysis of cost outcomes of the Texas Medication Algorithm Project.

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 multi-part 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 non-agency 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.

Clinical factors associated with employment among people with severe mental illness: findings from the employment intervention demonstration program.

Research has shown that supported employment programs are effective in helping psychiatric outpatients achieve vocational outcomes, yet not all program participants are able to realize their employment goals. This study used 24 months of longitudinal data from a multisite study of supported employment interventions to examine the relationship of patient clinical factors to employment outcomes. Multivariate random regression analysis indicated that, even when controlling for an extensive series of demographic, study condition (experimental versus control), and work history covariates, clinical factors were associated with individuals' ability to achieve competitive jobs and to work 40 or more hours per month. Poor self-rated functioning, negative psychiatric symptoms, and recent hospitalizations were most consistently associated with failure to achieve these employment outcomes. These findings suggest ways that providers can tailor supported employment programs to achieve success with a diverse array of clinical subpopulations.

Effects of job development and job support on competitive employment of persons with severe mental illness.

OBJECTIVES: Few studies have sought to determine which specific supported employment services improve employment outcomes for people with pyschiatric disabilities. This study examined the effects of job development and job support among other services on acquisition and retention of competitive employment. METHODS: Data used in the analysis came from seven sites of the Employment Intervention Demonstration Program. Employment data were collected weekly for a period up to 24 months for 1,340 participants. A random-effects meta-analysis was conducted. RESULTS: Job development increased the probability of obtaining competitive employment. The effects of job development on job acquisition remained after the effects of other factors were controlled for. Job support was associated with more months in the first competitive job but not total hours worked. However, no evidence for the causal role of job support was found in analyses that tested the effects of job support after the job support was provided. The causal role of job support alone was also cast in doubt by the fact that a substantial overlap existed between individuals who received job support and vocational counseling. CONCLUSIONS: Job development is a very effective service when the goal is job acquisition. Job support is associated with retention of a first competitive job, but its causal role is questionable.

Integration of psychiatric and vocational services: a multisite randomized, controlled trial of supported employment.

OBJECTIVE: Although large-scale surveys indicate that patients with severe mental illness want to work, their unemployment rate is three to five times that of the general adult population. This multisite, randomized implementation effectiveness trial examined the impact of highly integrated psychiatric and vocational rehabilitation services on the likelihood of successful work outcomes. METHOD: At seven sites nationwide, 1,273 outpatients with severe mental illness were randomly assigned either to an experimental supported employment program or to a comparison/services-as-usual condition and followed for 24 months. Data collection involved monthly services tracking, semiannual in-person interviews, recording of all paid employment, and program ratings made by using a services-integration measure. The likelihood of competitive employment and working 40 or more hours per month was examined by using mixed-effects random regression analysis. RESULTS: Subjects served by models that integrated psychiatric and vocational service delivery were more than twice as likely to be competitively employed and almost 1(1/2) times as likely to work at least 40 hours per month when the authors controlled for time, demographic, clinical, and work history confounds. In addition, higher cumulative amounts of vocational services were associated with better employment outcomes, whereas higher cumulative amounts of psychiatric services were associated with poorer outcomes. CONCLUSIONS: Supported employment models with high levels of integration of psychiatric and vocational services were more effective than models with low levels of service integration.

Brief psychiatric rating scale expanded version: How do new items affect factor structure?

Our goal was to suggest a factor structure for the Brief Psychiatric Rating Scale Expanded Version (BPRS-E) based upon a large and diverse sample and to determine which of the new items improved the factors derived from the 18-item version of the scale that have been used in clinical research for decades. We investigated the consistency of our proposed model over time and across demographic groups. As part of the Texas Medication Algorithm Project, the BPRS-E was administered to a total of 1440 psychiatric outpatients in three different diagnostic groups on multiple occasions. The sample was randomly split so that exploratory factor analysis could be done with the first half, and the model could be confirmed on the second half. A four-factor structure including factors assessing depression/anxiety, psychosis, negative symptoms, and activation was found. For each factor, we specify items in the expanded version that added to the breadth of the commonly used clinical factors while improving or maintaining goodness of fit and reliability. The final model proposed was consistent over time and across diagnosis, phase of illness, age, gender, ethnicity, and level of education. The BPRS-E has a stable four-factor structure, making it useful as a clinical outcome measure.

A psychoeducational program for children with ADHD or depression and their families: results from the CMAP feasibility study.

For children and adolescents with behavioral and emotional disorders and their families, education about their disorders and the treatments is an essential component of a comprehensive approach to their care. Education can encourage active participation in treatment, enhance adherence to treatment regimens, and provide patients and families with important coping skills. Thus, the Children's Medication Algorithm Project (CMAP) incorporated a psychoeducational program into the medication algorithm created to improve treatment of children with ADHD and/or depression in the Texas public mental health sector. This article describes the process by which a comprehensive educational program was developed in partnership with parents and advocates. The final program is described, as well as a pilot study to examine the feasibility of implementation in four community clinics.

Results of a multisite randomized trial of supported employment interventions for individuals with severe mental illness.

CONTEXT: National probability surveys indicate that most individuals with schizophrenia and other severe mental illnesses are not employed. This multisite study tested the effectiveness of supported employment (SE) models combining clinical and vocational rehabilitation services to establish competitive employment. METHODS: We randomly assigned 1273 outpatients with severe mental illness from 7 states in the United States to an experimental SE program or to a comparison or a services-as-usual condition, with follow-up for 24 months. Participants were interviewed semiannually, paid employment was tracked weekly, and vocational and clinical services were measured monthly. Mixed-effects random regression analysis was used to predict the likelihood of competitive employment, working 40 or more hours in a given month, and monthly earnings. RESULTS: Cumulative results during 24 months show that experimental group participants (359/648 [55%]) were more likely than those in the comparison programs (210/625 [34%]) to achieve competitive employment (chi(2) = 61.17; P<.001). Similarly, patients in experimental group programs (330/648 [51%]) were more likely than those in comparison programs (245/625 [39%]) to work 40 or more hours in a given month (chi(2) = 17.66; P<.001). Finally, participants in experimental group programs had significantly higher monthly earnings than those in the comparison programs (mean, US 122 dollars/mo [n=639] vs US 99 dollars/mo [n=622]); t(1259) = -2.04; P<.05). In the multivariate longitudinal analysis, experimental condition subjects were more likely than comparison group subjects to be competitively employed, work 40 or more hours in a given month, and have higher earnings, despite controlling for demographic, clinical, work history, disability beneficiary status, and study site confounders. Moreover, the advantage of experimental over comparison group participants increased during the 24-month study period. CONCLUSION: The SE models tailored by integrating clinical and vocational services were more effective than services as usual or unenhanced services.

Development of the Brief Bipolar Disorder Symptom Scale for patients with bipolar disorder.

The Brief Bipolar Disorder Symptom Scale (BDSS) is a 10-item measure of symptom severity that was derived from the 24-item Brief Psychiatric Rating Scale (BPRS24). It was developed for clinical use in settings where systematic evaluation is desired within the constraints of a brief visit. The psychometric properties of the BDSS were evaluated in 409 adult outpatients recruited from 19 clinics within the public mental health system of Texas, as part of the Texas Medication Algorithm Project (TMAP). The selection process for individual items is discussed in detail, and was based on multiple analyses, including principal components analysis with varimax rotation. Selection of the final items considered the statistical strength and factor loading of items within each of those factors as well as the need for comprehensive coverage of critical symptoms of bipolar disorder. The BDSS demonstrated good psychometric properties in this preliminary investigation. It demonstrated a strong association with the BPRS24 and performed similarly to the BPRS24 in its relationship to other symptom measures. The BDSS demonstrated superior sensitivity to symptom change, and an excellent level of agreement for classification of patients as either responders or non-responders with the BPRS24.

Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project.

CONTEXT: The Texas Medication Algorithm Project is an evaluation of an algorithm-based disease management program for the treatment of the self-declared persistently and seriously mentally ill in the public mental health sector. OBJECTIVE: To present clinical outcomes for patients with major depressive disorder (MDD) during 12-month algorithm-guided treatment (ALGO) compared with treatment as usual (TAU). DESIGN: Effectiveness, intent-to-treat, prospective trial comparing patient outcomes in clinics offering ALGO with matched clinics offering TAU. SETTING: Four ALGO clinics, 6 TAU clinics, and 4 clinics that offer TAU to patients with MDD but provide ALGO for schizophrenia or bipolar disorder. Patients Male and female outpatients with a clinical diagnosis of MDD (psychotic or nonpsychotic) were divided into ALGO and TAU groups. The ALGO group included patients who required an antidepressant medication change or were starting antidepressant therapy. The TAU group initially met the same criteria, but because medication changes were made less frequently in the TAU group, patients were also recruited if their Brief Psychiatric Rating Scale total score was higher than the median for that clinic's routine quarterly evaluation of each patient. MAIN OUTCOME MEASURES: Primary outcomes included (1) symptoms measured by the 30-item Inventory of Depressive Symptomatology-Clinician-Rated scale (IDS-C(30)) and (2) function measured by the Mental Health Summary score of the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12) obtained every 3 months. A secondary outcome was the 30-item Inventory of Depressive Symptomatology-Self-Report scale (IDS-SR(30)). RESULTS: All patients improved during the study (P<.001), but ALGO patients had significantly greater symptom reduction on both the IDS-C(30) and IDS-SR(30) compared with TAU. ALGO was also associated with significantly greater improvement in the SF-12 mental health score (P =.046) than TAU. CONCLUSION: The ALGO intervention package during 1 year was superior to TAU for patients with MDD based on clinician-rated and self-reported symptoms and overall mental functioning.

Combining evidence-based practice with stakeholder consensus to enhance psychosocial rehabilitation services in the Texas benefit design initiative.

This article describes the use of evidence-based practice along with a multi-stakeholder consensus process to design the psychosocial rehabilitation components in a benefit package of publicly funded mental health services in Texas. The Texas Benefit Design initiative demonstrates how the combination of science and consensus can be used as a powerful tool for change. It applies the findings of rigorous research regarding psychosocial rehabilitation service delivery approaches that achieve positive outcomes in real world, community settings. At the same time, it makes use of the unique knowledge and experience that mental health service consumers, providers and other advocates can bring to service system design and planning.

A feasibility study of the childhood depression medication algorithm: the Texas Children's Medication Algorithm Project (CMAP).

OBJECTIVE: To evaluate the feasibility and impact on clinical response and function associated with the use of an algorithm-driven disease management program (ALGO) for children and adolescents treated for depression with or without attention-deficit/hyperactivity disorder (ADHD) in community mental health centers. METHOD: Interventions included (1). medication algorithms, (2). clinical and technical support for the physician, (3). uniform chart documentation of outcomes, and (4). a patient/family psychoeducation program. Children eligible for entry into the study were referred to the child psychiatrist for initiation or change in medicine. Outcomes of treatment with the ALGO for up to 4 months are presented. Measures of change included clinical symptoms, functioning, and global improvement (Clinical Global Impression Scale). A historical chart cohort from the same clinics was used as a quasi-control. RESULTS: Thirty-nine individuals (depression = 24; comorbid depression with ADHD = 15) were enrolled for treatment with ALGO. One hundred fourteen children were in the control cohort (74 depressed, 40 comorbid). For the ALGO groups, Children's Depression Rating Scale-Revised depression severity scores decreased from 48.2 to 32.5 and Child Adolescent Functioning Assessment Scale function scores improved from 70.3 to 40.9 (all p < or =.0005). Clinical Global Impression Scale severity scores decreased from 5.7 to 3.7 in ALGO compared to only 5.8 to 4.8 in the control (p <.003). CONCLUSIONS: There was clear improvement in clinical symptoms, functioning, and global response with ALGO treatment. The magnitude of the improvement was greater in children and adolescents treated with the ALGO program compared with a historical cohort. These data support the need for controlled studies in larger populations examining the effects of algorithm-driven disease management programs on the clinical outcomes of children with mental illness.

The Texas medication algorithm project: clinical results for schizophrenia.

In the Texas Medication Algorithm Project (TMAP), patients were given algorithm-guided treatment (ALGO) or treatment as usual (TAU). The ALGO intervention included a clinical coordinator to assist the physicians and administer a patient and family education program. The primary comparison in the schizophrenia module of TMAP was between patients seen in clinics in which ALGO was used (n = 165) and patients seen in clinics in which no algorithms were used (n = 144). A third group of patients, seen in clinics using an algorithm for bipolar or major depressive disorder but not for schizophrenia, was also studied (n = 156). The ALGO group had modestly greater improvement in symptoms (Brief Psychiatric Rating Scale) during the first quarter of treatment. The TAU group caught up by the end of 12 months. Cognitive functions were more improved in ALGO than in TAU at 3 months, and this difference was greater at 9 months (the final cognitive assessment). In secondary comparisons of ALGO with the second TAU group, the greater improvement in cognitive functioning was again noted, but the initial symptom difference was not significant.

Texas Medication Algorithm Project, phase 3 (TMAP-3): clinical results for patients with a history of mania.

BACKGROUND: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. METHOD: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. RESULTS: ALGO and TAU patients showed significant initial decreases in symptoms (p =.03 and p <.001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. CONCLUSION: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.

Texas Medication Algorithm Project, phase 3 (TMAP-3): rationale and study design.

BACKGROUND: Medication treatment algorithms may improve clinical outcomes, uniformity of treatment, quality of care, and efficiency. However, such benefits have never been evaluated for patients with severe, persistent mental illnesses. This study compared clinical and economic outcomes of an algorithm-driven disease management program (ALGO) with treatment-as-usual (TAU) for adults with DSM-IV schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) treated in public mental health outpatient clinics in Texas. DISCUSSION: The disorder-specific intervention ALGO included a consensually derived and feasibility-tested medication algorithm, a patient/family educational program, ongoing physician training and consultation, a uniform medical documentation system with routine assessment of symptoms and side effects at each clinic visit to guide ALGO implementation, and prompting by on-site clinical coordinators. A total of 19 clinics from 7 local authorities were matched by authority and urban status, such that 4 clinics each offered ALGO for only 1 disorder (SCZ, BD, or MDD). The remaining 7 TAU clinics offered no ALGO and thus served as controls (TAUnonALGO). To determine if ALGO for one disorder impacted care for another disorder within the same clinic ("culture effect"), additional TAU subjects were selected from 4 of the ALGO clinics offering ALGO for another disorder (TAUinALGO). Patient entry occurred over 13 months, beginning March 1998 and concluding with the final active patient visit in April 2000. Research outcomes assessed at baseline and periodically for at least 1 year included (1) symptoms, (2) functioning, (3) cognitive functioning (for SCZ), (4) medication side effects, (5) patient satisfaction, (6) physician satisfaction, (7) quality of life, (8) frequency of contacts with criminal justice and state welfare system, (9) mental health and medical service utilization and cost, and (10) alcohol and substance abuse and supplemental substance use information. Analyses were based on hierarchical linear models designed to test for initial changes and growth in differences between ALGO and TAU patients over time in this matched clinic design.

Catching up on health outcomes: the Texas Medication Algorithm Project.

OBJECTIVE: To develop a statistic measuring the impact of algorithm-driven disease management programs on outcomes for patients with chronic mental illness that allowed for treatment-as-usual controls to "catch up" to early gains of treated patients. DATA SOURCES/STUDY SETTING: Statistical power was estimated from simulated samples representing effect sizes that grew, remained constant, or declined following an initial improvement. Estimates were based on the Texas Medication Algorithm Project on adult patients (age > or = 18) with bipolar disorder (n = 267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas. STUDY DESIGN: Study patients were assessed at baseline and three-month follow-up for a minimum of one year. Program tracks were assigned by clinic. DATA COLLECTION/EXTRACTION METHODS: Hierarchical linear modeling was modified to account for declining-effects. Outcomes were based on 30-item Inventory for Depression Symptomatology-Clinician Version. PRINCIPAL FINDINGS: Declining-effect analyses had significantly greater power detecting program differences than traditional growth models in constant and declining-effects cases. Bipolar patients with severe depressive symptoms in an algorithm-driven, disease management program reported fewer symptoms after three months, with treatment-as-usual controls "catching up" within one year. CONCLUSIONS: In addition to psychometric properties, data collection design, and power, investigators should consider how outcomes unfold over time when selecting an appropriate statistic to evaluate service interventions. Declining-effect analyses may be applicable to a wide range of treatment and intervention trials.

A feasibility study of the children's medication algorithm project (CMAP) algorithm for the treatment of ADHD.

OBJECTIVE: To determine whether an algorithm for the treatment of attention-deficit/hyperactivity disorder (ADHD) can be implemented in a community mental health center. METHOD: Fifty child and adolescent patients at Texas community mental health centers who met criteria for ADHD were treated according to an algorithm-based disease management program for ADHD. Psychiatrists were trained in the use of the algorithm, and each subject underwent a baseline assessment consisting of a structured interview and standardized rating scales. Subjects were monitored for 4 months. At the end of treatment, the psychiatrists completed the Clinical Global Impression Scale (CGI) and the baseline rating scales were repeated. The primary variables of interest were psychiatrist and family adherence to the algorithm. To examine impact on treatment outcome, the CGI of the algorithm subjects was compared with CGIs based on chart reviews of 118 historical controls. RESULTS: Psychiatrists implemented the major aspects of the algorithm, but the detailed tactics of the algorithm (use of fixed titration of stimulants) were less well adhered to. CONCLUSIONS: An algorithm for the treatment of ADHD can be implemented in a community mental health center.

Itemized clinician ratings versus global ratings of symptom severity in patients with schizophrenia.

This study compares ratings obtained with an itemized clinician-rated symptom severity measure--the 24-item Brief Psychiatric Rating Scale (BPRS24)--with a Physician Global Rating Scale (PhGRS) and a Patient Global Rating Scale (PtGRS) in assessing treatment outcomes in patients with schizophrenia (SCZ). A total of 91 patients (31 inpatients and 60 outpatients) with SCZ were enrolled in a feasibility study of the use of medication algorithms in the treatment of SCZ. Clinicians completed the BPRS24 and the PhGRS; patients completed the PtGRS at each visit. The analyses reported here were conducted using the original BPRS18 and four items from the BPRS18 that rate the positive symptoms of psychosis (the Positive Symptom Rating Scale or PSRS), comparing anchored with global rating scales and with one another. The PtGRS had the lowest effect size (0.8) and was negatively correlated with the other ratings in inpatients. The PhGRS was significantly correlated (0.46) with the BPRS18, but the same person completed both ratings. The effect size of the PhGRS (0.6) was generally lower than with the BPRS18 (1.4) in differentiating responders from non-responders. On average, the PSRS had a slightly lower effect size than the longer itemized BPRS18, but the results support its use as a quantitative rating in circumstances where it is not feasible to routinely use a lengthier scale.

Report of the Texas Consensus Conference Panel on medication treatment of bipolar disorder 2000.

BACKGROUND: The process and outcome of a consensus conference to develop revised algorithms for treatment of bipolar disorder to be implemented in the public mental health system of Texas are described. These medication algorithms for bipolar disorder are an update of those developed for the Texas Medication Algorithm Project, a research study that tested the clinical and economic impact of treatment guidelines for major psychiatric illnesses treated in the Texas public mental health system (Texas Department of Mental Health and Mental Retardation [TDMHMR]). METHOD: Academic clinicians and researchers, practicing clinicians in the TDMHMR system, administrators, advocates, and consumers participated in a consensus conference in August 2000. Participants attended presentations reviewing new evidence in the pharmacologic treatment of bipolar disorder and discussed the needs of consumers in the TDMHMR system. Principles were enumerated, including balancing of evidence for efficacy, tolerability, and safety in medication choices. A set of 7 distinct algorithms was drafted. In the following months, a subcommittee condensed this product into 2 primary algorithms. RESULTS: The panel agreed to 2 primary algorithms: treatment of mania/hypomania, including 3 pathways for treatment of euphoric symptoms, mixed or dysphoric symptoms, and psychotic symptoms; and treatment of depressive symptoms. General principles to guide algorithm implementation were discussed and drafted. CONCLUSION: The revised algorithms are currently being disseminated and implemented within the Texas public mental health system. The goals of the Texas initiative include increasing the consistency of appropriate treatment of bipolar disorder, encouraging systematic and optimal use of available pharmacotherapies, and improving the outcomes of patients with bipolar disorder.