Biomedical subjects

T Michael Kashner

Publications and source records attributed to T Michael Kashner.

5 recordsLinked to original sources

PubMed2003

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.

Adult↗

PubMed2003

Impact of structured clinical interviews on physicians' practices in community mental health settings.

OBJECTIVE: Budgetary constraints often limit practicing psychiatrists from conducting more thorough diagnostic evaluations. This study examined physicians' diagnosing and prescribing practices in the context of feedback from nurse-administered, structured clinical interviews of psychiatric patients in publicly funded outpatient mental health settings. METHODS: A randomized controlled trial was conducted of newly enrolled adult psychiatric patients who made at least one return visit for care at two county-supported outpatient clinics. Within two weeks after their intake psychiatric evaluation, patients were randomly assigned to receive a nurse-administered Structured Clinical Interview for DSM-IV-Clinician Version (SCID) (N=158) or to a control condition (N=138). The attending psychiatrist was provided with the SCID results. Abstracts from clinical records were used to measure differences in physicians' rates of ordering additional diagnostic evaluations, changing diagnoses, and changing types and dosages of medications at three- and six-month follow-ups. RESULTS: Physicians treating patients who received SCIDs, compared with control patients, were more likely to order evaluative procedures, update and change diagnosis (consistent with SCID results), and change prescription medication type and were less likely to increase patients' medication dosages. CONCLUSION: Nurse-administered structured clinical interviews are feasible and effectively help psychiatrists in publicly supported mental health clinics reach more accurate diagnoses for newly enrolled patients.

Adolescent↗

PubMed2003

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.

Adolescent↗

PubMed2003

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.

Adolescent↗

PubMed2002

Impact of work therapy on health status among homeless, substance-dependent veterans: a randomized controlled trial.

BACKGROUND: Little is known about the health outcomes of clinician-supervised, performance-based, abstinence-contingent therapeutic work programs on homeless persons with addiction disorders. This study examined the effect of the Department of Veterans Affairs compensated work therapy program (CWT) on nonvocational outcomes. With mandatory urine screenings and adherence to addiction treatment schedules, CWT provided work opportunities (wages, hours, and responsibilities) with jobs created from Veterans Affairs contracts competitively obtained from private industry. METHODS: Homeless, substance-dependent veterans (N = 142) from 4 Department of Veterans Affairs medical centers were randomized, assessed at baseline, and reassessed at 3-month intervals for 1 year. Both CWT and control groups had access to comprehensive rehabilitation, addictions, psychiatric, and medical services. Data were analyzed to determine an immediate CWT effect after treatment and rates of change during 1 year. RESULTS: Compared with control subjects, patients in the CWT program were more likely to (1) initiate outpatient addictions treatment, (2) experience fewer drug and alcohol problems, (3) report fewer physical symptoms related to substance use, (4) avoid further loss of physical functioning, and (5) have fewer episodes of homelessness and incarceration. No effect on psychiatric outcomes was found. CONCLUSION: Work therapy can enhance nonvocational outcomes of addiction treatment for homeless persons, although long-term gains remain unknown.

Adult↗