Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice.

Full Abstract

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the "next best" treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses.

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Author information

Author/s: Trivedi, Madhukar H (MH); Daly, Ella J (EJ);

Affiliation: Mood Disorders Program, Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX 75390, USA. madhukar.trivedu(-atsign-)utsouthwestern.edu

Grants: 5R01MH064062-2 (Agency:NIMH NIH HHS) ; 5R01MH067962-2 (Agency:NIMH NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Drug and alcohol dependence (Drug Alcohol Depend), published in Ireland. (Language: eng)

Reference: 2007-May; vol 88 Suppl 2 (issue ) : pp S61-71

Dates: Created 2007/04/09; Completed 2007/07/10; Revised 2007/11/15;

PMID: 17320312, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Decision Making, Computer-Assisted
Depressive Disorder, Major - drug therapy; therapy
Diagnostic and Statistical Manual of Mental Disorders
Drug Therapy - methods

Female
Humans
Male
Mental Health Services - organization & administration
Practice Guidelines as Topic

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