Computerizing Medication Algorithms and Decision Support Systems for Major

Trivedi, Madhukar H.; Kern, Janet K.; Baker, Shannon M.; Altshüler, Kenneth Z.

doi:10.1097/00131746-200009000-00004

Cited by 16 publications

(11 citation statements)

References 46 publications

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“…Means to achieve remission have been the focus of large-scale investigations. Algorithm-based approaches, such as studied in the TMAP (“Texas Medication Algorithm Project”, Rush et al, 1999; Trivedi et al, 2000) and STAR*D trials, provide an evidence base for selecting subsequent treatments, but these decisions are made for individual patients only after a remission has failed to occur despite an adequate trial of a particular intervention, a process which takes weeks to months for each sequential trial. The duration of algorithm-based treatment could be shortened if practitioners could identify, early in a treatment phase, those individuals who are unlikely to remit, and institute different, augmented, and/or more complex treatments sooner for those patients who need them.…”

Section: Introductionmentioning

confidence: 99%

Midline and right frontal brain function as a physiologic biomarker of remission in major depression

Cook¹,

Hunter²,

Abrams³

et al. 2009

Psychiatry Research: Neuroimaging

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Prior investigations have reported that changes in the prefrontal electroencephalogram (EEG) precede symptom improvement from antidepressant medications, and could serve as a biomarker of treatment outcome in major depressive disorder (MDD). A new physiologically-defined region of interest, overlying the midline and right frontal (MRF) cortical area, was examined here for a relationship between early decreases in theta-band cordance and remission. Subjects were 72 adults with unipolar MDD who had completed placebo-controlled antidepressant treatment trials, with 37 randomized to medication and 35 to placebo. We assessed changes in cordance and absolute and relative power in the MRF region at 48 hours, 1 week, and 2 weeks after start of drug, as potential predictors of remission (final Ham-D 17 ≤5). Eleven of 37 medication subjects (30%) remitted vs 6 of 35 placebo subjects (17%). Change in MRF cordance one and two weeks after start of drug was significantly associated with remission in medication subjects (P=0.02 at 1 week), with Receiver Operating Characteristic (ROC) analysis yielding 0.76 area under the curve. Decreases in MRF cordance at 1 week predicted remission with medication with 69% overall accuracy (90% sensitivity; 60% specificity). MRF cordance changes were not associated with remission with placebo. Absolute and relative power did not differentiate groups. These results suggest that remission may be predictable from physiologic measurements after 1 week of treatment, and that this region merits further investigation in the neurobiology of treatment response.

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Section: Introductionmentioning

confidence: 99%

Midline and right frontal brain function as a physiologic biomarker of remission in major depression

Cook¹,

Hunter²,

Abrams³

et al. 2009

Psychiatry Research: Neuroimaging

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“…These areas include electronic documentation and information retrieval [18], physician order entry and adverse drug events [19], [20] as well as preventive care and follow-up [21], [ 22].…”

Section: Related Workmentioning

confidence: 99%

Web Based Intelligent Decision Support System for Type 2 Diabetes Patients

Sahar¹

2013

IJET

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Abstract-Intensive treatment of type 2 diabetes can respond as a considerable decrease in the complications related to diabetes. In spite of specialist care there are still many patients that have poor control over their diabetes and the chronic nature of type 2 diabetes makes patients dependent on their care providers. In Type 2 diabetes patients require empowerment and self-management for initial and long term treatment. The required transfer of care from health care providers to patients needs an intervention of computerized decision support systems. In widespread health care practice the interventions that have demonstrated effective results in the management of chronic and/or progressive diseases have often been difficult to develop because of disease nature. The paper describes a web based intelligent decision support system (IDSS) for type 2 diabetes patients, where more emphasis is put on patient empowerment and self-management in treatment. The IDSS is based on well documented decision support system, designed mainly for type 2 diabetes patients. The scope of IDSS is broad from being mainly used by type 2 diabetes patients as a tool for their empowerment, self-management, communication, and education.

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“…While efforts to promote current advances in pharmacology have resulted in a surge of consensus guidelines, finding an effective dissemination method continues to be the greatest challenge (Davis et al, 1995;Gorton et al, 1995;Trivedi et al, 2000;Trivedi et al, 2002;Trivedi et al, 2004a). Despite wide promotion, clinical practice guidelines have had limited effect in changing physician behavior (Hayward 1997;Lomas et al, 1989).…”

Section: Barriers To Dissemination and Implementation Of Guidelinesmentioning

confidence: 99%

“…Previous attempts to implement guidelines for MDD using a paper-and-pencil format in clinical settings (Trivedi et al, 2004b;Trivedi et al, 2006c;Trivedi et al, 2007) have revealed problems with fidelity in terms of dosing, adequate duration of treatment, and visit frequency. One possible solution to this problem is to couple guidelines with a measurement-based, computerized approach (Hunt et al, 1998;Trivedi et al, 2000;Trivedi et al, 2001;Trivedi et al, 2002;Trivedi et al, 2004a). There is already evidence of improved physician fidelity to guidelines using this approach, suggesting that providing immediate decision support at the point of care via a comprehensive electronic health record could improve physician adherence and clinical outcomes Trivedi et al, 2002;Trivedi et al, 2004a).…”

Section: Introductionmentioning

confidence: 99%

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

Trivedi

Daly

2007

Drug and Alcohol Dependence

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106

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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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Computerizing Medication Algorithms and Decision Support Systems for Major

Cited by 16 publications

References 46 publications

Midline and right frontal brain function as a physiologic biomarker of remission in major depression

Midline and right frontal brain function as a physiologic biomarker of remission in major depression

Web Based Intelligent Decision Support System for Type 2 Diabetes Patients

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

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