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Creating an Algorithm

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Creating an Algorithm

Tracking the spread of diseases can be an intricate, complex, and labor-intensive process. As a result, automated surveillance systems utilizing algorithms are employed to interpret data. In this segment of your Scholar-Practitioner Project, you develop a simple algorithm to interpret data related to the disease or condition you selected last week. To aid your development, review your Learning Resources and research the construction of algorithms.

To complete this portion of your Scholar-Practitioner Project, write a 1- to 2-page paper that addresses the following:

• Identify the indicators you chose to include and explain why they are appropriate.
• Describe the logical process of the algorithm (you may wish to illustrate using a diagram).
• Justify any other salient features of the algorithm.
• Evaluate the strengths and limitations of the algorithm.

Creating an Algorithm

Detecting depression from structural MRI scans is significantly new in the mental health diagnosis. This detection requires processes including image acquisition as well as pre-processing, feature extraction, selection and classification. Identifying a suitable feature selection algorithm facilitates the enhancement of the detection accuracy. Medication algorithms for major depression disorder treatment are designed to optimize treatment implementation and the correctness of treatment strategies. Therefore, they are significant tools for treatment and avoidance of refractory depression. Treatment algorithms are express treatment protocols which aim at providing specific therapeutic pathways and tools for decision-making throughout the treatment process (Trivedi & Kleiber, 2001).

Indicators to include in the algorithm and their appropriateness

The severity indicators for a major depressive episode include recurrent thoughts of death and suicidal ideation, diminished ability to think or concentrate, feelings of worthlessness or excessive guilt, fatigue or loss of energy, psychomotor agitation or retardation, insomnia or hypertension, substantial weight loss or weight gain, increased or decreased appetite, diminished interest or pleasure, and depressed mood. These indicators are appropriate because they provide a basis for determining the severity of depression. Severity indicators are determined in terms of the number and type of symptoms exhibited by the patient. Thus, symptoms are either mide, moderate or severe depending on their degree of impairment of occupational function or the usual social functions or relationships with other people. The TMAP strongly recommends that measurement-based care should be adopted in the treatment of major depression disorder. In addition to symptom severity, it is also important to measure the side effects and global functioning at each visit in order to ensure that treatment decisions are guided by objective data (Suehs et al, 2008).
The logical process of the algorithm

The preliminary stage involves the assessment of the patient and discussion of treatment options. The first step of the algorithm is antidepressant monotherapy. Medication recommendations for antidepressant monotherapy include selective serotonin reuptake inhibitors, bupropion and mirtazipine. The selection of treatment is based on individual patient characteristics. The second step involves augmentation for patients with partial response to antidepressant monotherapy. This increases the chances for the achievement of remission without the loss of clinical improvements. Recommended augmentation strategies include addition of mirtazipine, buropion,or SSRI (Suehs et al, 2008).

The third step is for patients who do not respond to the first and second step. It involves the same medications offered in the previous stages, although a different class of antidepressants should be tried. The fourth step involves combined treatment and it is for patients who do not respond to medications prescribed in the second stage. The fifth stage is an alternative to the fourth step, with different combinations of medications. The sixth step is for patients who do not respond to the previous step. This treatment recommends the use of ECT, or vagus nerve stimulation in combination with antidepressant treatment. In the seventh stage, there is barely any evidence to guide treatment. The medications for this stage are based on expert opinion and the consensus of the TMAP panel (Suehs et al, 2008).

The algorithm requires the individualization of frequency of physician offices visits for each patient. Generally, an adequate medication trial for antidepressants need to last 8-12 weeks. Where a patient fails to respond to medication, a switch in antidepressant medication may be necessary.
Evaluation of the strengths and limitations of the algorithm

The strength of this treatment algorithm is that it is evidence-based to the extent that evidence is available for purposes of guiding treatment decisions. In cases of missing clinical data, treatment recommendations are driven by expert consensus opinion. However, this algorithm does not serve as a substitute for clinical judgment and it only provides a systematic approach to pharmacological treatment of major depression disorder (Aronson & Ayres, 2009).

References

Aronson, S. C. & Ayres, V. E. (2009). Depression: A Treatment Algorithm for the Family Physician. Clinical Review Article. Retrieved from: http://www.turner-white.com/pdf/hp_jul00_depress.pdf

Suehs, B., Argo, T. R., Bendele, S. D. et al. (2008). Texas Medication Algorithm Project Procedural Manual: Major Depressive Disorder Algorithms. Texas Department of State Health Services. Retrieved from: http://www.jpshealthnet.org/sites/default/files/tmap_depression_2010.pdf

Trivedi, M. H., & Kleiber, B. A. (2001). Algorithm for the treatment of chronic depression. Journal of Clinical Psychiatry.

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