Adli Mazda, Bauer Michael, Rush A John
Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany.
Biol Psychiatry. 2006 Jun 1;59(11):1029-38. doi: 10.1016/j.biopsych.2006.05.010.
Treatment algorithms and collaborative-care systems are systematic treatment approaches that are designed to improve outcomes by enhancing the quality of care. During the last decade, algorithm research has evolved as a new branch of clinical research that evaluates the clinical and economic impact of algorithm-guided treatment in primary and psychiatric care of patients with depressive disorders.
This article discusses the rationale of algorithm development, their risks and limitations, and important elements in their implementation in clinical practice. It further reviews the available studies that have evaluated algorithm-guided treatment for depression.
Recent studies show that compared with treatment as usual, the use of algorithms and collaborative-care approaches in the care of depressed patients enhances treatment outcomes by modifying practice procedures and treatment processes.
Treatment algorithms and collaborative-care systems clearly increase the efficacy of applied treatments in the care of depressed patients. However, to what extent the enhanced outcomes are a result of diligent measurement-based care or of the specific treatment steps that are used remains to be resolved. Valid clinical or pharmacogenetic predictors of response are needed to further tailor specific algorithms to individual patients.
治疗算法和协作护理系统是系统性治疗方法，旨在通过提高护理质量来改善治疗效果。在过去十年中，算法研究已发展成为临床研究的一个新分支，评估算法指导治疗在抑郁症患者初级和精神护理中的临床和经济影响。
本文讨论了算法开发的基本原理、其风险和局限性以及在临床实践中实施算法的重要因素。它还回顾了评估算法指导抑郁症治疗的现有研究。
最近的研究表明，与常规治疗相比，在抑郁症患者护理中使用算法和协作护理方法可通过修改实践程序和治疗过程来提高治疗效果。
治疗算法和协作护理系统显然提高了抑郁症患者护理中应用治疗的疗效。然而，改善的结果在多大程度上是基于勤奋的基于测量的护理或所使用的特定治疗步骤的结果仍有待解决。需要有效的临床或药物遗传学反应预测指标，以便进一步为个体患者量身定制特定算法。
